randomized ensemble svm based deep learning with

Randomized Ensemble SVM based Deep learning with Verifiabledynamic access control using user revocation in IoT architecture

RAVULA ARUN KUMAR* and KAMBALAPALLY VINUTHNA

Department of CSE, Koneru Lakshmaiah Education Foundation, Green Fields, Vaddeswaram,

Andhra Pradesh 522502, India

e-mail: [email protected]; [email protected]

MS received 8 June 2020; revised 14 June 2021; accepted 30 July 2021

Abstract. As applications based on the Internet of Things (IoT) are growing rapidly, the deployment of sensor

nodes have also increased. So, a large amount of sensed data has to be processed before storing. Since IoT-based

applications are context-aware computing, an attacker can easily inject false data. Even though existing

mechanisms were able to render the security on data, it is subjected to stealing, due to using symmetric-based

encryption. In order to provide a comprehensive security system, the entrance control strategy called Verifiable

Dynamic Access Control using User Revocation is utilized. In that, the combined CPASBE (Cipher Text

Attribute Set Based Encryption) –VOMAACS (Verifiable Outsourced Multi-Authority Access Control Scheme)

is utilized. CPASBE- a method for protecting sensitive data from third parties is to have data in the data owner

itself. A data owner is solely responsible for securing his data and therefore replication is avoided. The CPASBE

strategy ensures that the scrambled information is kept private regardless of considering reliability of supplier,

which secured information against agreement assaults.

Keywords. CPASBE–Cipher Text Attribute Control Scheme; VOMAACS – Verifiable Outsources Multi-

Authority Access Control Scheme.

1. Introduction

When all is said and done, numerous database groups and

extra assets are required to store enormous information.

Capacity and recovery, on the other hand, are not only

issues, according to all analyses. Getting important exam-

ples from huge information, for example, quiet demon-

strative data is additionally a fundamental issue [1].

Nowadays, this emerging application is being produced for

different situations like sensors. Sensors are regularly uti-

lized in basic applications without a doubt or not so distant

future. Various body sensors gadgets have been created for

ceaseless checking of social insurance, individual wellness,

and physical action mindfulness [2, 3]. Recently, numerous

analysts have been attempting to build up various wearable

clinical gadgets in remote wellbeing checking frameworks

for ceaseless observation of individual wellbeing conditions

[4]. For precedent, wearable device gadgets are utilized for

recommending physiological activities and sustenance

propensities by a two-multiday time of constant physio-

logical checking of patients. In this period wearable sensors

would ceaselessly watch and store the patients’ wellbeing

information into an information stock [5]. This enables

specialists towards the analysis of the patient’s wellbeing to

state and improve grades not just utilizing the laboratory

tests but also patients’ well-being information gathered

from the wearable body sensors. In this way, the sensor

information is frequently utilized for making the suit-

able move for patient’s wellbeing and treatment suggestion,

way of life decisions, and early analysis that are basic in

improving the nature of patient’s wellbeing [6]. Out-dated

well-being information gathered from the wearable body

sensors. In this manner, the sensor information is a standard

data for stockpiling procedure, and the stages are not for

assisting a previously stated developing sensing device

inside a utilised location where the volume, speed, and

variety of data is growing. This issue requires the

advancement of an effective stockpiling framework for

putting away such issue and handling large information [7].

Under this a verified engineering and execution of versatile

IoT design for handling and ensuring ongoing sensor

information is needed for adapting enormous information

technologies [8].

Fog processing is a dispersed registering framework in

which particular submission administrations are measured

at the system control in a brilliant gadget though particular

and others are measured in a distant information center

[9, 10]. The information created through sensors inserted in

different things/objects produces huge measures of

unstructured (enormous) information on the constant*For correspondence

Sådhanå (2021) 46:229 � Indian Academy of Sciences

https://doi.org/10.1007/s12046-021-01705-1Sadhana(0123456789().,-volV)FT3](0123456789().,-volV)

premise that hold the guarantee for knowledge and bits of

knowledge for significantly improved choice procedures.

Even though incorporation of distributed computing and

Fog registering have conveyed various preferences towards

the IT associations, yet the greatest critical drawback con-

cerning the cloud is security and insurance of the redis-

tributed data, so that cloud data wellbeing is an important

worry for information proprietors while utilizing cloud

administrations [11]. While utilizing cloud benefits the

clients got the opportunity to hand over their information to

cloud specialist co-ops. The cloud authority center is a

beneficial element that can not be trusted. Data is a fun-

damental information for data owners, since there is some

relationship presented before any endeavour were made,

therefore data security is the critical concern [12]. So, data

owners will at first guarantee that their data is kept char-

acterized by the unapproved person. Not just security, but

the data privacy, flexibility, and fine-grained access control

in circulating registration conditions are the critical issues.

Access switch is additionally an urgent issue and in

various models. Toward accomplishing a reasonable

grained access regulator the number of plans [13–16] have

been exhibited. However, these plans are just appropriate to

the frameworks in which information proprietors and the

specialist organizations are on the equivalent confided

space. Since information proprietors and specialist co-ops

are ordinarily on the various believed area this plan can not

be connected, another plan called quality based encryption

[17–19] is introduced. Superiority Created Encryption

(ABE) stays an auspicious method to guarantee the start to

finish security of huge information in the cloud. In any case,

the strategy refreshing has constantly remained a difficult

issue when ABE is utilized to build access control plans.

An inconsequential execution is to give information pro-

prietors a chance to recover the information and re-encode

it under the new access approach after that send it back to

the cloud. This method brings about a high correspondence

overhead and substantial calculation load on information

proprietors [20].

Moreover, IoT devices are gotten from anywhere through

the dependent system similar to the network. So, IoT sys-

tems are unprotected against a wide scope of malicious

attacks. If security issues are not kept, then the secret

records might be spilled once. Interruption Detection Sys-

tem (IDS) [21] is utilized to screen the vindictive traffic

specifically in hub and systems. It can go about as a

moment line of the barrier which can safeguard the system

from interlopers. Interruption is an undesirable or noxious

movement that is destructive to sensor hubs. IDS can

remain a product or equipment device. IDS can assess and

explore machinery and customer activities, recognize

marks of understood assaults and distinguish vindictive

system activity [22–25]. The objective of IDS is to watch

the systems and hubs, recognize different interruptions in

the system, and caution the clients after interruptions had

been distinguished. Recently, IoT gadgets are constantly

creating generous information which is regularly called

enormous information (structured and unstructured infor-

mation). When all said and done, it is hard to process and

break down huge information for finding important data. In

addition, security is the key prerequisite in social insurance

in the enormous information framework. To defeat this

issue, utilizing the construction for the usage of the IoT

(Internet of Things) to store and process versatile sensor

data is required. This design comprises two fundamental

sub-models, in particular, Meta Fog-Redirection (Mf-R)

and Grouping and Choosing (GC) architecture. MF-R

architecture (Meta-Fog Redirection) utilizes enormous

information advancements, for example, an Apache Pig and

Apache H Base for accumulation. The capacity of the

sensor information generated from the diverse sensor gad-

gets. With distributed computing, the GC Design is used to

protect reconciliation on cloud systems.

