Anomaly Detection on Encrypted and High-Performance Data Networks by Means of Machine Learning Techniques

With advancements in engineering and science, the application of smart systems is increasing, generating a faster growth of the IoT network traffic. The limitations due to IoT restricted power and computing devices also raise concerns about security vulnerabilities. Machine learning-based techniques have recently gained credibility in a successful application for the detection of network anomalies, including IoT networks. However, machine learning techniques cannot work without representative data. Given the scarcity of IoT datasets, the DAD emerged as an instrument for knowing the behavior of dedicated IoT-MQTT networks. This paper aims to validate the DAD dataset by applying Logistic Regression, Naive Bayes, Random Forest, AdaBoost, and Support Vector Machine to detect traffic anomalies in IoT. To obtain the best results, techniques for handling unbalanced data, feature selection, and grid search for hyperparameter optimization have been used. The experimental results show that the proposed dataset can achieve a high detection rate in all the experiments, providing the best mean accuracy of 0.99 for the tree-based models, with a low false-positive rate, ensuring effective anomaly detection.

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A Review of Machine Learning Techniques for Anomaly Detection in Static Graphs

Implementing Computational Intelligence Techniques for Security Systems Design - Advances in Computational Intelligence and Robotics ◽

10.4018/978-1-7998-2418-3.ch007 ◽

2020 ◽

pp. 146-162

Author(s):

Hesham M. Al-Ammal

Keyword(s):

Machine Learning ◽

Neural Networks ◽

Anomaly Detection ◽

Real Life ◽

Machine Learning Techniques ◽

Support Vector ◽

Learning Methods ◽

Data Set ◽

Learning Techniques ◽

Vector Machines

Detection of anomalies in a given data set is a vital step in several applications in cybersecurity; including intrusion detection, fraud, and social network analysis. Many of these techniques detect anomalies by examining graph-based data. Analyzing graphs makes it possible to capture relationships, communities, as well as anomalies. The advantage of using graphs is that many real-life situations can be easily modeled by a graph that captures their structure and inter-dependencies. Although anomaly detection in graphs dates back to the 1990s, recent advances in research utilized machine learning methods for anomaly detection over graphs. This chapter will concentrate on static graphs (both labeled and unlabeled), and the chapter summarizes some of these recent studies in machine learning for anomaly detection in graphs. This includes methods such as support vector machines, neural networks, generative neural networks, and deep learning methods. The chapter will reflect the success and challenges of using these methods in the context of graph-based anomaly detection.

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Anomaly Detection on ADS-B Flight Data Using Machine Learning Techniques

10.1007/978-3-030-88081-1_58 ◽

2021 ◽

pp. 771-783

Author(s):

Osman Taşdelen ◽

Levent Çarkacioglu ◽

Behçet Uğur Töreyin

Keyword(s):

Machine Learning ◽

Anomaly Detection ◽

Machine Learning Techniques ◽

Flight Data ◽

Learning Techniques

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Anomaly Detection in Drone-Captured Images Using Machine Learning Techniques and Deep Learning Architectures

10.1007/978-981-16-4625-6_78 ◽

2021 ◽

pp. 783-791

Author(s):

Kartik Joshi ◽

G. Vidya ◽

Soumya Shaw ◽

Abitha K. Thyagarajan ◽

Akhil Pathak ◽

...

Keyword(s):

Machine Learning ◽

Deep Learning ◽

Anomaly Detection ◽

Machine Learning Techniques ◽

Learning Techniques ◽

Learning Architectures

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An Approach for Anomaly Detection in GPR Data using Machine Learning Techniques

10.3997/2214-4609.2020geo082 ◽

2020 ◽

Author(s):

M. Ozkan-Okay ◽

R. Samet ◽

S. Stirenko

Keyword(s):

Machine Learning ◽

Anomaly Detection ◽

Machine Learning Techniques ◽

Learning Techniques

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Malicious web domain identification using online credibility and performance data by considering the class imbalance issue

Industrial Management & Data Systems ◽

10.1108/imds-02-2018-0072 ◽

2019 ◽

Vol 119 (3) ◽

pp. 676-696 ◽

Cited By ~ 5

Author(s):

Zhongyi Hu ◽

Raymond Chiong ◽

Ilung Pranata ◽

Yukun Bao ◽

Yuqing Lin

Keyword(s):

Machine Learning ◽

Class Imbalance ◽

Performance Data ◽

Machine Learning Techniques ◽

Data Sets ◽

Real World Data ◽

Content Type ◽

Domain Identification ◽

Learning Techniques ◽

And Performance

Purpose Malicious web domain identification is of significant importance to the security protection of internet users. With online credibility and performance data, the purpose of this paper to investigate the use of machine learning techniques for malicious web domain identification by considering the class imbalance issue (i.e. there are more benign web domains than malicious ones). Design/methodology/approach The authors propose an integrated resampling approach to handle class imbalance by combining the synthetic minority oversampling technique (SMOTE) and particle swarm optimisation (PSO), a population-based meta-heuristic algorithm. The authors use the SMOTE for oversampling and PSO for undersampling. Findings By applying eight well-known machine learning classifiers, the proposed integrated resampling approach is comprehensively examined using several imbalanced web domain data sets with different imbalance ratios. Compared to five other well-known resampling approaches, experimental results confirm that the proposed approach is highly effective. Practical implications This study not only inspires the practical use of online credibility and performance data for identifying malicious web domains but also provides an effective resampling approach for handling the class imbalance issue in the area of malicious web domain identification. Originality/value Online credibility and performance data are applied to build malicious web domain identification models using machine learning techniques. An integrated resampling approach is proposed to address the class imbalance issue. The performance of the proposed approach is confirmed based on real-world data sets with different imbalance ratios.

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