A Novel Approach for Intrusion Detection Using Swarm Intelligence

Sailaja, M.; Kumar, R. Kiran; Murty, P Sita Rama; Prasad, P. E. S. N. Krishna

doi:10.1007/978-3-642-27443-5_54

Cited by 5 publications

(2 citation statements)

References 6 publications

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“…The concept behind the IoT is to connect not just humans and computers as well as day-to-day objects to the Internet. This could be accomplished by outfitting things with computing and communication capacities hence altogether mapping the physical world to the digital one [4]. The initial years of the IoT mainly included data communication through machine to machine (M2M) communication.…”

Section: Introductionmentioning

confidence: 99%

A Hybrid Network Anomaly Detection system using Glowworm Swarm Optimization with Principal Component Analysis

Khilar

Mariyappan

Christo

et al. 2021

Preprint

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The security of the network is a significant issue in any distributed system. For that intrusion detection system (IDS), have been proposed for securing the network from malicious activities. This research is proposed to design and develop an anomaly detection model for detecting attacks and unusual activities in IoT networks. The primary objective of this research is to design efficient IDS for IoT network. The intrusion detection plays an essential role in detecting different attacks on IoT and enhances the performance of the IoT. In this research, anomaly detection in IoT networks using glowworm swarm optimization (GSO) algorithm with principal component analysis (PCA) is proposed. However, the proposed model is metaheuristic algorithm-based anomaly detection model to identify attacks by using the NSL-KDD dataset. The GSO algorithm based on PCA is implemented to perform the anomaly detection. For feature extraction, the PCA is used, and for classification, the GSO algorithm is used. For performance analysis, various parameters like accuracy, precision, recall, detection rate and FAR are evaluated. For normal class the proposed model achieved 94.14% accuracy, for DoS 95.52%, for R2L 93.15%, for probe 93.50% and for U2R 88.62% accuracy. Overall the detection rate was 94.08% and FAR was 3.41%.

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Section: Introductionmentioning

confidence: 99%

A Hybrid Network Anomaly Detection system using Glowworm Swarm Optimization with Principal Component Analysis

Khilar

Mariyappan

Christo

et al. 2021

Preprint

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“…. Recently, Swarm Intelligence (SI) techniques have been adapted by research community in the field of intrusion detection specially in Network [10,11]. Swarm Intelligence is a measure of collective behavior of ant, fish, bird or other societies of animal to implement algorithm.…”

Section: Introductionmentioning

confidence: 99%

A Hybrid Database Intrusion Detection Algorithm Using Swarm Intelligence and Radial Basis Function Network

Brahma¹,

Panigrahi²,

Mahapatra³

2019

helix

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Recently, all over the globe, intrusion detection especially in Database gaining lots of attentions from researchers as it serves a security support system to existing security mechanism. This paper presents a hybrid intrusion detection algorithm in Database System by making use of swarm intelligence and radial basis function network. Both these techniques are combined to produce better detection results. The performance of the proposed approach is investigated with synthetic dataset and is compared with other machine learning technique and found to be significantly better

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Reformed Binary Gray Wolf Optimizer (RbGWO) to Efficiently Detect Anomaly in IoT Network

Agrawal

Singh

2023

Lecture Notes in Electrical Engineering

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A Novel Approach for Intrusion Detection Using Swarm Intelligence

Cited by 5 publications

References 6 publications

A Hybrid Network Anomaly Detection system using Glowworm Swarm Optimization with Principal Component Analysis

A Hybrid Network Anomaly Detection system using Glowworm Swarm Optimization with Principal Component Analysis

A Hybrid Database Intrusion Detection Algorithm Using Swarm Intelligence and Radial Basis Function Network

Reformed Binary Gray Wolf Optimizer (RbGWO) to Efficiently Detect Anomaly in IoT Network

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