2. Literature survey

Some of the existing studies include are as follows.

Bhavani Thuraisingham et al [26] presented the approachnamely, Proactive Dynamic Secure Data Scheme (P2DS),

this intends to ensure the unforeseen gatherings which can

not achieve the protection information. There are dual

principle calculations secondary to the projected plan; they

remain Attribute-based Semantic Access Control (A-SAC)

Procedure and Proactive Determinative Access (PDA)

Procedure. The primary commitments of this paper require

three viewpoints. Fistly, displayed semantic methodology

for compelling information to get. Secondly, a client-driven

methodology that proactively keeps clients’ information

from sudden activities on the cloud side. Thirdly, this plan

has a more elevated amount of secure manageability, then it

can manage dynamic dangers, including the rising and

future perils.

Rahman et al [27] exploited the vital location of such

portals at the edge of the system to deal with a few larger

quantity directions, for example, nearby capacity, continu-

ous neighborhood information handling, installed infor-

mation mining, and so on and so forth. Therefore,

displaying a Smart e-Health Access. Before misuse the idea

of Fog Calculating in Healthcare, IoT frameworks by

shaping a Geo-dispersed middle person layer of insight

between sensor hubs and Cloud. By assuming liability for

taking maintenance through some certain weights of the

sensor organize and a remote human services focus, our

Fog-helped framework design can adjust to numerous dif-

ficulties in omnipresent social insurance structures, with

respect to portability, vitality proficiency, versatility, and

unwavering quality issues. The operative procedure of

Smart e-Health Gateways can empower huge sending of

omnipresent wellbeing checking frameworks, particularly

in clinical situations. Additionally, presents a model of a

Smart e-Health Gateway called UT-GATE where a portion

229 of 14 Sådhanå (2021) 46:229

of the examined more elevated amount of highlights have

been executed. Furthermore, execute an IoT-based Early

Warning Score (EWS) wellbeing observing for all intents

and purposes determine the proficiency and pertinence of

our framework on tending to a restorative contextual

investigation. Our proof of idea configuration shows IoT-

based wellbeing observing framework with upgraded by

and large framework knowledge, vitality effectiveness,

portability, execution, interoperability, security, and

unwavering quality.

Thirumalai et al [28] discussed that Memory Efficient

Multi Key (MEMK) acreage conspire. For touchy infor-

mation, our plan will help in trading the data between cloud

to IoT and IoT to IoT gadgets. At the point when cryp-

tography has a place with the topsy-turvy type, at that point

it has open and reserved keys. On behalf of memory

effectiveness, our plan reclaims the RSA conspire with a

Diophantine type of the nonlinear condition. Additionally,

our plan execution similarly performs well, and this is

chiefly because of the utilization of RSA open key alone.

Due to this, MEMK does not require multiplicative reverse

capacity or Extended Euclid’s algorithm.

Yinghui Zhang et al [29] introduced a trademark

appended the information sharing course of action rea-

sonable for asset constrained conservative clients in cir-

cled figuring. This course of action disposes of a

calculation task by with structure open parameters extra

than flimsy lacking encryption estimation isolates. An

open figure substance test stage is performed before the

unscrambling stage, which gets out a colossal portion of

calculation overhead because of preposterous fig-

ure works. For information security, a Chameleon hash

point of confinement is utilized to make a short fig-

ure content, which will be blinded by the disconnected

figure attempts to get the last online figure creations. This

course of action has appeared against adaptively picked

figure substance ambushes, which is completely observed

as standard security suspected. Expansive examination

shows that the proposed course of action is secure and

effective. Watchwords: Cloud processing, Access control,

Attribute-based encryption, Online/disconnected encryp-

tion, Chosen figure content security. Anyway, this system

would not be considered direct quality renouncement in

information sharing for asset restricted customers in dis-

persed processing.

Xiong et al [30] explained that the sharing of personal

data with multiple users from different domains has been

benefitted considerably from the rapid advances of cloud

computing, and it is highly desirable to ensure the sharing

file should not be exposed to the unauthorized users or

cloud providers. Unfortunately, issues such as achieving the

flexible access control of the sharing file, preserving the

privacy of the receivers, forming the receiver groups

dynamically, and high efficiency in encryption/decryption

remain challenging. To deal with these challenges, a novel

anonymous attribute-based broadcast encryption (A2 B2 E)

features the property of hidden access policy and enables

the data owner to share his/her data with multiple partici-

pants who are inside a predefined receiver set and fulfil the

access policy was provided. Firstly, it suggested a concrete

A2 B2 E scheme together with the rigorous and formal

security proof without the support of the random oracle

model. Then, it designed an efficient and secure data

sharing system by incorporating the A2 B2 E scheme, ver-

ifiable outsourcing decryption technique for attribute-based

encryption, and the idea of online/offline attribute-based

encryption. Extensive security analysis and performance

evaluation demonstrate that the data-sharing system is

secure and practical.

This proposed technique is especially huge to informa-

tion proprietors for protecting information from aggressors.

By then, widen this strategy for checking steadfastness in

cloud structure at the time of record invigorating. Besides,

this proposes an Attribute-Based Encryption (ABE) which

adds fitting analyses to uncover at any rate various

infringements as would be sensible. Then, it is finished by

utilizing the client task table. Each utilization keeps up a

UOT for tape nearby assignments. Every affirmation in the

UOT is portrayed by three sections: task, judicious vector,

and physical vector. Notwithstanding the way that users can

see distinctive sub servers in CSP, it is considered to give

an ordinary update framework to confirm areas essentially

and give the data to customers resulting in reviving in a

manner of speaking.

One of the key inconveniences going up against the

insistence of the web of Things is the security challenge,

particularly in the district of protection and characteriza-

tion. Constancy, economy, efficiency, and feasibility of the

security and insurance of the catch of Things are principal

for ensuring grouping, reliability, confirmation, and access

control. For example, customers should be anxious to share

certain data about their penchants in the open spaces of the

web, and this yearning is made for the customer just with

the fundamental Shields to divert the divulgence of infor-

mation to different individuals. In this way, the framework

must ensure the protection and secrecy of the client. Our

main aim is to screen the client action to recognize the

legitimate access, and also to keep away from any unap-

proved access of data, to reduce the computational cost and

burden of the IoT devices.

