Role of swarm and evolutionary algorithms for intrusion detection system: A survey

Thakkar, Ankit; Lohiya, Ritika

doi:10.1016/j.swevo.2019.100631

Cited by 80 publications

(27 citation statements)

References 86 publications

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“…Machine learning-based IDSs can reach satisfactory detection levels, and machine learning models have sufficient generalizability to detect attack variants and novel threats. The promising research area in computer science, derived from SWEVO algorithms, is motivated by the natural evolution of biological organisms [15]. Many heuristic algorithms obtained from the natural behavior of biological or physical systems were suggested as robust methods for global optimization [16].…”

Section: Swarm Optimization Evolutionary Algorithms (Swevo) and Machine Learning Algorithms Machine Learning (Ml)mentioning

confidence: 99%

A new proactive feature selection model based on the enhanced optimization algorithms to detect DRDoS attacks

Nuiaa

Manickam

Alsaeedi

et al. 2022

IJECE

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Cyberattacks have grown steadily over the last few years. The distributed reflection denial of service (DRDoS) attack has been rising, a new variant of distributed denial of service (DDoS) attack. DRDoS attacks are more difficult to mitigate due to the dynamics and the attack strategy of this type of attack. The number of features influences the performance of the intrusion detection system by investigating the behavior of traffic. Therefore, the feature selection model improves the accuracy of the detection mechanism also reduces the time of detection by reducing the number of features. The proposed model aims to detect DRDoS attacks based on the feature selection model, and this model is called a proactive feature selection model proactive feature selection (PFS). This model uses a nature-inspired optimization algorithm for the feature subset selection. Three machine learning algorithms, i.e., k-nearest neighbor (KNN), random forest (RF), and support vector machine (SVM), were evaluated as the potential classifier for evaluating the selected features. We have used the CICDDoS2019 dataset for evaluation purposes. The performance of each classifier is compared to previous models. The results indicate that the suggested model works better than the current approaches providing a higher detection rate (DR), a low false-positive rate (FPR), <span>and increased accuracy detection (DA).</span> The PFS model shows better accuracy to detect DRDoS attacks with 89.59%.

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Section: Swarm Optimization Evolutionary Algorithms (Swevo) and Machine Learning Algorithms Machine Learning (Ml)mentioning

confidence: 99%

A new proactive feature selection model based on the enhanced optimization algorithms to detect DRDoS attacks

Nuiaa

Manickam

Alsaeedi

et al. 2022

IJECE

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“…The huge amount of logging information generated by complex Big Data infrastructures is, without a doubt, a rich substrate for detecting, identifying and counteracting security threats. The self-organizing nature of bio-inspired computation can provide the required level of robustness and resilience against such threats, specially those inspired by artificial immune systems for authentication and access control systems [ 246 ], evolutionary algorithms as constituent parts of intrusion detection systems relying on predictive modeling [ 247 ], or swarm intelligence methods for forensic analysis [ 248 ]. The record of successes around the application of bio-inspired methods to the security of complex networked systems is a motivational evidence towards embracing them massively in the Big Data realm.…”

Section: Critical Analysis Open Challenges and Research Directionsmentioning

confidence: 99%

Bio-inspired computation for big data fusion, storage, processing, learning and visualization: state of the art and future directions

Torre-Bastida

Díaz-de-Arcaya

Osaba

et al. 2021

Neural Comput & Applic

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This overview gravitates on research achievements that have recently emerged from the confluence between Big Data technologies and bio-inspired computation. A manifold of reasons can be identified for the profitable synergy between these two paradigms, all rooted on the adaptability, intelligence and robustness that biologically inspired principles can provide to technologies aimed to manage, retrieve, fuse and process Big Data efficiently. We delve into this research field by first analyzing in depth the existing literature, with a focus on advances reported in the last few years. This prior literature analysis is complemented by an identification of the new trends and open challenges in Big Data that remain unsolved to date, and that can be effectively addressed by bio-inspired algorithms. As a second contribution, this work elaborates on how bio-inspired algorithms need to be adapted for their use in a Big Data context, in which data fusion becomes crucial as a previous step to allow processing and mining several and potentially heterogeneous data sources. This analysis allows exploring and comparing the scope and efficiency of existing approaches across different problems and domains, with the purpose of identifying new potential applications and research niches. Finally, this survey highlights open issues that remain unsolved to date in this research avenue, alongside a prescription of recommendations for future research.

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“…Her sınıfın testi için ayrı ayrı veri setleri oluşturmuşlardır. Thakkar ve Lohiya [12] saldırı tespit sistemleri için üretilen sığ ve derin makine öğrenmesi modellerinin performanslarını artırmak için kullanılan sürü ve evrimsel yöntemleri kullanan çalışmaları incelemişler ve sonuçları karşılaştırmalı olarak sunmuşlardır. Hosseini [13] gerçekleştirdiği çalışmada öznitelik seçimi için hem genetik algoritma hem de parçacık sürü optimizasyonunu kullanmıştır.…”

Section: Literatürdeki İlgili çAlışmalarunclassified

Genetic Feature Selection Approach in Detection of Web Application Attacks Using Machine Learning Methods

Ahmetoglu

Daş

2021

Türkiye Bilişim Vakfı Bilgisayar Bilimleri Ve Mühendisliği Dergisi

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Applications on the Internet have some coding-related security concerns. Weaknesses or vulnerabilities allow criminals to gain direct and public access to databases to steal sensitive data. This study proposes an approach based on heuristic feature selection and machine learning for easier and more accurate detection of web application attacks with hybrid intrusion detection systems. Web application attacks and benign flow examples in CIC-IDS2017 and CSE-CIC-IDS2018 datasets were combined after a series of data preprocessing stages, and a new dataset was created. Using Genetic Algorithm and LogisticRegression, mean square error and feature count optimization were performed, and the results were tested with five different machine learning algorithms. When the results obtained were examined, it was observed that the success rate in classification remained at the level of 99%, although the number of features was reduced by 85%

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Role of swarm and evolutionary algorithms for intrusion detection system: A survey

Cited by 80 publications

References 86 publications

A new proactive feature selection model based on the enhanced optimization algorithms to detect DRDoS attacks

A new proactive feature selection model based on the enhanced optimization algorithms to detect DRDoS attacks

Bio-inspired computation for big data fusion, storage, processing, learning and visualization: state of the art and future directions

Genetic Feature Selection Approach in Detection of Web Application Attacks Using Machine Learning Methods

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