A novel sophisticated hybrid method for intrusion detection using the artificial immune system

Farzadnia, Ehsan; Shirazi, Hossein; Nowroozi, Alireza

doi:10.1016/j.jisa.2020.102721

Cited by 17 publications

(10 citation statements)

References 28 publications

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“…Farzadnia et al [36], proposed a novel hybrid method for Intrusion Detection System (IDS) using an Artificial Immune System (AIS). The system consisted of two defensive lines.…”

Section: Discussing the Use Of Conditionally Classified "Distributed" Ai Methods In Cybersecuritymentioning

confidence: 99%

The impacts of artificial intelligence techniques in augmentation of cybersecurity: a comprehensive review

Naik

Mehta

Yagnik³

et al. 2021

Complex Intell. Syst.

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Given the prevailing state of cybersecurity, it is reasonable to understand why cybersecurity experts are seriously considering artificial intelligence as a potential field that can aid improvements in conventional cybersecurity techniques. Various progressions in the field of technology have helped to mitigate some of the issues relating to cybersecurity. These advancements can be manifested by Big Data, Blockchain technology, Behavioral Analytics, to name but a few. The paper overviews the effects of applications of these technologies in cybersecurity. The central purpose of the paper is to review the application of AI techniques in analyzing, detecting, and fighting various cyberattacks. The effects of the implementation of conditionally classified “distributed” AI methods and conveniently classified “compact” AI methods on different cyber threats have been reviewed. Furthermore, the future scope and challenges of using such techniques in cybersecurity, are discussed. Finally, conclusions have been drawn in terms of evaluating the employment of different AI advancements in improving cybersecurity.

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“…Farzadnia et al [36], proposed a novel hybrid method for Intrusion Detection System (IDS) using an Artificial Immune System (AIS). The system consisted of two defensive lines.…”

Section: Discussing the Use Of Conditionally Classified "Distributed" Ai Methods In Cybersecuritymentioning

confidence: 99%

The impacts of artificial intelligence techniques in augmentation of cybersecurity: a comprehensive review

Naik

Mehta

Yagnik³

et al. 2021

Complex Intell. Syst.

View full text Add to dashboard Cite

show abstract

“…For the binary classification problem, let the output probability of the first sample be p . Bringing it into equation (11), the expression of the Gini coefficient of its probability distribution can be obtained as:…”

Section: V-detector Intrusion Detection Model Buildingmentioning

confidence: 99%

“…Therefore, intrusion detection belongs to a typical classification problem [9][10]. As for SVM, one of the most typical classification algorithms, its most prominent feature is the use of the structural risk minimization principle for classification problems such as high dimensionality and nonlinearity, which improves the generalization ability of the learning machine [11][12]. It is due to the many advantages that exist in SVMs that a large number of researchers have successively applied SVMs to the field of intrusion detection.…”

Section: Introductionmentioning

confidence: 99%

Research on the application of improved V-detector algorithm in network intrusion detection

Zhong,

Chen

2023

Applied Mathematics and Nonlinear Sciences

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Network intrusion detection has been widely discussed and studied as an important part of protecting network security. Therefore, this paper presents an in-depth study of the application of an improved V-detector algorithm in network intrusion detection. In this paper, we construct a V-detector intrusion detection model, adopt the “self-oriented” identification principle, and randomly generate detectors with large differences from the health library. A smaller number of detectors are used to compare the data information generated by the computer, and if they are similar, they are judged as intrusions. Intrusion detection experiments are performed on multiple types of networks by using classifiers to determine whether the access to be detected is an attack access. The experimental results show that the model has the lowest false alarm rate for mixed feature networks, with a false alarm rate of only 13% and a detection rate of 89%, with a sample size of 25,987. After the improvement of the V-detector intrusion detection model, the error correction output problem leads to a network intrusion with a miss rate of only 11% and a protection rate of 85%. The experimental data proved that the model has the advantages of large data size and comprehensive intrusion attack types.

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“…Cybercrime activities on the internet primarily involve communication-based attacks. Detecting malicious network traffic with minimal cost is increasingly challenging for information security researchers [5]. Although there has been considerable interest in understanding the cyber threat landscape of IoT networks and devising security methodologies incorporating artificial intelligence (AI), a gap exists in distributed architecture.…”

Section: Introductionmentioning

confidence: 99%

Performance evaluation of multiclass classification models for ToN-IoT network device datasets

Soni,

Remli,

Daud

et al. 2024

IJEECS

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Internet of things (IoT) technology has empowered tangible objects to establish internet connections, facilitating data exchange with computational capabilities. With significant potential across sectors like healthcare, environmental monitoring, and industrial control, IoT represents a promising technological advancement. This study explores datasets from ToN-IoT’s IoT devices, focusing on multi-class classification, including normal and attack classes, with an additional aim of identifying potential attack sub-classes. Datasets comprise various IoT devices, such as refrigerators, garage doors, global positioning systems (GPS) sensors, motion lights, modbus devices, thermostats, and weather sensors. Comparative analysis is conducted between two prominent multiclass classification models, extreme gradient boosting (XGBoost) and light gradient boosting machine (LightGBM), utilizing accuracy and computational time metrics as evaluation criteria. Research findings highlight that the LightGBM model achieves superior accuracy at 78%, surpassing XGBoost 74.31%. However, XGBoost demonstrates an advantage with a shorter computational time of 1.23 seconds, compared to LightGBM 6.79 seconds. This study not only provides insights into multiclass classification model selection but also underscores the crucial consideration of the trade-off between accuracy and computational efficiency in decision-making. Research contributes to advancing our understanding of IoT security through effective classification methodologies. The findings offer valuable information for researchers and practitioners, emphasizing the nuanced decisions needed when selecting models based on specific priorities like accuracy and computational efficiency.

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A novel sophisticated hybrid method for intrusion detection using the artificial immune system

Cited by 17 publications

References 28 publications

The impacts of artificial intelligence techniques in augmentation of cybersecurity: a comprehensive review

The impacts of artificial intelligence techniques in augmentation of cybersecurity: a comprehensive review

Research on the application of improved V-detector algorithm in network intrusion detection

Performance evaluation of multiclass classification models for ToN-IoT network device datasets

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