In Secure fog communication, an existing Cipher text

technique is used for encryption and decryption in key

exchange protocol to establish secure communication

among a gathering of mist hubs and the cloud. Since

encryption and decryption operation requires a large num-

ber of module experimentation and pairings. So, the com-

putation cost is high. The traditional ciphertext policy

attribute creates new private keys for the user’s original set

of attributes. The new keys are misguiding the users hence,

difficult to trace the malicious user. While using the face

distinguishing proof and goals technique in the fog com-

puting framework, it has to meet the challenges of

Sådhanå (2021) 46:229 of 14 229

confidentiality, integrity, and availability in the process of

face identification. So, high privacy-preserving security

was required. For that, a flexible and scalable, well-grained

access device must be maintained. For access control,

policy-based schemes have been utilized. Generally,

Attribute-Based Encryption (ABE) has ascended as a

promising procedure to ensure data security. It empowers

data owners to portray and get the chance to courses of

action scrambles the information under the methodology,

with the genuine target that single clients whose properties

fulfilling these section procedures can interpret the infor-

mation. Whenever more and more affiliation is pursued, the

path of action that energizes the cloud becomes a crucial

problem, because if the information holder redistributes the

information to the cloud, a duplicate in close systems would

be held. Precisely when the information proprietor needs to

change the section strategy, it needs to move the informa-

tion back to the zone site from the cloud, re-encode the

information under the new access approach, and a for brief

span later move it back to the cloud server. Along these

lines, it accomplishes a high correspondence overhead and

overwhelming check burden on information proprietors.

This rouses us to build up another method to redistribute the

undertaking of methodology invigorating to the cloud

server.

3. Superintend framework based on regressionapproach using encryption

In this research, to rectify the issues given above, proposed

a Randomized Ensemble SVM-based Deep learning with

Verifiable dynamic access control using user revocation in

IoT architecture which aims at improving data security.

Initially, client validation is a basic procedure because of

the consistently developing security and assurance con-

cerns. In this research, client’s face is given just as the

client’s mark for the client verification. At first, the mark of

the client is scrambled with the SHA calculation to make

the advanced mark which gives the underlying confirma-

tion. To give greater security to the validation, the strategy

DNN (Deep Neural Network) learning is used. Utilizing

DNN, the component similitude can be figured out how to

locate the unapproved client. The ability to create new

features from the restricted course for highlights in the

prerequisite dataset is valuable learning over a range of AIs

because substantial learning can render features without

human intercession. Even so, computational planning is

unbelievably expensive and it also has a low example, it

looks at the rough highlights for the deep realization that

the results are not anything but hard to comprehend. The

randomized ensemble SVM is consolidated into DNN to

enhance understanding. Randomized Ensemble SVM is the

coordination of SVM with Random Jungles in which the

SVM classifier having the restriction in speed, just as size

both in preparing and testing. Furthermore, it has high

algorithmic multifaceted design and wide memory neces-

sities so as to beat that the irregular backwoods is fused

with the SVM more useful.Irregular Forests is likewise

called Ensemble Learning in light of the fact that, rather

than preparing a solitary tree, a variety of trees is prepared.

Each tree is set up on a subset of the scheme, chosen by the

substitute with an autonomous subset of information vari-

ables. Arbitrary woodlands have great exactness if the fast

and adaptable to tremendous enlightening accumulation

and it requires a limited quantity of reminiscence. Because

of that arbitrary nature, the DNN performs better profound

figuring out how to make the system increasingly validated

for safeguarding security.

To give greater security and protection, the entrance

gadget system called Verifiable Dynamic Access Control

utilizing User Revocation is used. In that, the joined

CPASBE (Cipher Text Attribute Set Based Encryption) –

VOMAACS (Verifiable Outsourced Multi-Authority

Access Control Scheme) is used. CPASBE- a method for

protecting sensitive data from third parties is to have data

in the data owner itself. A data owner is solely respon-

sible for securing his data also and the replication is

avoided. The CPASBE procedure ensures that scrambled

information is kept secret regardless of whether the

supplier is not reliable. It verifies the information against

conspiracy assaults. Moreover, CP-ASBE supports mul-

tiple value assignments for an attribute with a single key

which organizes user attributes in key thus allowing users

to dynamically inflict constraint to combine attributes to

satisfy policies. In addition to that, the VOMAACS is

used which will be secure against agreement assaults.

Here, the vast majority of the encryption and decoding

calculation is mineral appropriated to fog contraptions,

which immensely decreases the count on the customer

side, furthermore, it gives a check strategy to the redis-

tributed encryption and unscrambling. On the off chance

that a mist gadget returns inaccurate outcomes, clients

can see it promptly by running the relating check cal-

culation. Moreover, this VOMAACS scheme can have an

efficient client and property denial method. During the

process of property renouncement, a large portion of the

update and re-encryption tasks are re-appropriated to the

cloud server, which incredibly lessens the overhead of

the data owner. In the meantime, all cipher texts and

proxy keys need not be refreshed. It can only be modi-

fied for components involved in the default attribution. In

this way, the combined access policy control CPASBE

and VOMAACS can greatly improve the efficiency of

the security and privacy preservation with reduced cost.

For improving the network security, the optimized

intrusion detection system can be used. Coincidental PSO

intrusion detection is integrated into the PSO (Particle

Swarm optimization is used. PSO is nothing but difficult to

fall into the neighbourhood ideal in high dimensional space

and has a low assembly rate in the iterative procedure.

MBO (Marriage in Honey bee optimization) in which MBO

229 of 14 Sådhanå (2021) 46:229

has a random assignments behavior, thus providing fast

convergence. The search of the PSO algorithm at the

population phase is optimized by random behavior.

Accordingly, this optimized algorithm can optimally detect

intrusion attacks in the audit data. If there is any anomaly

present, the Authentication and access control approach is

again adopted to make the network secure. Throughout all

the techniques, this paper provides more privacy and

security with less cost.

3.1 Secure Hash Algorithm

An application or a computer that demands authentication

from access owners will definitely implement the login

process to secure the data. The login operation is per-

formed in conjunction with the application or device by

uploading data in the form of a username and password.

If the data are right, the client will be allowed to enter

the network. A single-way hash function known as a

message definition or a compression function is a cryp-

tographic feature which takes the variable length and

makes it a binary format of constant length. The one-way

hash function is built to overcome the series in a com-

plicated way, which ensures that the circuit is located at

a particular value. The main concept to use the secure

hash algorithm is to secure the data in the process. In a

cryptographic type, the data is then transmitted using a

secure hash algorithm, which increases the reliability of

privacy and protection at minimal cost.

