A Detailed Investigation and Analysis of Using Machine Learning Techniques for Intrusion Detection

By Preeti Mishra, V. Varadharajan, U. Tupakula, Emmanuel S. PIlli. IEEE Communications Surveys and Tutorials, 2019

613 citations recorded by the source corpus.

Intrusion detection is one of the important security problems in todays cyber world. A significant number of techniques have been developed which are based on machine learning approaches. However, they are not very successful in identifying all types of intrusions. In this paper, a detailed investigation and analysis of various machine learning techniques have been carried out for finding the cause of problems associated with various machine learning techniques in detecting intrusive activities. Attack classification and mapping of the attack features is provided corresponding to each attack. Issues which are related to detecting low-frequency attacks using network attack dataset are also discussed and viable methods are suggested for improvement. Machine learning techniques have been analyzed and compared in terms of their detection capability for detecting the various category of attacks. Limitations associated with each category of them are also discussed. Various data mining tools for machine learning have also been included in the paper. At the end, future directions are provided for attack detection using machine learning techniques.

Subfields: Artificial Intelligence, Computer Networks and Communications, Signal Processing

View Paper: https://www.semanticscholar.org/paper/d294d5246e0dd8ed8bd9ec9d24a01fd4ece4fb3c

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Citations in the corpus

Deep Learning Approach for Intelligent Intrusion Detection System (2019, background): “Based on intrusive behaviors, intrusion detection is classiﬁed into network-based intrusion detection system (NIDS) and host-based intrusion detection system (HIDS) [5].”
Deep Learning in Mobile and Wireless Networking: A Survey (2018, background): “[74] A survey machine learning driven intrusion detection.”
Network Intrusion Detection for IoT Security Based on Learning Techniques (2019, methodology): “Surveys about IDS based on learning techniques (not specially for IoT) such as [13], [15], and [24]–[27] have performed comprehensive evaluation for traditional systems.”
A Review of Intrusion Detection Systems Using Machine and Deep Learning in Internet of Things: Challenges, Solutions and Future Directions (2020, background): “There are other studies [35,36], which discussed the feasibility of both ML and data mining techniques to detect intrusions in IoT networks by implementing these techniques in IDSs either through detecting anomalies or classification of traffic.”
Explainable Artificial Intelligence Applications in Cyber Security: State-of-the-Art in Research (2022, neutral): “6 [18] 2019 N/A N/A √ ≈ N/A N/A √ √ ≈ N/A √”
Building an efficient intrusion detection system based on feature selection and ensemble classifier (2019, background): “t for IDS has also been brought forward on the other hand.Hence,manyapproacheshavebeenresearchedanddeveloped to improve the detection rate and performance of IDSs; one of them is Machine learning (ML)[4], which can be applied for both anomaly and misuse detection models. By analyzing network traffic”
A Survey of Machine and Deep Learning Methods for Internet of Things (IoT) Security (2018, background): “Other survey papers such as [24], [25] focused on the uses of data mining and machine learning methods for cybersecurity to”