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Nature-inspired adaptive decision support system for secured clustering in cyber networks

  • 1203: Applications of Advanced Artificial Intelligence in Multimedia and Information Security
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Abstract

The Internet of Things (IoT) technology has proved that Wireless Sensor Networks (WSN) is important for all IoT application areas. WSN combined with other advanced technologies like Artificial Intelligence (AI) brings automation via the sensing, transmitting, and monitoring steps. However, the cyber threats such as Malware, Distributed Denial of Service Attack (DDoS), and Man in the Middle (MitM), etc. limits the potential of such networks. Several security methods were introduced in last decade to protect the WSN from various cyber threats; however, due to resource-constrained sensor nodes, designing the energy-efficient security algorithm for WSN is a widely studied research problem. In the proposed work, a novel Nature-inspired Decision Support System for Secure Clustering (NIDSC) is proposed to overcome the security issues with minimum resource consumptions and computational overhead. In NIDSC, a hybrid trust model is designed to evaluate each sensor node before selecting Cluster Head (CH) by measuring various sensor node parameters, to achieve a reliable decision support system which classifies each node as either legitimate or attacker. Later, the proposed decision support system along with clustering optimization is formulated for CH selection using a natural evolution-based hybrid trust model. Due to its fast convergence over other optimization algorithms, the nature-inspired Differential Evolution (DE) algorithm is used to perform Decision Support System (DSS) for optimal and secure WSN clustering. The proposed method is a lightweight trust-based decision-making method for Quality of Service (QoS) clustering to establish secure data transmission in intra-cluster and/or inter-cluster communication. The simulation is carried out to analyze and compare the performance of the proposed method with the existing works such as Low Energy Adaptive Clustering Hierarchy(LEACH), Trust Management System (TMS), and Energy-efficient Trusted Moth Flame Optimization and Genetic Algorithm based clustering algorithm(eeTMFO/GA). The comparisons were mainly focused on throughput, Packet Delivery Ratio (PDR), delay, communication overhead, and energy consumption to validate the performance of the proposed method. The experimental results found that the proposed method has got improved throughput value (~3 kbps), improved PDR (~4%), minimum delay (~0.01 seconds), less communication overhead (~0.75 ms, and less energy consumption (~0.003 joules) as compared to the existing methods on various testcase scenarios.

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Abbreviations

NIDSC:

Nature-Inspired Decision-Support System for Secure Clustering

CH:

Cluster Head

BS:

Base Station

DE:

Differential Evolution

NS2/NS3:

Network Simulator-2/3

QoS:

Quality of Service

DDoS:

Distributed Denial of Service

MitM:

Man in the Middle

WSN:

Wireless Sensor Network

IoT:

Internet of Things

DSS:

Decision Support System

LEACH:

Low-Energy Adaptive Clustering Hierarchy

HEED:

Hybrid Energy Efficient Distributed protocol

HBMA:

Honey Bee Mating Algorithm

GA:

Genetic Algorithm

ABC:

Ant Bee Colony

TECP:

Threshold-sensitive Energy-efficient Clustering Protocol

MFO:

Moth Flam Optimization

PSO:

Particle Swarm Optimization

E2HRC:

Energy Efficient Heterogeneous Ring Clustering Routing protocol

SPT:

Shortest Path Tree

BS:

Base Station

TDMA:

Time Division Multiple Access

PTB:

Packet Transmission Behaviour

RER:

Residual Energy Ratio

DoC:

Degree of Connectivity

RSSI:

Received Signal Strength Indicator

ACK:

Acknowledgement

CM:

Cluster Member

TMS:

Trust Management Scheme

eeTMFO/GA:

Energy Efficient Trusted MFO and Genetic Algorithm

CBR:

Cooperative balancing routing

MAC:

Medium Access Control

S = {s 1, s 2, …, s N}:

sensor nodes

X × Y :

X and Y axis represents network width and height

C = {c 1, c 2, …, c M}:

(Clusters)

M :

Number of clusters.

A = {a 1, a 2, …, a Z}:

malicious nodes/ attackers.

N :

number of sensor nodes.

s i :

Source sensor node.

s j :

Neighbour sensor node of si.

t:

Time

ptb(s i(t)):

Packet transmission behavior of node si at time t

pfr(s i, j):

Faction to calculate packet forwarding ratio of siby sj

af(s i):

Availability factor of sibysj

w1 , w2:

weight parameters

FP(s i, j(t)):

number of successful forwarded packets by si at time t

af(s i, j):

availability factor of si and sj

AH(s i, j(t)):

number of acknowledged HELLO packets

NAH(s i, j(t)):

number of non-acknowledged HELLO packets

rer(s i):

residual energy of node si

E initial(s i):

initial energy of the node si

E residual(s i(t)):

remaining energy of the node si at time t

i ≠ j :

The Value of i is not equal to the value of j

dist (i, j):

geographical distance between the two nodes

rssi :

communication range of the nodei

cr(s i(t)):

congestion rate of node si at time congestion rate of node t

Q allocated(s i(t)):

current number of packets processing at node si at time t

Q total :

maximum capacity of queue allocated to each sensor node

f(s i(t)) f(s i):

of node siat time t

λ 1, λ 2, λ 3, λ 4 :

weight parameters

p :

Initial population

G :

Initial iteration

G max :

Maximum number of iterations

fpbest :

fitness particle (local) best value of a node

gbest :

global best value of the node

fgbest :

fitness global best value of a node

pbest :

particle (local) best value of the node

q :

current node

lnodes :

Legitimate nodes

mnodes :

Malicious nodes

M :

Number of clusters

CM :

Set of nodes in cluster (Cluster Member)

CH :

Cluster heads

getfitness :

Initial fitness value selected randomly from population

threshold :

Predefined value used to detect malicious node and legitimate nodes present in the network

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Authors and Affiliations

  1. School of Computing Science and Engineering, VIT Bhopal University, Bhopal, India

    Shahana Gajala Qureshi & Shishir Kumar Shandilya

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  1. Shahana Gajala Qureshi
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  2. Shishir Kumar Shandilya
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Correspondence to Shishir Kumar Shandilya.

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Qureshi, S.G., Shandilya, S.K. Nature-inspired adaptive decision support system for secured clustering in cyber networks. Multimed Tools Appl 83, 3153–3187 (2024). https://doi.org/10.1007/s11042-022-13336-7

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