An intrusion detection system using Exponential Henry Gas Solubility Optimization based Deep Neuro Fuzzy Network in MANET

Ninu, S.B.

doi:10.1016/j.engappai.2023.105969

Cited by 10 publications

(2 citation statements)

References 21 publications

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“…Ninu 35 developed the Deep Neuro Fuzzy Network based on Exponential-Henry Gas Solubility Optimization (DNFN-EHGSO) for IDS in MANET. The suggested EHGSO approach was employed for selecting optimal routes during the early stages of safe routing.…”

Section: State-of-the-art Workmentioning

confidence: 99%

An artificial immune system‐based algorithm for selfish node detection in Mobile Ad Hoc Networks (MANETs)

Gowtham,

Vigenesh,

Ramkumar

2024

Trans Emerging Tel Tech

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The Mobile Ad Hoc Network (MANET) depending on dynamic distributed topology presents several challenges such as decentralized infrastructure. In MANET, each node act as a host as well as a router to exchange packet. From Source to destination, the MANET nodes offer the ability of transmission to send, receive, and route traffic. Moreover, the presence of selfish nodes or malicious nodes in MANETs significantly degrades network performance. Identifying and isolating such nodes poses a formidable challenge. Consequently, this research introduces a security model founded on the principles of an Artificial Immune System (AIS) to detect and defend against Selfish Node (SN) attacks. This research work employs the principles of AIS to categorize a node's behavioral state based on the Mature Context Immune Antigen Rate (MCIAR). Subsequently, a hybrid protocol named Reliable History‐dependent Resource Conscious Clustered and Defensive AODV (RRCC‐DAODV) is introduced to detect Selfish Nodes (SNs) and counteract their attacks. Within the RRCC algorithm, the identification of selfish nodes relies on both Trust Value (TV) and Residual Energy (RE). Additionally, the proposed DAODV protocol incorporates the V‐detector algorithm to enhance defense mechanisms against potential attacks. The simulation of the proposed model is carried out in NS3. The proposed mechanism achieves a detection ratio of 94.05%. The simulation outcomes demonstrate the excellence of the implemented system. This superiority is observed when compared to other standard protocols, as indicated by the parameters of throughput, residual energy, packet delivery ratio, and delay.

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Section: State-of-the-art Workmentioning

confidence: 99%

An artificial immune system‐based algorithm for selfish node detection in Mobile Ad Hoc Networks (MANETs)

Gowtham,

Vigenesh,

Ramkumar

2024

Trans Emerging Tel Tech

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“…Another concern pertains to the phenomenon of out-of-order packets. In the context of dynamic networks, it is possible for packets to be received in a non-sequential sequence as a result of fluctuating paths and delays [58]. In the event that an intrusion detection system (IDS) encounters the processing of packets in a non-sequential manner, there exists the possibility of misinterpreting the chronological sequence of events or experiencing a failure in detecting coordinated attacks.…”

Section: Limitations and Future Directionsmentioning

confidence: 99%

Hybrid Detection Technique for IP Packet Header Modifications Associated with Store-and-Forward Operations

Munshi

2023

Applied Sciences

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The detection technique for IP packet header modifications associated with store-and-forward operation pertains to a methodology or mechanism utilized for the identification and detection of alterations made to packet headers within a network setting that utilizes a store-and-forward operation. The problem that led to employing this technique lies with the fact that previous research studies expected intrusion detection systems (IDSs) to perform everything associated with inspecting the entire network transmission session for detecting any modification. However, in the store-and-forward process, upon arrival at a network node such as a router or switch, a packet is temporarily stored prior to being transmitted to its intended destination. Throughout the duration of storage, IDS operation tasks would not be able to store that packet; however, it is possible that certain adjustments or modifications could be implemented to the packet headers that IDS does not recognize. For this reason, this current research uses a combination of a convolutional neural network and long short-term memory to predict the detection of any modifications associated with the store-and-forward process. The combination of CNN and LSTM suggests a significant improvement in the model’s performance with an increase in the number of packets within each flow: on average, 99% detection performance was achieved. This implies that when comprehending the ideal pattern, the model exhibits accurate predictions for modifications in cases where the transmission abruptly increases. This study has made a significant contribution to the identification of IP packet header modifications that are linked to the store-and-forward operation.

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Crossover Boosted Grey Wolf Optimizer‐based framework for leader election and resource allocation in Intrusion Detection Systems for MANETs

Selvaraj,

Nanjappan,

Nagalingam

et al. 2024

Trans Emerging Tel Tech

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Mobile Ad hoc Networks (MANETs) is a self‐organizing networks without having a fixed infrastructure for making them susceptible to security threats. Intrusion Detection Systems (IDS) promotes security in MANETs by identifying malicious activities. Leader election is a fundamental aspect of IDS deployment, impacting resource allocation and system efficiency. This article presents a novel approach, the Crossover Boosted Grey Wolf Optimizer (CBGWO), for leader election and resource allocation in MANET‐based IDS. The proposed CBGWO algorithm integrates the Grey Wolf Optimizer (GWO) with innovative crossover operators that have an ability to enhance the capabilities of exploration and exploitation in the optimization process. The leader election problem is solved through applying multi‐objective optimization by considering energy consumption, reputation, and communication overhead. Objective functions are defined to maximize energy efficiency while maintaining a balanced distribution of leadership roles. Extensive simulations are conducted, varying network densities and the percentage of selfish nodes. Results demonstrate the effectiveness of the CBGWO‐based model in balancing energy consumption, prolonging network lifespan, and enhancing intrusion detection by comparing different state‐of‐the‐art models such as PCA‐FELM, CTAA‐MPSO, FLS‐RE, LEACH, DCAIDS, WOA‐GA, and VOELA. The proposed model achieved an energy consumption of 4.31 J, network lifetime of 560.482 ms, and average intrusion detection latency of 0.12 s, respectively. The proposed model outperforms than existing random and connectivity‐based leader election methods that is evaluated by taking main consideration of energy efficiency and network survivability. This research contributes to the field by introducing a robust algorithm for leader election in MANET‐based IDS, addressing challenges posed by network dynamics and resource constraints. The CBGWO‐based approach showcases its potential to achieve effective leader election and efficient resource allocation, thereby enhancing the security and sustainability of MANETs.

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An intrusion detection system using Exponential Henry Gas Solubility Optimization based Deep Neuro Fuzzy Network in MANET

Cited by 10 publications

References 21 publications

An artificial immune system‐based algorithm for selfish node detection in Mobile Ad Hoc Networks (MANETs)

An artificial immune system‐based algorithm for selfish node detection in Mobile Ad Hoc Networks (MANETs)

Hybrid Detection Technique for IP Packet Header Modifications Associated with Store-and-Forward Operations

Crossover Boosted Grey Wolf Optimizer‐based framework for leader election and resource allocation in Intrusion Detection Systems for MANETs

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