Hybrid Intrusion Detection System using K-means and K-Nearest Neighbors Algorithms

Aung, Yi Yi; Min, Myat Myat

doi:10.1109/icis.2018.8466537

“…Supervised learning methods are already specified with the class labels to verify model classification or predictions. K-nearest neighbours (KNN) method is the classification method used to classify the incoming entry as normal or malicious entry [50]. Naïve Bayes is a statistical method that uses a probabilistic method based on the Bayesian theory; the probability of a field prone to attack can be calculated [51].…”

Section: Artificial Intelligence For Cyber Securitymentioning

confidence: 99%

Cyber-attacks in Cyber Physical Microgrid Systems : A comprehensive Review

Koduru¹,

Machina²,

Madichetty³

2023

Preprint

0

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Importance and need for cyber security have increased in folds since a decade. Indirectly, the country’s security depends on the country’s cyber-physical systems. Attackers are becoming more innovative, and attacks are becoming undetectable, causing huge risks to the systems. In this scenario, intelligent and evolving detection methods should be introduced to replace the basic and outworn ones. This article discusses about new-age intelligence and smart techniques dealing with artificial intelligence (AI) models. Artificial intelligence for cyber security is reviewed, and the performance of machine learning models (ML) and deep learning (DL) models are analysed.

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“…Supervised learning methods are already specified with the class labels to verify model classification or predictions. The k-nearest neighbours (KNN) method is used to classify the incoming entry as normal or malicious entry [50]. Naïve Bayes is a statistical method that uses a probabilistic method based on the Bayesian theory; the probability of a field prone to attack can be calculated [51].…”

Section: Artificial Intelligence For Cyber Securitymentioning

confidence: 99%

Cyber-attacks in Cyber-Physical Microgrid Systems: A Comprehensive Review

Koduru¹,

Machina²,

Madichetty³

2023

Preprint

3

0

View full text Add to dashboard Cite

Importance and need for cyber security have increased in folds since a decade. Indirectly, the country’s security depends on the country’s cyber-physical systems. Attackers are becoming more innovative, and attacks are becoming undetectable, causing huge risks to the systems. In this scenario, intelligent and evolving detection methods should be introduced to replace the basic and outworn ones. This article discusses about new-age intelligence and smart techniques dealing with artificial intelligence (AI) models. Artificial intelligence for cyber security is reviewed, and the performance of machine learning models (ML) and deep learning (DL) models are analysed. A real-time case study of stealthy local covert attacks with false data injection attacks is implemented on the DC-DC converter. A deep learning model is designed to mitigate cyber attacks, and its performance is evaluated.

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“…e study in [22] used Markov models for feature extraction and solved the problem that Bayesian network classifiers were usually trained on data by selecting suboptimal model heuristics, but the evaluation indicators used in their paper were not exhaustive. e work in [23] used information gain algorithm for feature extraction and solved the problem that the KDD CUP99 dataset did not include the current state of cyber attacks, while the F-measure for the unknown attack category was low. e study in [24] solved the problem that the data became more complex and it was difficult to extract better low-dimensional features effectively as the number of features increased and the accuracy of binary classification on NSL-KDD achieved 95.25%.…”

Section: Related Workmentioning

confidence: 99%

Network Intrusion Detection Model Based on Improved BYOL Self-Supervised Learning

Wang

¹

,

Li

²

,

Wang

³

et al. 2021

Security and Communication Networks

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The combination of deep learning and intrusion detection has become a hot topic in today’s network security. In the face of massive, high-dimensional network traffic with uneven sample distribution, how to be able to accurately detect anomalous traffic is the primary task of intrusion detection. Most research on intrusion detection systems based on network anomalous traffic detection has focused on supervised learning; however, the process of obtaining labeled data often requires a lot of time and effort, as well as the support of network experts. Therefore, it is worthwhile investigating the development of label-free self-supervised learning-based approaches called BYOL which is a simple and elegant framework with sufficiently powerful feature extraction capabilities for intrusion detection systems. In this paper, we propose a new data augmentation strategy for intrusion detection data and an intrusion detection model based on label-free self-supervised learning, using a new data augmentation strategy to introduce a perturbation enhancement model to learn invariant feature representation capability and an improved BYOL self-supervised learning method to train the UNSW-NB15 intrusion detection dataset without labels to extract network traffic feature representations. Linear evaluation on UNSW-NB15 and transfer learning on NSK-KDD, KDD CUP99, CIC IDS2017, and CIDDS_001 achieve excellent performance in all metrics.

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Hybrid Intrusion Detection System using K-means and K-Nearest Neighbors Algorithms

Cited by 22 publications

References 1 publication

Cyber-attacks in Cyber Physical Microgrid Systems : A comprehensive Review

Cyber-attacks in Cyber Physical Microgrid Systems : A comprehensive Review

Cyber-attacks in Cyber-Physical Microgrid Systems: A Comprehensive Review

Network Intrusion Detection Model Based on Improved BYOL Self-Supervised Learning

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