The SHA 512 algorithm is an algorithm that uses the

one-way hash function created by Ron Rivest. This algo-

rithm is the development of previous algorithms SHA 0,

SHA 1, SHA 256, and SHA 384 algorithms. The crypto-

graphic algorithm of SHA 512 is receiving input in the form

of messages of any size and generates a message digest that

has a 512-bit length.

Its predecessor is SHA1, and MD5 which is a renewal of

MD4, the linkage, and development of the hash algorithm,

indicating that the collision vulnerability algorithm proved

to be observed. Currently, the National Institute of Stan-

dards and Technology (NIST) has made SHA 224, SHA

256, SHA 384, and SHA 512, the new standard hash

function. In Table 2, the resume parameters show some

hash functions.

SHA 512 hash function performs the same hash opera-

tion as SHA 2 operation in general [10]. SHA 512 hash

function is a function that generates message diggest

512-bit size and 1024 bit block length. The cryptographic

algorithm works SHA 512 is to accept input in the form of a

message with any length or size and will generate a mes-

sage digest that has a fixed length of 512 bits.

The workings of making message digest with SHA 512

algorithm are as follows:

3.1a The addition of bits: The primary procedure is to

include a message with various piece wedges to such an

extent that the message length (in bits) is harmonious with

890 mod1024. The thing to recall is that the 1024 number

shows up, as a result of the SHA 512 algorithm procedures

messages are in squares of1024 sizes. If a 24-bit-length

message is off, then the message will be distributed with the

group bits in both situations. The message will be added

with 896-(24 ? 1) = 871 bits.

So the length of the wedge bits is between 1 and 896. At

that point, one more thing to note is that the bit comprises

of a bit 1 pursued by the rest of the bit 0.

3.1b Adding Long Message Redemption Value: At that

point, the following procedure is the message included

again with 128 bits expressing the length of the first mes-

sage. On the off chance that the message length is more

prominent than 2128, at that point, the length is taken in

modulo 2128. In other words, if initially, the message

length is equivalent to Kbit, at that point 128 pieces include

K modulo of 2128 after the second procedure is done then

the message length now is 1024 bits.

3.1c Initialize Hash Value: In the SHA 512 algorithm, the

H hash value (0) consists of 8 words with 64 bits in the

hexadecimal notation as in the Table.

Buffer Initial value

A 6a09e667f3bcc908

B bb67ae8584caa73b

C 3c6ef372fe94f82b

D a54ff53a5f1d36f1

E 510e527fade682d1

F 9b05688c2b3e6c1f

G 1f83d9abfb41bd6b

H 5be0cd19137e2179

3.2 Ensemble machine learning algorithm basedon sequential Adaboost

In ensemble techniques, various learning calculations are

used to get preferred advancements over which any of

the component learning calculations alone can be

achieved. Boosting involves creating a group by design-

ing each new model for the implementation phase

misordered by the previous versions occurs from time to

time that boosting is superior to sacking. However, the

design information would often usually be over-fitted. By

a wide margin, the most well-known usage of boosting is

Adaboost. The process for preparing AdaBoost only

selects certain highlights that can enhance the model’s

high intensity.

Sådhanå (2021) 46:229 of 14 229

a) Training phase AdaBoost refers to a specific strategy

for preparing a boosted classifier. A lift classifier is a

classifier in the structure

CI pð Þ ¼XI

i¼1

ci pð Þ ð1Þ

where each ci is a frail student that takes an article p as info

and returns esteem showing the class of the item. For

precedent, in the two-class issue, the indication of the

feeble student yield distinguishes the anticipated item class

and the incomparable regard gives the trust in that

arrangement. Equally, the Ith classifier is sure if the

example is in a confident exercise and destructive else.

Each frail student creates yield speculation, h pmð Þ for

each example in the training set. At every emphasis I, afrail student is chosen and allotted a coefficient ei with the

end goal that the entirety preparing mistake Mi of the

subsequent i-stage help classifier is limited.

Mi ¼Xm

M Ci�1 pmð Þ þ eih pmð Þ½ � ð2Þ

Here Ci�1 pmð Þ is the supported classifier that has been

developed to the past phase of preparing, M Cð Þ is some

mistake work and ci pð Þ ¼ eih pmð Þ is the powerless student

that is being considered for expansion to the last classifier.

b) Weighting At every emphasis of the preparation pro-

cedure, a weight xm;i is doled out to each example in the

training set equivalent to the present blun-

der M Ci�1 pmð Þð Þ on that example. These loads can be uti-

lized to educate the preparation regarding the feeble

student, for example, choice trees can be developed that

support part sets of tests with high loads.

c) Derivation Suppose it has a data set

p1; q1ð Þ:::::; pB; qBð Þf g where everything pm has a related

class qm 2 �1; 1f g, furthermore, a lot of powerless classi-

fiers S1; :::; S‘f g every one of which yields a characteriza-

tion Sn pmð Þ 2 �1; 1f g for everything. After the a� 1ð Þ-themphasis our helped classifier is a straight mixture of the

delicate classifiers of the structure:

F a�1ð Þ pmð Þ ¼ e1s1 pmð Þ þ ::::þ ea�1sa�1 pmð Þ ð3Þ

At the a-th cycle, this need to stretch out to a superior

supported classifier by including another feeble classifier

sa, with additional load ea

Fa pmð Þ ¼ F a�1ð Þ pmð Þ þ easa pmð Þ ð4ÞSo it stays to figure out which frail classifier is the best

decision for sa, its weight should be ea. To characterize the

absolute mistake M of Fa as the total of its exponential

misfortune on every datum point, given as pursues:

M ¼XBm¼1

e�qmFa pmð Þ ð5Þ

Letting x 1ð Þm ¼ 1 and x að Þ

m ¼ e�qmFa pmð Þ for a[ 1 it have,

M ¼XBm¼1

x að Þm e�qmeasa pmð Þ ð6Þ

It can part this summation between those information

focuses that are accurately arranged by sa (so

qmsa pmð Þ ¼ 1) also, those that are misclassified (so

qmsa pmð Þ ¼ �1):

M ¼X

qm¼sa pmð Þ

x að Þm e�ea þ

Xqm 6¼sa pmð Þ

x að Þm e�ea ð7Þ

¼XBm¼1

x að Þm e�ea þ

Xqm 6¼sa pmð Þ

x að Þm eea � e�eað Þ ð8Þ

Since the main piece of the right-hand side of this con-

dition relies upon sa isP

qm 6¼sa pmð Þx að Þ

m , see that theM is the one

that limitsP


m , i.e. the powerless classifier with the

most minimal weighted mistake (with loads

x að Þm ¼ e�qmsa�1 pmð Þ).To determine the desired weight ea that minimizes M

with the sa that it was just determined, then to differentiate:

dM

dea¼

dP

qm¼sa pmð Þ xað Þm e�ea þP

qm 6¼sa pmð Þ xað Þm eea

� �dea

ð9Þ

Setting this to zero and concluding for ea yields:

ea ¼ 1

2ln

Pqm¼sa pmð Þ x

að ÞmP

qm 6¼sa pmð Þ xað Þm

0@

1A ð10Þ

To determine the weighted mistake rate of the frail

classifier be Ea ¼P


�PBm¼1

x að Þm ; so it follows

that:

ea ¼ 1

2ln

1� Ea

Ea

� �ð11Þ

Which is the negative log its capacity increased by 0.5.

