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 11 publications

(4 citation statements)

References 36 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…These networks are composed of resource-limited (memory, CPU, bandwidth, battery), low-cost, and small nodes that perform communication in a wireless manner (Akyildiz et al , 2002; Chen et al , 2009; Islabudeen and Kavitha Devi, 2020). The communication between the source node and the destination node is performed with in the limited communication range (Krishnan et al , 2020; Srilakshmi and Muthukuru, 2021). The hostile deployment environments, the quick deployment practices, the absence of infrastructure, and the broad mind of the wireless communication provide the sensor networks susceptible to a wide variety of security related attacks, mostly aiming the data or control traffic (Roman et al , 2006).…”

Section: Introductionmentioning

confidence: 99%

Literature review on network security in Wireless Mobile Ad-hoc Network for IoT applications: network attacks and detection mechanisms

Pamarthi

Narmadha

2021

IJIUS

View full text Add to dashboard Cite

PurposeNowadays, more interest is found among the researchers in MANETs in practical and theoretical areas and their performance under various environments. WSNs have begun to combine with the IoT via the sensing capability of Internet-connected devices and the Internet access ability of sensor nodes. It is essential to shelter the network from attacks over the Internet by keeping the secure router.Design/methodology/approachThis paper plans to frame an effective literature review on diverse intrusion detection and prevention systems in Wireless Sensor Networks (WSNs) and Mobile Ad hoc NETworks (MANETs) highly suitable for security in Internet of Things (IoT) applications. The literature review is focused on various types of attacks concentrated in each contribution and the adoption of prevention and mitigation models are observed. In addition, the types of the dataset used, types of attacks concentrated, types of tools used for implementation, and performance measures analyzed in each contribution are analyzed. Finally, an attempt is made to conclude the review with several future research directions in designing and implementing IDS for MANETs that preserve the security aspects of IoT.FindingsIt observed the different attack types focused on every contribution and the adoption of prevention and mitigation models. Additionally, the used dataset types, the focused attack types, the tool types used for implementation, and the performance measures were investigated in every contribution.Originality/valueThis paper presents a literature review on diverse contributions of attack detection and prevention, and the stand of different machine learning and deep learning models along with the analysis of types of the dataset used, attacks concentrated, tools used for implementation and performance measures on the network security for IoT applications.

show abstract

Section: Introductionmentioning

confidence: 99%

Literature review on network security in Wireless Mobile Ad-hoc Network for IoT applications: network attacks and detection mechanisms

Pamarthi

Narmadha

2021

IJIUS

View full text Add to dashboard Cite

show abstract

“…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

View full text Add to dashboard Cite

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.

show abstract

“…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

View full text Add to dashboard Cite

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.

show abstract

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

Cited by 11 publications

References 36 publications

Literature review on network security in Wireless Mobile Ad-hoc Network for IoT applications: network attacks and detection mechanisms

Literature review on network security in Wireless Mobile Ad-hoc Network for IoT applications: network attacks and detection mechanisms

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

Contact Info

Product

Resources

About