Journal ArticleDOI
Software Vulnerability Analysis and Discovery Using Machine-Learning and Data-Mining Techniques: A Survey
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TLDR
An extensive review of the many different works in the field of software vulnerability analysis and discovery that utilize machine-learning and data-mining techniques that utilize both advantages and shortcomings in this domain is provided.Abstract:
Software security vulnerabilities are one of the critical issues in the realm of computer security. Due to their potential high severity impacts, many different approaches have been proposed in the past decades to mitigate the damages of software vulnerabilities. Machine-learning and data-mining techniques are also among the many approaches to address this issue. In this article, we provide an extensive review of the many different works in the field of software vulnerability analysis and discovery that utilize machine-learning and data-mining techniques. We review different categories of works in this domain, discuss both advantages and shortcomings, and point out challenges and some uncharted territories in the field.read more
Citations
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Journal ArticleDOI
Software Vulnerability Detection Using Deep Neural Networks: A Survey
TL;DR: This survey reviews the current literature adopting deep-learning-/neural-network-based approaches for detecting software vulnerabilities, aiming at investigating how the state-of-the-art research leverages neural techniques for learning and understanding code semantics to facilitate vulnerability discovery.
Journal ArticleDOI
SySeVR: A Framework for Using Deep Learning to Detect Software Vulnerabilities
TL;DR: This work proposes the first systematic framework for using deep learning to detect vulnerabilities, dubbed Syntax- based, Semantics-based, and Vector Representations (SySeVR), which focuses on obtaining program representations that can accommodate syntax and semantic information pertinent to vulnerabilities.
Journal ArticleDOI
A Survey of Android Malware Detection with Deep Neural Models
TL;DR: This survey aims to address the challenges in DL-based Android malware detection and classification by systematically reviewing the latest progress, including FCN, CNN, RNN, DBN, AE, and hybrid models, and organize the literature according to the DL architecture.
Journal ArticleDOI
Survey on software defect prediction techniques
TL;DR: This work is planning to develop an efficient approach for software defect prediction by using soft computing based machine learning techniques which helps to predict optimize the features and efficiently learn the features.
Journal ArticleDOI
A taxonomy of network threats and the effect of current datasets on intrusion detection systems
Hanan Hindy,David Brosset,David Brosset,Ethan Bayne,Amar Seeam,Christos Tachtatzis,Robert Atkinson,Xavier Bellekens +7 more
TL;DR: In this paper, the authors provide researchers with two key pieces of information; a survey of prominent datasets, analyzing their use and impact on the development of the past decade's Intrusion Detection Systems (IDS) and a taxonomy of network threats and associated tools to carry out these attacks.
References
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A Survey on Transfer Learning
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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.
Journal ArticleDOI
Some studies in machine learning using the game of checkers
TL;DR: In this article, two machine learning procedures have been investigated in some detail using the game of checkers, and enough work has been done to verify the fact that a computer can be programmed so that it will lear...