Optimized Multi - Layer Hierarchical Network Intrusion Detection System with Genetic Algorithms

Santikellur, Pranesh; Haque, T.; Al-Zewairi, Malek; Chakraborty, Rajat Subhra

doi:10.1109/ictcs.2019.8923067

Cited by 6 publications

(4 citation statements)

References 17 publications

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“…Methodologies such as Genetic Algorithms (GA) [122], Particle Swarm Optimization (PSO) [123] and Ant Colony Optimization (ACO) [124]) are now used for feature selection and optimization for solving problems ranging from the detection of malware in Android OS to the improvement of intrusion detection systems. Unfortunately, to be efficiently deployed to production-quality scenarios, the bio-inspired methods require facing several problems, such as solving the imbalance of a dataset [125], tuning the configurations of neural network models [126], as well as finding the optimal combination of parameters while avoiding the problem of falling into local optimal solution [127]. However, GA algorithms demonstrated their capability for obtaining a strong generalization ability and robustness by finding the best learner group for ensemble models [128].…”

Section: E Bio-inspired and Other Detection Methodsmentioning

confidence: 99%

Tight Arms Race: Overview of Current Malware Threats and Trends in Their Detection

Caviglione

Choraś

Corona³

et al. 2021

IEEE Access

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Section: E Bio-inspired and Other Detection Methodsmentioning

confidence: 99%

Tight Arms Race: Overview of Current Malware Threats and Trends in Their Detection

Caviglione

Choraś

Corona³

et al. 2021

IEEE Access

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“…Their results suggested that ELM has better performance when presented with large data, while SVM performed the best with relatively small data. Santikellur et al (2019) proposed and evaluated the use of a new multi-layer network-based IDS to improve the detection rate of modern network attacks. The proposed system implements a hierarchical architecture of multiple machine learning models optimised using evolutionary computing algorithms organised into a two-layer architecture.…”

Section: Literature Reviewmentioning

confidence: 99%

“…The proposed system implements a hierarchical architecture of multiple machine learning models optimised using evolutionary computing algorithms organised into a two-layer architecture. The first layer consists of several variants of three binomial classifiers (i.e., AdaBoost, ANN and NB), while the second layer has a decision tree (DT) multi-class classifier [23]. The authors evaluated the system using the CIC-IDS-2017 dataset.…”

Section: Literature Reviewmentioning

confidence: 99%

“…In conclusion, it is apparent that, although many researchers have tried to address the issue of detecting unknown attacks [12][13][14][15][16][17][18]20,21,23,28,29,31], the proposed techniques either lacked rigorous analysis and evaluation [15][16][17]21] or had a flawed definition of unknown attacks [12][13][14]18,23]. The lack of a complete solution for the problem demonstrates the need for further research in this area.…”

Section: Literature Reviewmentioning

confidence: 99%

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Unknown Security Attack Detection Using Shallow and Deep ANN Classifiers

2020

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Advancements in machine learning and artificial intelligence have been widely utilised in the security domain, including but not limited to intrusion detection techniques. With the large training datasets of modern traffic, intelligent algorithms and powerful machine learning tools, security researchers have been able to greatly improve on the intrusion detection models and enhance their ability to detect malicious traffic more accurately. Nonetheless, the problem of detecting completely unknown security attacks is still an open area of research. The enormous number of newly developed attacks constitutes an eccentric challenge for all types of intrusion detection systems. Additionally, the lack of a standard definition of what constitutes an unknown security attack in the literature and the industry alike adds to the problem. In this paper, the researchers reviewed the studies on detecting unknown attacks over the past 10 years and found that they tended to use inconsistent definitions. This formulates the need for a standard consistent definition to have comparable results. The researchers proposed a new categorisation of two types of unknown attacks, namely Type-A, which represents a completely new category of unknown attacks, and Type-B, which represents unknown attacks within already known categories of attacks. The researchers conducted several experiments and evaluated modern intrusion detection systems based on shallow and deep artificial neural network models and their ability to detect Type-A and Type-B attacks using two well-known benchmark datasets for network intrusion detection. The research problem was studied as both a binary and multi-class classification problem. The results showed that the evaluated models had poor overall generalisation error measures, where the classification error rate in detecting several types of unknown attacks from 92 experiments was 50.09%, which highlights the need for new approaches and techniques to address this problem.

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A practical intrusion detection system based on denoising autoencoder and LightGBM classifier with improved detection performance

Ayubkhan

Yap

Morris

et al. 2022

J Ambient Intell Human Comput

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Optimized Multi - Layer Hierarchical Network Intrusion Detection System with Genetic Algorithms

Cited by 6 publications

References 17 publications

Tight Arms Race: Overview of Current Malware Threats and Trends in Their Detection

Tight Arms Race: Overview of Current Malware Threats and Trends in Their Detection

Unknown Security Attack Detection Using Shallow and Deep ANN Classifiers

A practical intrusion detection system based on denoising autoencoder and LightGBM classifier with improved detection performance

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