HMM-Based IDS for Attack Detection and Prevention in MANET

Pathak, Priya; Chauhan, Ekta; Rathi, S.; Kosti, Siddhartha

doi:10.1007/978-981-10-3920-1_42

Cited by 4 publications

(1 citation statement)

References 8 publications

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“…The proposed mechanism is better than traditional HMM but is specific to agent-based systems and needs to include the effect of malicious attacks. Heading towards the attack, Pathak et al [11] proposed an intrusion detection system where they have applied two states' HMM to evaluate the reputation of vehicles. Safe and malicious are considered as a state while send, drop, and forward are regarded as observation states.…”

Section: Related Workmentioning

confidence: 99%

Hidden Markov Trust for Attenuation of Selfish and Malicious Nodes in the IoT Network

Joshi

Sharma

2022

Wireless Pers Commun

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The exposure of IoT nodes to the internet makes them vulnerable to malicious attacks and failures. These failures affect the survivability, integrity, and connectivity of the network. Thus the detection and elimination of attacks in a timely manner become an important factor to maintain the network connectivity. Trust-based techniques are used in understanding the behavior of nodes in the network. Several researchers have proposed conventional trust models that are power-hungry and demand large storage space. Succeeding this Hidden Markov Models have also been developed to calculate trust but the survivability of network achieved from them is low. To improve the survivability selfish and malicious nodes present in the network are required to be treated separately. Hence, an improved Hidden Markov Trust (HMT) Model is developed in this paper which accurately detects the selfish and malicious nodes that illegally intercept the network. An algorithm is generalized for learning the behavior of nodes using the HMT model with the expected output. The evaluated node's likelihood functions differentiate the selfish node from the malicious node and provide independent timely treatment to both types of nodes. Further, comparative analysis for attacks such as black-hole, grey-hole, and sink-hole has been done and performance parameters have been extended to survivability-rate, power-consumption, delay, and false-alarm-rate, for different networks sizes and vulnerability. Simulation result provides a 10% higher PDR, 29% lower overhead, and 15% higher detection rate when compared to FUCEM, FTCSPM, and OADM trust models presented in the literature.

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Section: Related Workmentioning

confidence: 99%

Hidden Markov Trust for Attenuation of Selfish and Malicious Nodes in the IoT Network

Joshi

Sharma

2022

Wireless Pers Commun

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Light-Weight Hidden Markov Trust Evaluation Model for IoT network

Joshi

Sharma

2021

2021 Fifth International Conference on I-Smac (IoT in Social, Mobile, Analytics and Cloud) (I-Smac)

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Novel Intrusion Detection System for MANETs using Hybrid Improved Grey Wolf Optimizer based deep recurrent neural network model

SATHIYA,

YUVARAJ

2023

Preprint

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The advent growth of wireless networks and their applications in different fields has been more prominent in the previous few years and specifically mobile ad-hoc networks (MANETs) has gained the importance from consumers and researchers due to their reliability and sustainability. MANET is widely employed for communicating and sending and receiving packets in a network without any requirement of specific hardware structure, due to which these are employed in different sectors. Because of its wide applicability, there exist numerous challenges in handling MANETs, especially with respect to network security. Intrusion is one of the key security problem faced in MANETs during the transmission of data packets within the communication system. The occurrence of malicious node in MANETs shall lead to removal of information packets during data transfer and thus intrusion detection system (IDS) are successfully designed to handle the node behaviours and identify the malicious nodes in the network and their behaviours. Henceforth, this research study develops a novel IDS model using the proposed improved grey wolf optimizer (ImGWO) hybridized with that of the deep recurrent neural network (DRNN) model for predicting and observing the behaviour of malicious nodes in MANETs. The developed novel hybrid ImGWO-DRNN model is applied on the KDD Cup 1999 datasets and training and testing of the proposed IDS technique is studied. Evaluation metrics was set to analyse and validate the proposed IDS technique and the results prove the superiority of this ImGWO-DRNN intrusion detection system over the previous techniques from literature for the MANETs.

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HMM-Based IDS for Attack Detection and Prevention in MANET

Cited by 4 publications

References 8 publications

Hidden Markov Trust for Attenuation of Selfish and Malicious Nodes in the IoT Network

Hidden Markov Trust for Attenuation of Selfish and Malicious Nodes in the IoT Network

Light-Weight Hidden Markov Trust Evaluation Model for IoT network

Novel Intrusion Detection System for MANETs using Hybrid Improved Grey Wolf Optimizer based deep recurrent neural network model

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