Subsequently, to determine the AdaBoost calculation: At

every emphasis, pick the classifier sa, which limits the all-

out weighted mistakeP


m ; use this to calculate the

error rate Ea ¼P


�PBm¼1

x að Þm ; use this to cal-

culate the weight ea ¼ 12ln 1�Ea

Ea

� �, lastly utilize this to

improve the helped classifier F a�1ð Þ to Fa ¼ F a�1ð Þ þ easa.

229 of 14 Sådhanå (2021) 46:229

Thus AdaBoost classifier predicts the network that has

better QoS and allows the user to connect with that ideal

network.

3.3 MPO

The Particle Swarm Optimization calculation contained a

gathering of particles that move around the pursuit space

impacted by their own best past area and the best past

location of the entire swarm or a nearby neighbor. Every

cycle a molecule’s speed is refreshed utilizing:

viðt þ 1Þ ¼ viðtÞ þ ðc1 �randðÞ � ðpbesti � piðtÞÞÞþ ðc2 �randðÞ � ðpgbest � piðtÞÞÞ ð12Þ

where viðt þ 1Þ is the new speed for the ith element, c1and c2 are the weighting coefficients for the individual best

and worldwide best positions separately, piðtÞ is the

ith particle’s position at the time t , pbesti is the ith element’s

best-known location, and pgbest is the best location known

to the group. The randðÞ capacity produces a consistently

arbitrary variable 2 ½0; 1�½0; 1� .Variations on this update condition consider best posi-

tions inside particles nearby neighborhood at time t.

A particle’s location is updated and used by:

piðt þ 1Þ ¼ piðtÞ þ viðtÞThe algorithm (below) delivers a pseudocode listing of

the Particle Swarm Optimization procedure for reducing a

cost function.

4. Experimental results and discussion

The proposed system for the privacy cheating discourage-

ment and secure computation auditing protocol is imple-

mented in the working platform of MATLAB 2013a with

the following system configuration.

Processor: Intel Core i5

CPU Speed: 3 GHz

RAM: 8 GB

Operating system: Windows 8

4.1 Security Analysis

In this method, the file data are encrypted utilizing kfbecause of the lower efficiency of ABE and kf is encryptedutilizing the SHA algorithm. The confidentiality of data of

our method is based on the security of Verifiable Dynamic

Access Control using User Revocation.

Initialization: The adversary X selects the access struc-

ture T�, attribute sets cð1Þ; cð2Þ; � � � c ver��1ð Þ� �, and version

number ver�, and then submits to challenger Y.

Setup: Y initially creates version 1 for the public key of X

as follows. e b; bð Þb is expressed as e b; bð Þb¼ e A;Bð Þ�e b; bð Þy

0¼ e b; bð Þabþy

0; y

0 2R Zp; and b ¼ abþ y0. For each

n 2 V , Y selects randomly rn 2 Zp. For each set of attributes

cðkÞ; 1� k� ver� � 1, create a PK for that particular version

as follows: For n 2 cðkÞ, for 1� n� i, select randomly

rkðkÞn 2 Zp,T

ðkþ1Þn ¼ T

ðkÞn

� �rkðkÞn

. For n 62 cðkÞ; rkðkÞn ¼ 1;

Tðkþ1Þn ¼ T

ðkÞn .

Stage 1: The adversary X gives a top-secret key query on

set S ¼ njn 2 V; and n 2 T�f g on behalf of variety

k,1� k� ver�. Y initially selects randomly r0 2 Zp: D is

expressed as D ¼ by0�r

0 �g; and r ¼ agþ r0: Study that for

any n 2 V ; TðkÞn ¼ T

ð1Þn

� �rkð2Þn �rkð3Þn ��rkðkÞn ¼ T

ð1Þn

� �Qk

m¼2rk

ðmÞn

denote as RðkÞn ¼ Qk

m¼2 rkðmÞn : For n 2 S;Dn ¼ br:R

ðkÞn =tn ¼

bðagþr0 Þ�RðkÞ

n = g=rnð Þ ¼ Arn:RðkÞn � br0 :rn:RðkÞ

n ,where tn ¼ grn.Y directs

secret key SKS¼ k;D; 8n 2 S : Dnð Þ to X.

Challenge X gives two messages j0 and j1. Y flips random

coin g and set C ¼ bc; ~C ¼ jg:e b; bð Þb�c¼ jg � e b; bð Þagc�e b; bð Þcy

0.According to the technique of

producing qxð0Þ in encryption, Cx ¼ bqxð0Þ�rn�RðkÞn ; for each

leaf node n 2 T�.Stage 2: Stage 1 is repeated.

In this method, the cloud server acquires the fractional

set of secret key elements of the client and the proxy re-

encryption keys. So, this needs that the leakage does not

affect the confidentiality of data. In attribute adjunction, the

data owner selects a randomly symmetric key k0f and s0 2 Zp

for the root node of the access control tree. Then, it pro-

duces the proxy re-encryption keys, i.e.,

rks$s0 ; rkqxð0Þ$q0xð0Þ; rkn$n0 . In the proxy re-encryption pro-

cedure, a cloud server does not decrypt cipher text for not

recovering secret numbers s0. They acquire random num-

bers only while obtaining the proxy re-encryption keys.

When a client accesses the encrypted file, the cloud servers

initially update the secret key of the client. Cloud servers

can not acquire all the secret key elements of a user because

SA will not get updated at all times. Then, they do not

decrypt the encrypted file data.

Figures 4–9 prove the calculation overhead brought

about in the stages set-up, key age, and encryption and

decoding under different conditions (figures 1). Figure 2, 3

proves the framework-wide set-up time with the various

number of trait specialists. Also in the proposed method,

comparison was made with the existing attribute-based

encryption scheme [28] based on parameters like set-up

time, key generation time, encryption time as well as

decryption time.

Figure 4 proves the all-out key age time with the various

number of experts, and the quantity of attributes is fixed to

20.

Sådhanå (2021) 46:229 of 14 229

Figures 5 and 6 prove the encryption and decoding time

with the various number of characteristics and set just one

benefit for document access to gauge the most continuous

task, the record gets with file size is 100 KB.

