Swarm Intelligence inspired Intrusion Detection Systems — A systematic literature review

Nasir, Muhammad; Khan, Salman; Khan, Muhammad Mubashir; Fatima, Mahawish

doi:10.1016/j.comnet.2021.108708

Cited by 47 publications

(22 citation statements)

References 113 publications

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“…The KDDCup 99 dataset is one of the popular datasets in IoT with cybersecurity [33], [34], [35], [36], [37], [38], [39], [40], [41], [42], [43], [44], [45], [46], [47]. This dataset provides labelled and unlabeled training and testing data, and it originated from the evaluation program DARPA98 IDS with corresponds to seven and two weeks [33], [41], [48], [49], [50], [51], [52], [53], [54], [55], [56], [57], [58], [59], [60], [61], [62], [63], [64], [65], [66], [67], [68], [69], [70], [71], [72], [73], [74]. The UNSW-NB15 dataset was created by perfectStorm (IXIA) in collaboration with the UNSW Cyber Range Lab to generate moderately aggressive activities and attacks.…”

Section: Methodsmentioning

confidence: 99%

“…ML methods secure ICT on the network and physical levels by managing the information through packets and controlling anomalies [66]. The research on ML-AIDS identifies and efficiently implements the effective and efficient anomalies of networks and computers [70]. Recently, many researchers have been dedicated to developing ML with NIDs [41], [50], [51], [52], [53], [54], [55], [56], [57], [58], [59], [60], [61], [62], [63], [64], [65], [66], [67], [68], [69], [70].…”

Section: Related Workmentioning

confidence: 99%

“…The research on ML-AIDS identifies and efficiently implements the effective and efficient anomalies of networks and computers [70]. Recently, many researchers have been dedicated to developing ML with NIDs [41], [50], [51], [52], [53], [54], [55], [56], [57], [58], [59], [60], [61], [62], [63], [64], [65], [66], [67], [68], [69], [70]. The IDS faced challenges in accuracy by reducing false alarm rates.…”

Section: Related Workmentioning

confidence: 99%

“…For that reason, the DL with an IDS system was deployed as a potential solution to identify intrusion attacks [69]. Beyond that, binary and multiclass experiments were performed on the CSECIC-IDS2018 and the Bot-IoT datasets [70], [71], [72], [73], [74].…”

Section: Related Workmentioning

confidence: 99%

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Detection of Real-Time Malicious Intrusions and Attacks in IoT Empowered Cybersecurity Infrastructures

et al. 2023

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Computer viruses, malicious, and other hostile attacks can affect a computer network. Intrusion detection is a key component of network security as an active defence technology. Traditional intrusion detection systems struggle with issues like poor accuracy, ineffective detection, a high percentage of false positives, and an inability to handle new types of intrusions. To address these issues, we propose a deep learning-based novel method to detect cybersecurity vulnerabilities and breaches in cyber-physical systems. The proposed framework contrasts the unsupervised and deep learning-based discriminative approaches. This paper presents a generative adversarial network to detect cyber threats in IoT-driven IICs networks. The results demonstrate a performance increase of approximately 95% to 97% in terms of accuracy, reliability, and efficiency in detecting all types of attacks with a dropout value of 0.2 and an epoch value of 25. The output of well-known state-of-the-art DL classifiers achieved the highest true rate (TNR) and highest detection rate (HDR) when detecting the following attacks: (BruteForceXXS, BruteForceWEB, DoS_Hulk_Attack, and DOS_LOIC_HTTP_Attack) on the NSL-KDD, KDDCup99, and UNSW-NB15 datasets. It also maintained the confidentiality and integrity of users' and systems' sensitive information during the training and testing phases.

