Anomaly-Based Intrusion Detection Systems for Mobile Ad Hoc Networks

Valiveti, Sharada; Manglani, Anush; Desai, Tadrush

doi:10.4018/ijsssp.2021070102

Cited by 3 publications

(3 citation statements)

References 23 publications

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“…They are available for different types of networks with several attacks incorporated within the log. Details of some of the most prevalent data sets are discussed in [6]. Some specific characteristics of these datasets are shown in 1. Study concludes that the datasets of DARPA series and KDDCup99 are created using primitive traffic and do not offer better estimates of the modern network traffic.…”

Section: Datasetsmentioning

confidence: 99%

Anticipated Network Surveillance - An extrapolated study to predict cyber-attacks using Machine Learning and Data Analytics

Srivast¹,

Thakkar

Valiveti

et al. 2022

Preprint

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Machine learning and data mining techniques are utiized for enhancement of the security of any network. Researchers used machine learning for pattern detection, anomaly detection, dynamic policy setting, etc. The methods allow the program to learn from data and make decisions without human intervention, consuming a huge training period and computation power. This paper discusses a novel technique to predict an upcoming attack in a network based on several data parameters. The dataset is continuous in real-time implementation. The proposed model comprises dataset pre-processing, and training, followed by the testing phase. Based on the results of the testing phase, the best model is selected using which, event class which may lead to an attack is extracted. The event statistics are used for attack prediction. Implementation of the proposed architecture is evaluated against accuracy, precision, recall, and F1 score for processed data.

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Section: Datasetsmentioning

confidence: 99%

Anticipated Network Surveillance - An extrapolated study to predict cyber-attacks using Machine Learning and Data Analytics

Srivast¹,

Thakkar

Valiveti

et al. 2022

Preprint

View full text Add to dashboard Cite

show abstract

“…Participating nodes need to update their routing tables frequently, resulting in flooding control packets in the network which consume precious network resources. As a result, in ad hoc networks, it is hard to construct and maintain routes [26].…”

Section: Introductionmentioning

confidence: 99%

An Efficient Bio-inspired Routing Scheme for Tactical Ad hoc Networks

Khanpara

Valiveti

2023

SCPE

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Ad hoc networks are temporary networks, created mainly for applications that are infrastructure-less. Such networks and network nodes demand special characteristics like mobile nodes having dynamic topology, wireless medium, heterogeneous deployment environment, and reactive or proactive routing depending on the nature of the network which includes network parameters such as node placement, mobility model, number of participants in the network, patterns of mobility, etc. Due to these characteristics and the mobility of network nodes, the process of routing is quite challenging in the ad hoc environment, especially when the node mobility is high. Bio-inspired routing can be an effective solution to meet all the design requirements and deal with the issues of tactical ad hoc networks. Different types of nature-inspired routing mechanisms are possible to use for tactical networks. This paper proposes the design of a novel Ant Colony Optimization-based routing strategy for ad hoc networks. Ant-based algorithms are dynamic and have adaptive behavior. Hence, they are competent for routing in ad hoc networks. Our proposed routing scheme is evaluated based on the network’s performance by varying different parameters. The performance of our proposed ACO-based routing approach is also compared to some existing ad hoc routing mechanisms. Different metrics in different deployment scenarios that can affect the efficiency of our proposed protocol are taken into consideration to evaluate the performance.

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“…The attacker does not G. Madhu 2 interfere with the network's functionality but instead seeks out important information in passive attacks. Active attacks produced varying degrees of network damage depending on the sort of attack [4]. As a result, several researchers focused on providing security agencies in MANETs, even though security is the most important factor in MANET application acceptance.…”

Section: Introductionmentioning

confidence: 99%

Design of Intrusion Detection and Prevention Model Using COOT Optimization and Hybrid LSTM-KNN Classifier for MANET

Madhu

2022

EAI Endorsed Scal Inf Syst

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INTRODUCTION: MANET is an emerging technology that has gained traction in a variety of applications due to its ability to analyze large amounts of data in a short period of time. Thus, these systems are facing a variety of security vulnerabilities and malware assaults. Therefore, it is essential to design an effective, proactive and accurate Intrusion Detection System (IDS) to mitigate these attacks present in the network. Most previous IDS faced challenges such as low detection accuracy, decreased efficiency in sensing novel forms of attacks, and a high false alarm rate. OBJECTIVES: To mitigate these concerns, the proposed model designed an efficient intrusion detection and prevention model using COOT optimization and a hybrid LSTM-KNN classifier for MANET to improve network security. METHODS: The proposed intrusion detection and prevention approach consist of four phases such as classifying normal node from attack node, predicting different types of attacks, finding the frequency of attack, and intrusion prevention mechanism. The initial phases are done through COOT optimization to find the optimal trust value for identifying attack nodes from normal nodes. In the second stage, a hybrid LSTM-KNN model is introduced for the detection of different kinds of attacks in the network. The third stage performs to classify the occurrence of attacks. RESULTS: The final stage is intended to limit the number of attack nodes present in the system. The proposed method's effectiveness is validated by some metrics, which achieved 96 per cent accuracy, 98 per cent specificity, and 35 seconds of execution time. CONCLUSION: This experimental analysis reveals that the proposed security approach effectively mitigates the malicious attack in MANET.

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Anomaly-Based Intrusion Detection Systems for Mobile Ad Hoc Networks

Cited by 3 publications

References 23 publications

Anticipated Network Surveillance - An extrapolated study to predict cyber-attacks using Machine Learning and Data Analytics

Anticipated Network Surveillance - An extrapolated study to predict cyber-attacks using Machine Learning and Data Analytics

An Efficient Bio-inspired Routing Scheme for Tactical Ad hoc Networks

Design of Intrusion Detection and Prevention Model Using COOT Optimization and Hybrid LSTM-KNN Classifier for MANET

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