Figures 7 and 8 prove the encryption and separating time

with various document sizes, where the quantity of assigns

is fixed to 20.

4.2 Utilization Rate

The average evolutionary curves of SLPSO, GA, and our

proposed method are given. The parallel axis denotes the

number of calculations and the perpendicular axis denotes

the yield value. It can be detected that in initial iterations

SLPSO and GA can join more quickly than this projected

method but the problem is SLPSO and GA cannot contin-

uously evolve the swarm to find a better explanation. The

operation rate of VM or memory at the ith time slot for the

secluded cloud is designed as,

CðiÞ ¼XS

n¼1

RTnaniTR

; i 2 1; 2; :::; If g

Where, RT� Number of VM or memory in the private

cloud

TR� Total VM or memory in the private cloud

If assignment tn runs in the ith slot, then ani ¼ 1; other-

wise, ani ¼ 0. Then, the normal operation rate of VM or

memory is designed by

Av ¼XI

i¼1

C ið ÞI

The average virtual machine utilization rate and average

memory utilization rate of the proposed method are given

in table 1 and also have contrasted the proposed strategy

and existing methods like SLPSO and GA-based methods

and the comparison chart is shown in figure 4.

Figure 1. Flow diagram of the proposed system.

Figure 2. Working Illustration/Creation of Message-Digest SHA

512.

229 of 14 Sådhanå (2021) 46:229

From figure 9 and table 1, it can be understood that the

proposed method can accomplish a higher source operation

ratio for the average virtual machine and average memory

than the SLPSO algorithm and GA.

4.3 Execution Cost

Figure 10 shows the comparison between the Execution

cost of the SLPSO algorithm and GA with our proposed

system. The graph showed that our proposed system

executes with less cost as per increasing user demands.

Thus the cost of SLPSO and GA is higher than our pro-

posed system.

4.4 Comparison of the proposed systemwith existing techniques

In this section, the proposed system is compared with

existing approaches like duel steganography combined with

AES, Steganography with DES, and Steganography with

Figure 3. Set-up Time.

Figure 4. KeyGen time with different authorities’ number.

Figure 5. Encryption time with different attributes number.

Figure 6. Decryption time with different attributes number.

Figure 7. Encryption time with the different file size.

Figure 8. Decryption time with the different file size.

Sådhanå (2021) 46:229 of 14 229

AES to evaluate the performance of the proposed system.

In order to evaluate the proposed system following

parameters are concerned Encryption time, Encryption

memory, Mean Square Error Value, Peak Signal to noise

ratio, and embedded ratio.

4.4a Encryption time: The amount of time required to

encrypt data using the selected algorithm is known as

encryption time (table 2). The comparative encryption time

of the proposed algorithms for cryptography is given using

figure 11. In the given diagram the X-axis shows the file

size (in terms of KB-kilobytes) of images used for experi-

ments and the Y-axis shows the amount of time consumed

for encryption in terms of seconds.In the proposed system,

the SHA algorithm is combined with Randomized

Ensemble Deep Learning for reducing the number of steps

for substitution and permutation operations which is

involved in the traditional AES based encryption. So the

proposed system has taken less time such that when the size

of an image is 100KB, it has taken 118 sec., whereas

existing approaches like duel steganography combined with

AES, Steganography with DES and Steganography with

AES experienced 315.48, 287.95 and 125.98 sec.

respectively.

4.4b Encryption memory: The measure of principle

memory required to execute the implemented encryption

algorithms is known as encryption memory. Figure 10

shows the relative performance of the proposed procedures

for the space complexity of encryption. The X pivot shows

the different examinations conducted by the framework,

whereas the Y pivot shows the consumption during

encryption in kilobytes. To compute the memory con-

sumption, the following formula is used (table 3; figures

12, 13, 14).

The proposed system has taken less memory since it

reduces the no of repetitions by adding features derived

from signature data to achieve the lightweight encryption

process. The result obtained showed that when the image

size is 100, the proposed system has taken a memory size of

15.633 KB whereas existing approaches like duel steganog-

raphy combined with AES, Steganography with DES, and

Steganography with AES experienced 31.25895, 21.25987,

and 25.12365, respectively.

4.4c MSE: The Mean Square Error is characterized as the

square of the distinction between the pixel estimations of

the first picture and the Steno picture and after that sepa-

rating it by the size of the picture (table 4).

In the proposed system, the least significant bit encoding

is combined with Randomized Ensemble Deep Learning to

embed the encrypted data into the cover image. So, this

approach perfectly embedded the data in less time when

compared to existing techniques such that when size is 100,

the proposed system experienced the less mean square error

value of 0.24 whereas existing approaches like duel

steganography combined with AES, Steganography with

DES and Steganography with AES experienced 0.551367,

0.497305 and 0.65925, respectively.

4.4d PSNR: The PSNR estimates the pinnacle signal-to-

commotion proportion between two pictures. This propor-

tion is frequently utilized as quality estimation between the

first and a packed picture. Higher the PSNR means better

the nature of the packed or repeated picture (table 5).

In the proposed system, Randomized Ensemble SVM for

reducing the number of repetition for basic operations such

as substitution and permutation which is involved in the

traditional AES based encryption and pixel vertex differ-

entiating technique is combined with least significant bit

encoding for embedding process increased the quality of

the embedded image so it achieves high PSNR value when

Table 1. Comparison of VM and Memory Utilization Rates of

SLPSO, GA and the Proposed Method.

Utilization Rate

Proposed

Method SLPSO GA

Average VM Utilization rate 0.911 0.892 0.709

Average memory Utilization

rate

0.822 0.774 0.682

Figure 9. Average VM and Memory Operation Rate for SLPSO,

GA and the Proposed method.

Figure 10. Comparison between Execution Cost of SLPSO and

GA with Proposed method.

229 of 14 Sådhanå (2021) 46:229

compared to other existing techniques. When size is

100 Kb, the proposed system experienced the high PSNR

value of 96.45 whereas existing approaches like duel

steganography combined with AES, Steganography with

DES and Steganography with AES experienced 52.673192,

54.164578 and 53.94774, respectively.

4.5 Comparison of the proposed methodand the existing method’s various parameters

In this section, the proposed system is compared with

existing approaches like Attribute based Access control

scheme (ABACS), Novel Attribute based access control

(NABAC), Multi authority Access based Encryption

(MAABE), Block chain based Multi authority Access

control (BMAC).

Figure 15 depicts the comparison for computation over-

head. Thus, the graph reveals that for the proposed method

attains low computation overhead when compared with the

previous technique.