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Section: Methodsmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

See 2 more Smart Citations

Detection of Real-Time Malicious Intrusions and Attacks in IoT Empowered Cybersecurity Infrastructures

et al. 2023

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“…That is because the PSO finds the optimal subset features faster than the other algorithms. Moreover, the standard PSO suffers from the swarm's premature convergence, which causes stagnation during the iteration for the optimal features [19]. As a result, we consider the NSL-KDD benchmark dataset to evaluate the proposed solution.…”

Section: Introductionmentioning

confidence: 99%

Feature Selection of The Anomaly Network Intrusion Detection Based on Restoration Particle Swarm Optimization

2022

IJIES

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Intrusion detection systems are vital for detecting networking attacks due to their ability to analyze network data and find different types of attacks. The high-dimensional internet data leads to feature selection becoming a fundamental process in network intrusion detection systems. The current approaches are insufficient to determine the most effective features in the network data due to the nature of intrusion attacks appearance compared to the normal data. Moreover, the wrapper feature selection methods suffer from the search time complexity such as the standard PSO algorithm for feature selection. The standard Particle Swarms Optimization (PSO) algorithm suffers from the stagnation effect in local optima. This paper proposes a new wrapper feature selection model called Restoration Particle Swarms Optimization (RPSO) to select highly relevant feature data taking into consideration the limitation of the premature convergence between the particles which results in a stagnation problem during the iteration for the optimal features. Moreover, we utilize the randomness value to overcome the stagnation problem, reduce data volume and decrease the processing time. The Random Forest algorithm uses to classify the feature selected with our solution. As a result, we consider the NSL-KDD benchmark dataset to evaluate the proposed solution. The experiments show that the performance evaluation achieves high results in general accuracy (85%) compared to standard PSO up to 83.86%. Additionally, the results show that the proposed solution increased the detecting rate of low distributing classes in the training data up to (521 classes) compared with standard PSO by (79 classes).

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Swarm Intelligence in Action: Particle Swarm Optimization and Rendezvous Algorithms for Swarm Robotics

Ganduri,

Pathri

2024

Journal of Field Robotics

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Swarm technology is evolving quickly in the new era in the domains of autonomous underwater vehicles (AUVs), unmanned aerial vehicles (UAVs), and unmanned ground vehicles (UGVs). Swarm technology takes its cues from nature, particularly from the behaviors of ants, bees, schools of fish, and birds. The rapid growth of swarm technologies can be attributed to this organic inspiration. It is utilized in a variety of applications, such as pick‐and‐place robotics and drone swarms for laser shows. To mimic the behavior of swarm robots or microbots, this research work explores the use of swarm algorithms, namely particle swarm optimization (PSO) and rendezvous algorithms (RA). The emphasis will be on applying these algorithms to situations that require swarm robots to navigate around obstacles while planning their paths and forming patterns. This study shows how successful these algorithms are by using MATLAB as a useful tool. The outcomes demonstrate that the algorithms are capable of producing intricate patterns and facilitating effective path planning for swarms of robots. The robots' ability to avoid obstacles and travel around them at a high speed is noteworthy. This study covers a wide range of industries, such as space exploration, military/defense applications, storage, surveillance, and search and rescue. The application of swarm technology demonstrates how it may transform practical situations and provides information about how flexible and efficient swarm algorithms are in a variety of settings. By offering useful insights for researchers, engineers, and practitioners in utilizing the combined potential of swarm intelligence (SI) for a variety of applications, this investigation advances the field of swarm robotics.

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Swarm Intelligence inspired Intrusion Detection Systems — A systematic literature review

Cited by 47 publications

References 113 publications

Detection of Real-Time Malicious Intrusions and Attacks in IoT Empowered Cybersecurity Infrastructures

Detection of Real-Time Malicious Intrusions and Attacks in IoT Empowered Cybersecurity Infrastructures

Feature Selection of The Anomaly Network Intrusion Detection Based on Restoration Particle Swarm Optimization

Swarm Intelligence in Action: Particle Swarm Optimization and Rendezvous Algorithms for Swarm Robotics

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