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
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TLDR
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.Abstract:
With the development of the Internet, cyber-attacks are changing rapidly and the cyber security situation is not optimistic. 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. Papers representing each method were indexed, read, and summarized based on their temporal or thermal correlations. Because data are so important in ML/DL methods, we describe some of the commonly used network datasets used in ML/DL, discuss the challenges of using ML/DL for cybersecurity and provide suggestions for research directions.read more
Citations
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Journal ArticleDOI
Machine learning
TL;DR: Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis.
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State-of-the-art in artificial neural network applications: A survey
Oludare Isaac Abiodun,Oludare Isaac Abiodun,Aman Jantan,Abiodun Esther Omolara,Kemi Victoria Dada,Nachaat AbdElatif Mohamed,Humaira Arshad +6 more
TL;DR: The study found that neural-network models such as feedforward and feedback propagation artificial neural networks are performing better in its application to human problems and proposed feedforwardand feedback propagation ANN models for research focus based on data analysis factors like accuracy, processing speed, latency, fault tolerance, volume, scalability, convergence, and performance.
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Applications of Deep Reinforcement Learning in Communications and Networking: A Survey
Nguyen Cong Luong,Dinh Thai Hoang,Shimin Gong,Dusit Niyato,Ping Wang,Ying-Chang Liang,Dong In Kim +6 more
TL;DR: This paper presents a comprehensive literature review on applications of deep reinforcement learning (DRL) in communications and networking, and presents applications of DRL for traffic routing, resource sharing, and data collection.
Journal ArticleDOI
Deep Learning Approach for Intelligent Intrusion Detection System
R. Vinayakumar,Mamoun Alazab,K. P. Soman,Prabaharan Poornachandran,Ameer Al-Nemrat,Sitalakshmi Venkatraman +5 more
TL;DR: A highly scalable and hybrid DNNs framework called scale-hybrid-IDS-AlertNet is proposed which can be used in real-time to effectively monitor the network traffic and host-level events to proactively alert possible cyberattacks.
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Machine Learning: Algorithms, Real-World Applications and Research Directions
TL;DR: In this paper, the authors present a comprehensive view on these machine learning algorithms that can be applied to enhance the intelligence and the capabilities of an application and highlight the challenges and potential research directions based on their study.
References
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Deep learning
TL;DR: Deep learning is making major advances in solving problems that have resisted the best attempts of the artificial intelligence community for many years, and will have many more successes in the near future because it requires very little engineering by hand and can easily take advantage of increases in the amount of available computation and data.
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Yann LeCun,Léon Bottou,Léon Bottou,Yoshua Bengio,Yoshua Bengio,Yoshua Bengio,Patrick Haffner +6 more
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