Figure 16 depicts that comparison for delay. The graph

reveals that the proposed method attains less delay when

compared with the previous technique such as for ABACS

which has 500 ms. It gradually decreases for NABAC and

then it attains Maximum for MAABE, then it decreases to

Table 2. Comparison table for encryption time.

Data Size

(in KB )

Dual Steganography Combined with

AES (Time in sec.)

Steganography Combined with

DES (Time in sec.)


AES (Time in sec.) Proposed

10 561.10 261.94 114.6 72

20 466.94 308.66 777 79

40 758.12 314.81 116.3 89

60 364.64 350.98 116.25 95

80 285.20 230.65 115.31 119

100 315.48 287.95 125.98 120

10 20 30 40 50 60 70 80 90 100File Size(KB)

0

200

400

600

800

Tim

e(se

cond

s)

Encryption TimeDual Steganography combined with AESSteganography combined with DESSteganography combined with AESPROPOSED

Figure 11. Comparison graph for encryption time.

Table 3. Comparison table for memory requirement.

Data

Size(in

KB)

Dual Steganography Combined

with AES (Memory in KB)


DES (Memory in KB)


AES (Memory in KB)

Proposed

(Memory in

KB)

10 39.211914 19.598633 39.00098 13.450

20 55.18457 24.712891 38.4541 13.930

40 72.585938 27.175781 45.24316 14.329

60 25.279297 18.581055 37.20215 14.119

80 22.392578 20.542969 29.2666 15.211

100 31.25895 21.25987 25.12365 15.633

10 20 30 40 50 60 70 80 90 100File Size(Kb)

10

20

30

40

50

60

70

80

Mem

ory(

Kb)

Memory Consumption in EncryptionDual Steganography combined with AESSteganography combined with DESSteganography combined with AESPROPOSED

Figure 12. Comparison graph for memory usage.

Sådhanå (2021) 46:229 of 14 229

0.375 ms for BMAC and for the proposed which has 0.15

ms.

Figure 17 depicts that comparison for throughput. Thus

the graph reveals that the proposed method achieves high

throughput when compared with the previous technique

such as ABACS, NABAC, MAABE and BMAC.

Table 6 shows comparison of the proposed method with

various parameters. The computation overhead of the pro-

posed method obtained 9 ms when compared with prior

techniques as ABACS which has 727.6 ms, NABAC has

500 ms, MAABE has 300 ms and BMAC has 50 ms. The

delay of the proposed method obtained 0.15 ms when

compared with prior techniques as ABACS has 500 ms,

10 20 30 40 50 60 70 80 90 100File Size(Kb)

0.2

0.3

0.4

0.5

0.6

0.7

Err

orR

ate

Mean Square Error Rate

Dual Steganography combined with AESSteganography combined with DESSteganography combined with AESPROPOSED

Figure 13. Comparison graph for mean square error.

10 20 30 40 50 60 70 80 90 100File Size(Kb)

50

60

70

80

90

100

Rat

io%

PSNR

Dual Steganography combined with AESSteganography combined with DESSteganography combined with AESPROPOSED

Figure 14. Comparison graph for PSNR value.

Table 4. Comparison table for MSE.

Data Size

(in KB)


AES

Steganography with

DES


AES Proposed

10 0.3227109 0.4995313 0.449846 0.29

20 0.4156016 0.5329297 0.396492 0.30

40 0.6013047 0.5052734 0.454565 0.27

60 0.4822031 0.5118359 0.417235 0.26

80 0.5302891 0.6456641 0.46752 0.25

100 0.551367 0.497305 0.65925 0.24

Table 5. Comparison table for PSNR.

Data Size(in

KB)


AES


DES


AES Proposed

10 53.042667 51.145177 51.60016 97.23

20 57.501167 56.894584 52.14845 97.12

40 55.092265 51.095539 54.07282 96.23

60 52.307861 51.039496 51.927 96.16

80 56.981724 54.227387 52.478 96.03

100 52.673192 54.164578 53.94774 96.45

ABACS NABAC MAABE BMAC ProposedMethods

0

200

400

600

800

Time(ms)

Computation Overhead

Figure 15. Comparison for computation overhead.

229 of 14 Sådhanå (2021) 46:229

NABAC has 2.9 ms, MAABE has 1500 ms and BMAC has

0.375 ms. The throughput of the proposed method obtained

was 90% when compared with prior techniques such as

ABACS has 62%, NABAC has 65%, MAABE has 75% and

BMAC has 85%. Thus, the proposed method will effi-

ciently have enhanced the security.

5. Conclusion

IOT indicates to the following period of data upset whose

setting includes billions of smart gadgets and sensors

interconnected to encourage quick data and information

trade under continuous limitations. The large collection of

data leads to a large volume consumption, which paves

the way for the pre-processing requirements. Then, the

pre-processed data should be stored in the cloud-based

storage system. Since the proposed system used machine

learning algorithms for pre-processing data, it achieved

better results when compared to the existing one. Then

data to be stored is subjected to cryptography as well as

learning-based steganography, the integrity of the data is

high when compared to other existing methods. Therefore,

the proposed work attains the throughput as 15% higher

than the MAABE. Similarly, the delay is highly reduced

as 1499.85 ms than MAABE and also computation over-

head is highly diminished as 291 ms than MAABE. Thus,

the proposed method proficiently enhanced the data

security.

References

[1] Manogaran Gunasekaran, Varatharajan Ramachandran,

Lopez Daphne, Kumar Priyan Malarvizhi, Sundarasekar

Revathi and Thota Chandu 2018 A new architecture of

Internet of Things and big data ecosystem for secured smart

healthcare monitoring and alerting system. Future Gener.Comput. Syst. 82: 375–387

[2] Huang Yangjian, Junkai Xu, Bo Yu and Peter Shull B 2016

Validity of FitBit, Jawbone UP, Nike? and other wearable

devices for level and stair walking. Gait & Posture 48:

36–41

[3] Santos Alexandre, Macedo Joaquim, Costa Antonio and Joao

Nicolau M 2014 Internet of things and smart objects for

M-health monitoring and control. Procedia Technol. 16:

1351–1360

[4] Paradiso Rita and Gianni cola Loriga and Nicola Taccini,

2005 A wearable health care system based on knitted

integrated sensors. IEEE Trans. Inf. Technol. Biomed. 9(3):337–344

[5] Lorincz Konrad, David Malan J, Thaddeus Fulford-Jones R

F, Nawoj Alan, Clavel Antony, Shnayder Victor, Mainland

Geoffrey, Welsh Matt and Moulton Steve 2004 Sensor

networks for emergency response: challenges and opportu-

nities. IEEE Pervasive Comput. 3(4): 16–23[6] Ullah, Sana, Henry Higgin, Arif Siddiqui M and Kyung Sup

