Journal ArticleDOI
Towards an Energy-Efficient Anomaly-Based Intrusion Detection Engine for Embedded Systems
Eduardo Viegas,Altair Olivo Santin,André Luiz Pereira de França,Ricardo P. Jasinski,Volnei A. Pedroni,Luiz S. Oliveira +5 more
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TLDR
It is demonstrated that a hardware (HW) implementation of network security algorithms can significantly reduce their energy consumption compared to an equivalent software (SW) version.Abstract:
Nowadays, a significant part of all network accesses comes from embedded and battery-powered devices, which must be energy efficient. This paper demonstrates that a hardware (HW) implementation of network security algorithms can significantly reduce their energy consumption compared to an equivalent software (SW) version. The paper has four main contributions: (i) a new feature extraction algorithm, with low processing demands and suitable for hardware implementation; (ii) a feature selection method with two objectives—accuracy and energy consumption; (iii) detailed energy measurements of the feature extraction engine and three machine learning (ML) classifiers implemented in SW and HW—Decision Tree (DT), Naive-Bayes (NB), and k-Nearest Neighbors (kNN); and (iv) a detailed analysis of the tradeoffs in implementing the feature extractor and ML classifiers in SW and HW. The new feature extractor demands significantly less computational power, memory, and energy. Its SW implementation consumes only 22 percent of the energy used by a commercial product and its HW implementation only 12 percent. The dual-objective feature selection enabled an energy saving of up to 93 percent. Comparing the most energy-efficient SW implementation (new extractor and DT classifier) with an equivalent HW implementation, the HW version consumes only 5.7 percent of the energy used by the SW version.read more
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Journal ArticleDOI
Machine Learning and Deep Learning Methods for Cybersecurity
Yang Xin,Lingshuang Kong,Liu Zhi,Yuling Chen,Yanmiao Li,Hongliang Zhu,Mingcheng Gao,Haixia Hou,Chunhua Wang +8 more
TL;DR: This survey report describes key literature surveys on machine learning (ML) and deep learning (DL) methods for network analysis of intrusion detection and provides a brief tutorial description of each ML/DL method.
Journal ArticleDOI
Cybersecurity data science: an overview from machine learning perspective
Iqbal H. Sarker,Iqbal H. Sarker,A. S. M. Kayes,Shahriar Badsha,Hamed Alqahtani,Paul A. Watters,Alex Hay-Man Ng +6 more
TL;DR: This paper focuses and briefly discusses on cybersecurity data science, where the data is being gathered from relevant cybersecurity sources, and the analytics complement the latest data-driven patterns for providing more effective security solutions.
Data preprocessing for anomaly based network intrusion detection : a review
Jonathan J. Davis,Andrew Clark +1 more
TL;DR: The review finds that many NIDS limit their view of network traffic to the TCP/IP packet headers, and shows a trend toward deeper packet inspection to construct more relevant features through targeted content parsing.
Journal ArticleDOI
A survey on Intrusion Detection Systems and Honeypot based proactive security mechanisms in VANETs and VANET Cloud
TL;DR: A proactive bait based Honeypot optimized IDS system is also proposed with the aim to detect existing and zero-day attacks with minimal overhead and to bridge the research gaps in terms of performance, detection rate and overhead.
Journal ArticleDOI
A Deep Learning Method With Filter Based Feature Engineering for Wireless Intrusion Detection System
Sydney Mambwe Kasongo,Yanxia Sun +1 more
TL;DR: This paper proposes a IDS based on deep learning using feed forward deep neural networks (FFDNNs) coupled with a filter-based feature selection algorithm and proves that the FFDNN-IDS achieves an increase in accuracy in comparison to other methods.
References
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Book
Genetic algorithms in search, optimization, and machine learning
TL;DR: In this article, the authors present the computer techniques, mathematical tools, and research results that will enable both students and practitioners to apply genetic algorithms to problems in many fields, including computer programming and mathematics.
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Genetic algorithms in search, optimization and machine learning
TL;DR: This book brings together the computer techniques, mathematical tools, and research results that will enable both students and practitioners to apply genetic algorithms to problems in many fields.
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Irrelevant features and the subset selection problem
TL;DR: A method for feature subset selection using cross-validation that is applicable to any induction algorithm is described, and experiments conducted with ID3 and C4.5 on artificial and real datasets are discussed.
Proceedings ArticleDOI
Outside the Closed World: On Using Machine Learning for Network Intrusion Detection
Robin Sommer,Vern Paxson +1 more
TL;DR: The main claim is that the task of finding attacks is fundamentally different from these other applications, making it significantly harder for the intrusion detection community to employ machine learning effectively.
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