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Artificial Immune System Based Intrusion Detection: Innate Immunity using an Unsupervised Learning Approach
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TLDR
The adaptive immune system in this proposed architecture also takes advantage of the distributed structure, which has shown better self-improvement rate compare to centralized mode and provides primary and secondary immune response for unknown anomalies and zero-day attacks.Abstract:
This paper presents an intrusion detection system architecture based on the artificial immune system concept. In this architecture, an innate immune mechanism through unsupervised machine learning methods is proposed to primarily categorize network traffic to “self” and “non-self” as normal and suspicious profiles respectively. Unsupervised machine learning techniques formulate the invisible structure of unlabeled data without any prior knowledge. The novelty of this work is utilization of these methods in order to provide online and real-time training for the adaptive immune system within the artificial immune system. Different methods for unsupervised machine learning are investigated and DBSCAN (density-based spatial clustering of applications with noise) is selected to be utilized in this architecture. The adaptive immune system in our proposed architecture also takes advantage of the distributed structure, which has shown better self-improvement rate compare to centralized mode and provides primary and secondary immune response for unknown anomalies and zero-day attacks. The experimental results of proposed architecture is presented and discussed.read more
Citations
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Network Intrusion Detection for IoT Security Based on Learning Techniques
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Unsupervised Machine Learning for Networking: Techniques, Applications and Research Challenges
Muhammad Usama,Junaid Qadir,Aunn Raza,Hunain Arif,Kok-Lim Alvin Yau,Yehia Elkhatib,Amir Hussain,Ala Al-Fuqaha +7 more
TL;DR: In this article, the authors provide an overview of unsupervised learning in the domain of networking, and provide a comprehensive review of the current state of the art in this area, by synthesizing insights from previous survey papers.
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Flow-based intrusion detection: techniques and challenges
TL;DR: A taxonomy for flow-based intrusion detection systems is proposed on the basis of the technique used for detection of maliciousness in flow records to identify important research challenges for future research in the area of flow- based intrusion detection.
Proceedings ArticleDOI
A deep auto-encoder based approach for intrusion detection system
TL;DR: The proposed DAE model is trained in a greedy layer-wise fashion in order to avoid overfitting and local optima, and provides substantial improvement over other deep learning-based approaches in terms of accuracy, detection rate and false alarm rate.
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From Intrusion Detection to Attacker Attribution: A Comprehensive Survey of Unsupervised Methods
TL;DR: This paper provides a comprehensive overview of unsupervised and hybrid methods for intrusion detection, discussing their potential in the domain and descant how IDS data could be used to reconstruct and correlate attacks to identify attackers, with the use of advanced data analytics techniques.
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