Kwak 2008 A study of implanted and wearable body sensor

networks. In: KES International Symposium on Agent andMulti-Agent Systems: Technologies and Applications464-473

[7] Snigdh and Itu 2019 Anonymity in Body Area Sensor

Networks-an Insight. In: 2018 IEEE World Symposium onCommunication Engineering (WSCE) 1-7

[8] Sankar S and Srinivasan P 2016 Internet of things (iot): A

survey on empowering technologies, research opportunities

and applications. Int. J. Pharmacy Technol 8(4):

26117–26141

[9] Hou Xueshi, Li Yong, Chen Min, Di Wu, Jin Depeng and

Chen Sheng 2016 Vehicular fog computing: A viewpoint of

vehicles as the infrastructures. IEEE Trans. VehicularTechnol. 65(6): 3860–3873

[10] Bonomi, Flavio, Rodolfo Milito, Preethi Natarajan and Jiang

Zhu 2014 Fog computing: A platform for internet of things

and analytics. In: Big data and internet of things: A roadmapfor smart environments 169-186


0

500

1000

1500Time(ms)

Delay

Figure 16. Comparison for delay.


60

70

80

90

Throughput(%)

Figure 17. Comparison for throughput.

Table 6. Shows the comparison of the proposed method and

previous methods with various parameters.

Methodologies

Computation

Overhead(ms)

Delay

(ms) Throughput(%)

ABACS 727.6 500 62

NABAC 500 2.9 65

MAABE 300 1500 75

BMAC 50 0.375 85

Proposed 9 0.15 90

Sådhanå (2021) 46:229 of 14 229

[11] Zhu, Jiang S, Douglas Chan, Mythili Suryanarayana Prabhu,

Preethi Natarajan, Hao Hu and Flavio Bonomi 2013

Improving web sites performance using edge servers in fog

computing architecture. In: Service Oriented System Engi-neering (SOSE), 2013 IEEE 7th International Symposium,320-323

[12] Ghorbani Reza H and Hossein Ahmadzadegan M 2017

Security challenges in internet of things: survey. In: WirelessSensors (ICWiSe), 2017 IEEE Conference 1-6

[13] Shree, Iniya, Narmatha K and Vijesh Joe C 2016 An multi-

authority attribute based encryption for personal health

record in cloud computing. In: 2016 10th InternationalConference on Intelligent Systems and Control (ISCO), 1-5

[14] Wang Shulan, Zhou Junwei, Liu Joseph K, Jianping Yu,

Chen Jianyong and Xie Weixin 2016 An efficient file

hierarchy attribute-based encryption scheme in cloud com-

puting. IEEE Transactions on Information Forensics andSecurity 11(6): 1265–1277

[15] Grandison, Tyrone, Michael Maximilien E, Sean Thorpe and

Alfredo Alba 2010 ‘Towards a formal definition of a

computing cloud. In: 2010 6th World Congress on Services,191-192

[16] Liu, Xuejiao, Yingjie Xia, Shasha Jiang, Fubiao Xia, and

YanboWang 2013 Hierarchical attribute-based access control

with authentication for outsourced data in cloud computing.

In: 2013 12th IEEE international conference on trust, securityand privacy in computing and communications 477-484

[17] Dastjerdi, Amir Vahid, and Buyya Rajkumar 2016 Fog

computing: Helping the Internet of Things realize its

potential. Computer 49(8): 112–116[18] Fugkeaw, Somchart and Hiroyuki Sato 2015 An extended

CP-ABE based Access control model for data outsourced in

the cloud. In: Computer Software and Applications Confer-ence (COMPSAC), 2015 IEEE 39th Annual, 3: 73-78

[19] Wan Zhiguo, Jun Liu E and Robert Deng H 2012 HASBE: a

hierarchical attribute-based solution for flexible and scalable

access control in cloud computing. IEEE Trans. Inf. Foren-sics Secur. 7(2): 743–754

[20] Yang, Kan, Xiaohua Jia, Kui Ren, Ruitao Xie and Liusheng

Huang 2014 Enabling efficient access control with dynamic

policy updating for big data in the cloud. In: INFOCOM,2014 Proceedings IEEE 2013-2021

[21] Gendreau, Audrey A and Michael Moorman 2016 Survey of

intrusion detection systems towards an end to end secure

internet of things. In: 2016 IEEE 4th international confer-ence on future internet of things and cloud (FiCloud), 84-90

[22] Can, Okan and Ozgur Koray Sahingoz 2015 A survey of

intrusion detection systems in wireless sensor networks.

In: 2015 6th International Conference on Modeling, Simu-lation, and Applied Optimization (ICMSAO), 1-6

[23] Yan Qiao, Huang Wenyao, Luo Xupeng, Gong Qingxiang

and Richard Yu F 2018 A multi-level DDoS mitigation

framework for the industrial internet of things. IEEECommuni. Mag. 56(2): 30–36

[24] Guo Qi, Li Xiaohong, Guangquan Xu and Feng Zhiyong

2017 MP-MID: Multi-Protocol Oriented Middleware-level

Intrusion Detection method for wireless sensor networks.

Future Gener. Comput. Syst. 70: 42–47[25] Sandhya G and Anitha Julian 2014 Intrusion detection in

wireless sensor network using genetic K-means algorithm.

In: 2014 IEEE International Conference on Advanced Com-munications, Control and Computing Technologies1791-1794

[26] Qiu Meikang, Gai Keke, Thuraisingham Bhavani, Tao Lixin

and Zhao Hui 2018 Proactive user-centric secure data

scheme using attribute-based semantic access controls for

mobile clouds in financial industry. Future Gener. Comput.Syst. 80: 421–429

[27] Rahmani Amir M, Gia Tuan Nguyen, Negash Behailu,

Anzanpour Arman, Azimi Iman, Jiang Mingzhe and Lilje-

berg Pasi 2018 Exploiting smart e-health gateways at the

edge of healthcare internet-of-things: a fog computing

approach. Future Gener. Comput.Syst. 78: 641–658[28] Thirumalai, Chandrasegar and Himanshu Kar 2017 Memory

Efficient Multi Key (MEMK) generation scheme for secure

transportation of sensitive data over Cloud and IoT devices.

In: Power and Advanced Computing Technologies (i-PACT),Innovations, 1-6

[29] Li Jin, Zhang Yinghui, Chen Xiaofeng and Xiang Yang 2018

Secure attribute-based data sharing for resource-limited users

in cloud computing. Comput.Secur. 72: 1–12[30] Xiong Hu, Zhang Hao and Sun Jianfei 2018 Attribute-based

privacy-preserving data sharing for dynamic groups in cloud

computing. IEEE Syst. J. 99: 1–22

229 of 14 Sådhanå (2021) 46:229

randomized ensemble svm based deep learning with

Documents