Intrusion detection in mobile ad-hoc network using Hybrid Reactive Search and Bat algorithm

Srilakshmi, R.; Muthukuru, Jayabhaskar

doi:10.1108/ijius-09-2020-0049

Cited by 10 publications

(4 citation statements)

References 36 publications

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“…Their application of autoencoders with the Modbus TCP protocol yielded an impressive F1 score of 98.36%. Similarly, [11][12][13] introduced an unsupervised Attention-based ConvLSTM Autoencoder with a Dynamic Thresholding (ACLAE-DT) framework for handling multivariate time series anomalies. By processing the data and creating feature images, they utilized an attention-based ConvLSTM autoencoder to discern temporal behaviors, achieving AD through dynamic thresholding of reconstruction errors [14][15].…”

Section: Literature Reviewmentioning

confidence: 99%

Adaptive Approach to Anomaly Detection in Internet of Things Using Autoencoders and Dynamic Thresholds

Shanmugam

et al. 2024

JMC

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The Internet of Things (IoT) represents a vast network of interconnected devices, from simple sensors to intricate machines, which collect and share data across sectors like healthcare, agriculture, and home automation. This interconnectivity has brought convenience and efficiency but also introduced significant security concerns. Many IoT devices, built for specific functions, may lack robust security, making them vulnerable to cyberattacks, especially during device-to-device communications. Traditional security approaches often fall short in the vast and varied IoT landscape, underscoring the need for advanced Anomaly Detection (AD), which identifies unusual data patterns to warn against potential threats. Recently, a range of methods, from statistical to Deep Learning (DL), have been employed for AD. However, they face challenges in the unique IoT environment due to the massive volume of data, its evolving nature, and the limitations of some IoT devices. Addressing these challenges, the proposed research recommends using autoencoders with a dynamic threshold mechanism. This adaptive method continuously recalibrates, ensuring relevant and precise AD. Through extensive testing and comparisons, the study seeks to demonstrate the efficiency and adaptability of this approach in ensuring secure IoT communications.

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Section: Literature Reviewmentioning

confidence: 99%

Adaptive Approach to Anomaly Detection in Internet of Things Using Autoencoders and Dynamic Thresholds

Shanmugam

et al. 2024

JMC

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“…SVM uses a hyperplane, a subset of supervised machine learning, to determine the best classification for each observation in a given data set. SVM is more effective with big datasets and can handle linear and non-linear problems [25]. SVM is incorporated into WSNs to handle various challenges, including congestion control, fault detection, routing, communication, and localization concerns.…”

Section: B Support Vector Machine (Svm)mentioning

confidence: 99%

Performance Analysis of Machine Learning-based Detection of Sinkhole Network Layer Attack in MANET

N¹,

Archana²

2022

IJACSA

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This paper proposes an Intrusion Detection System (IDS) against Sinkhole attacks in Mobile Adhoc Networks (MANET) with mobile sinks. A sinkhole attack is where a hacked node advertises a false routing update to draw network traffic. One effect of a sinkhole attack is that it may be used to launch further attacks, such as drops or changed routing information. Sinkhole nodes attempt to forge the source-destination routes to attract the surrounding network traffic. For this purpose, they modify routing control packets to publish fake routing information that makes sinkhole nodes appear as the best path to some destinations. Several machine learning techniques, including Decision Tree (DT), K-Nearest Neighbor (KNN), Convolution neural network (CNN), and Support Vector Machine (SVM), are used to do the categorization. Furthermore, the MANET's node's characteristics, particularly speed, are used for feature extraction. Totally 3997 unique samples, including 256 malicious samples and 3604 normal samples are collected. The categorization results demonstrate the accuracy of DT, KNN, CNN, and SVM at 98.4%, 96.7%, 98.6%, and 97.8%, respectively. The CNN approach is more accurate than other methods, at 98.6%, based on the data. After that, Priority, SVM, KNN, and CNN, in that order, each denotes excellent accuracy.

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“…A total of 20 characteristics are extracted from this model. For the detection of wormhole and other harmful attacks in MANET, a reactive search and the bat algorithm 18 are employed. By using an information exchange coordinator to create a hub, the approach gets rid of the centralised structure.…”

Section: Introductionmentioning

confidence: 99%

An adaptive technique for wormhole attack detection in MANET using quantized ad-hoc on-demand multipath distance vector routing protocol

Kumar

2023

International Conference on Mathematical and Statistical Physics, Computational Science, Education, and Communication (ICMSCE 2

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In a self-evolving network, security is an essential requirement, and ensuring it in any circumstance is the most difficult task because the network is very dynamic and lacks reliable centralised monitoring. MANETs are much more vulnerable to security attacks when compared to wired networks. There are two steps in this research: the routing stage and the wormhole detection stage. Quantum walk and reinforcement learning are used to improve the results of ad hoc on-demand multipath distance vector routing technology. Quantum walk detects the moving nodes from the area and updates it to the reinforcement learning table. The proposed model is faster since the adaptive African vulture optimization is employed for path selection. This optimization creates an ideal path from source to destination using the data obtained by the routing protocol and reinforcement learning. Round trip time and packet delivery rate are key elements in the detecting phase. This parameter establishes a threshold point for classifying suspicious and normal nodes. Depending on the type of attack, the node will either be terminated or recovered if it turns malicious. Performance indicators including the proportion of packet delivery ratio (98%), end-to-end latency (23.91 sec), packet loss (4.75%), energy consumption (57J), throughput (408/sec), and average E2E delay are examined and compared with existing models to validate the efficacy of the proposed model.

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Intrusion detection in mobile ad-hoc network using Hybrid Reactive Search and Bat algorithm

Cited by 10 publications

References 36 publications

Adaptive Approach to Anomaly Detection in Internet of Things Using Autoencoders and Dynamic Thresholds

Adaptive Approach to Anomaly Detection in Internet of Things Using Autoencoders and Dynamic Thresholds

Performance Analysis of Machine Learning-based Detection of Sinkhole Network Layer Attack in MANET

An adaptive technique for wormhole attack detection in MANET using quantized ad-hoc on-demand multipath distance vector routing protocol

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