2021
DOI: 10.3390/s21093005
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Infrequent Pattern Detection for Reliable Network Traffic Analysis Using Robust Evolutionary Computation

Abstract: While anomaly detection is very important in many domains, such as in cybersecurity, there are many rare anomalies or infrequent patterns in cybersecurity datasets. Detection of infrequent patterns is computationally expensive. Cybersecurity datasets consist of many features, mostly irrelevant, resulting in lower classification performance by machine learning algorithms. Hence, a feature selection (FS) approach, i.e., selecting relevant features only, is an essential preprocessing step in cybersecurity data an… Show more

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Cited by 7 publications
(1 citation statement)
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References 26 publications
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“…Another approach is to utilize Bayesian networks for scalable anomaly detection, where anomalies can arise from malicious cyber-attacks and operational faults [11]. Rashid et al discussed the concept of anomaly detection as identifying data patterns that deviate from expected behavior [12]. Jadidi et al proposed a comprehensive system-wide anomaly detection approach that utilized deep neural networks to identify anomalies and correlation analysis to investigate cyber-attacks impact interconnected devices in industrial control systems [13].…”
Section: Introductionmentioning
confidence: 99%
“…Another approach is to utilize Bayesian networks for scalable anomaly detection, where anomalies can arise from malicious cyber-attacks and operational faults [11]. Rashid et al discussed the concept of anomaly detection as identifying data patterns that deviate from expected behavior [12]. Jadidi et al proposed a comprehensive system-wide anomaly detection approach that utilized deep neural networks to identify anomalies and correlation analysis to investigate cyber-attacks impact interconnected devices in industrial control systems [13].…”
Section: Introductionmentioning
confidence: 99%