Journal ArticleDOI
Adversarial attacks against intrusion detection systems: Taxonomy, solutions and open issues
TLDR
This paper will provide a general taxonomy of attack tactics against IDSs, an extensive description of how such attacks can be implemented by exploiting IDS weaknesses at different abstraction levels, and highlight the most promising research directions for the design of adversary-aware, harder-to-defeat IDS solutions.About:
This article is published in Information Sciences.The article was published on 2013-08-01. It has received 178 citations till now. The article focuses on the topics: Intrusion detection system.read more
Citations
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Proceedings ArticleDOI
Wild Patterns: Ten Years After the Rise of Adversarial Machine Learning
Battista Biggio,Fabio Roli +1 more
TL;DR: A thorough overview of the evolution of this research area over the last ten years and beyond is provided, starting from pioneering, earlier work on the security of non-deep learning algorithms up to more recent work aimed to understand the security properties of deep learning algorithms, in the context of computer vision and cybersecurity tasks.
Posted Content
Adversarial Attacks and Defences: A Survey.
TL;DR: This paper attempts to provide a detailed discussion on different types of adversarial attacks with various threat models and also elaborate the efficiency and challenges of recent countermeasures against them.
Journal ArticleDOI
A Survey on Security Threats and Defensive Techniques of Machine Learning: A Data Driven View
TL;DR: This paper revisits existing security threats and gives a systematic survey on them from two aspects, the training phase and the testing/inferring phase, and categorizes current defensive techniques of machine learning into four groups: security assessment mechanisms, countermeasures in theTraining phase, those in the testing or inferring phase; data security, and privacy.
Journal ArticleDOI
Big Data Analytics framework for Peer-to-Peer Botnet detection using Random Forests
TL;DR: The authors build up on the progress of open source tools like Hadoop, Hive and Mahout to provide a scalable implementation of quasi-real-time intrusion detection system used to detect Peer-to-Peer Botnet attacks using machine learning approach.
Journal ArticleDOI
A holistic review of Network Anomaly Detection Systems: A comprehensive survey
TL;DR: Various Decision Engine (DE) approaches are described, including new ensemble learning and deep learning approaches, and cyber kill chain models and cyber-attacks that compromise network systems are explained.
References
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Journal ArticleDOI
Anomaly detection: A survey
TL;DR: This survey tries to provide a structured and comprehensive overview of the research on anomaly detection by grouping existing techniques into different categories based on the underlying approach adopted by each technique.
Proceedings ArticleDOI
A theory of the learnable
TL;DR: This paper regards learning as the phenomenon of knowledge acquisition in the absence of explicit programming, and gives a precise methodology for studying this phenomenon from a computational viewpoint.
Proceedings Article
Hypertext Transfer Protocol -- HTTP/1.1
Roy T. Fielding,James Gettys,Jeffrey C. Mogul,H. Frystyk,Larry Masinter,Paul J. Leach,Tim Berners-Lee +6 more
TL;DR: The Hypertext Transfer Protocol is an application-level protocol for distributed, collaborative, hypermedia information systems, which can be used for many tasks beyond its use for hypertext through extension of its request methods, error codes and headers.
Proceedings Article
Snort - Lightweight Intrusion Detection for Networks
TL;DR: Snort provides a layer of defense which monitors network traffic for predefined suspicious activity or patterns, and alert system administrators when potential hostile traffic is detected.
Journal ArticleDOI
An Intrusion-Detection Model
TL;DR: A model of a real-time intrusion-detection expert system capable of detecting break-ins, penetrations, and other forms of computer abuse is described, based on the hypothesis that security violations can be detected by monitoring a system's audit records for abnormal patterns of system usage.