Machine Learning Techniques for Network Anomaly Detection: A Survey

Eltanbouly, Sohaila; Bashendy, May; Al-Naimi, Noora; Chkirbene, Zina; Erbad, Aiman

doi:10.1109/iciot48696.2020.9089465

Cited by 50 publications

(17 citation statements)

References 22 publications

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“…There are different algorithms proposed in the literature for OCL that address distance, clustering, density, ensembles, and learning-based methods divided into Active Learning (Pimentel et al, 2018), graphs (Eltanbouly et al, 2020;Akoglu et al, 2015), and Deep Learning (Chalapathy & Chawla, 2019;Manevitz and Yousef 2007). However, there are still few studies that apply OCL for text classification (Alam et al, 2020;Khan & Madden, 2014;Perera et al, 2021;Wang et al, 2019).…”

Section: One-class Learningmentioning

confidence: 99%

A network-based positive and unlabeled learning approach for fake news detection

Souza¹,

Nogueira²,

Rossi³

et al. 2021

Mach Learn

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Section: One-class Learningmentioning

confidence: 99%

A network-based positive and unlabeled learning approach for fake news detection

Souza¹,

Nogueira²,

Rossi³

et al. 2021

Mach Learn

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“…In our approach, we used three machine learning algorithms, namely One-Class Support Vector Machines (OCSVMs), Local Outlier Factor (LOFs), and Autoencoders (AEs). These algorithms were selected as they represent a set of frequently used unsupervised machine learning algorithms for anomaly detection in the recent literature [39][40][41][42][43]. Since these algorithms are unsupervised machine learning algorithms, they do not require any labeled data to train these algorithms.…”

Section: Related Workmentioning

confidence: 99%

Data-Driven Correlation of Cyber and Physical Anomalies for Holistic System Health Monitoring

Marino¹,

Wickramasinghe

Tsouvalas

et al. 2021

IEEE Access

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Concerns of cyber-security threats are increasingly becoming a part of everyday operations of cyber-physical systems, especially in the context of critical infrastructures. However, despite the tight integration of cyber and physical components in modern critical infrastructures, the monitoring of cyber and physical subsystems is still done separately. For successful health monitoring of such systems, a holistic approach is needed. In this paper, we present an approach for holistic health monitoring of cyber-physical systems based on cyber and physical anomaly detection and correlation. We provide a data-driven approach for the detection of cyber and physical anomalies based on machine learning. The benefits of the presented approach are: 1) integrated architecture that supports acquisition and real-time analysis of both cyber and physical data; 2) a metric for holistic health monitoring that allows for differentiation between physical faults, cyber intrusion, and cyber-physical attacks. We present experimental analysis on a power-grid use case using the IEEE-33 bus model. The system was tested on several types of attacks such as network scan, Denial of Service (DOS), and malicious command injections.

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“…Akoglu et al [24], Ranshous et al [45], and Jennifer and Kumar [46] put their concentration on graph anomaly detection and reviewed many conventional approaches in this area including statistical models and machine learning techniques. Other surveys (e.g., [3], [5], [17], and [47]- [51].) were dedicated to particular applications of graph anomaly detection, such as computer network intrusion detection and anomaly detection in online social networks.…”

Section: Existing Anomaly Detection Surveysmentioning

confidence: 99%

A Comprehensive Survey on Graph Anomaly Detection with Deep Learning

Ma,

Wu,

Xue

et al. 2021

Preprint

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Anomalies represent rare observations (e.g., data records, messages or events) that are deviating significantly from others. Over the last forty years, researches on anomalies have received intensified interests and the burst of information has attracted more attention on anomalies because of their significance in a wide range of disciplines (e.g., computer science, chemistry, and biology). Anomaly detection, which aims to identify these rare observations, is among the most vital tasks and has shown its power in preventing detrimental events, such as financial fraud, network intrusion, and social spam, from happening. The detection task is typically solved by detecting outlying data points in the features space and inherently overlooks the structural information in real-world data. Graphs have been prevalently used to preserve the structural information, and this raises the graph anomaly detection problem -identifying anomalous graph objects (i.e., nodes, edges and sub-graphs). However, conventional anomaly detection techniques cannot well solve this problem because of the complexity of graph data (e.g., irregular structures, non-independent and large-scale). For the aptitudes of deep learning in breaking these limitations, graph anomaly detection with deep learning has received intensified studies recently. In this survey, we aim to provide a systematic and comprehensive review of the contemporary deep learning techniques for graph anomaly detection. Specifically, our categorization follows a task-driven strategy and classifies existing works according to the anomalous graph objects they can detect. We especially focus on the motivations, key intuitions and technical details of existing works. Moreover, we summarize open-sourced implementations, public datasets, and commonly-used evaluation metrics to provide affluent resources for future studies. Finally, we highlight twelve extensive future research directions according to our survey results covering emerging problems introduced by graph data, anomaly detection and real applications.

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Machine Learning Techniques for Network Anomaly Detection: A Survey

Cited by 50 publications

References 22 publications

A network-based positive and unlabeled learning approach for fake news detection

A network-based positive and unlabeled learning approach for fake news detection

Data-Driven Correlation of Cyber and Physical Anomalies for Holistic System Health Monitoring

A Comprehensive Survey on Graph Anomaly Detection with Deep Learning

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