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Papers on “cybersecurity intrusion threat detection machine learning”

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1. Survey of intrusion detection systems: techniques, datasets and challenges

   Ansam Khraisat, Iqbal Gondal, Peter Vamplew, et al. · 2019 · Cybersecurity · 1,950 cites

   Cyber-attacks are becoming more sophisticated and thereby presenting increasing challenges in accurately detecting intrusions. Failure to prevent the intrusions could degrade the credibility of security services, e.g. data confidentiality, integrity, and availability. Numerous intrusion detection methods have been proposed in the literature to tackle computer security threats, which can be broadly classified into Signature-based Intrusion Detection Systems (SIDS) and Anomaly-based Intrusion Detection Systems (AIDS). This survey paper presents a taxonomy of contemporary IDS, a comprehensive review of notable recent works, and an overview of the datasets commonly used for evaluation purposes.

2. Deep Learning Approach for Intelligent Intrusion Detection System

   R. Vinayakumar, Mamoun Alazab, K. P. Soman, et al. · 2019 · IEEE Access · 1,893 cites

   Machine learning techniques are being widely used to develop an intrusion detection system (IDS) for detecting and classifying cyberattacks at the network-level and the host-level in a timely and automatic manner. However, many challenges arise since malicious attacks are continually changing and are occurring in very large volumes requiring a scalable solution. There are different malware datasets available publicly for further research by cyber security community. However, no existing study has shown the detailed analysis of the performance of various machine learning algorithms on various publicly available datasets. Due to the dynamic nature of malware with continuously changing attackin

3. Network intrusion detection system: A systematic study of machine learning and deep learning approaches

   Zeeshan Ahmad, Adnan Shahid Khan, Cheah Wai Shiang, et al. · 2020 · Transactions on Emerging Telecommunications Technologies · 1,275 cites

   Abstract The rapid advances in the internet and communication fields have resulted in a huge increase in the network size and the corresponding data. As a result, many novel attacks are being generated and have posed challenges for network security to accurately detect intrusions. Furthermore, the presence of the intruders with the aim to launch various attacks within the network cannot be ignored. An intrusion detection system (IDS) is one such tool that prevents the network from possible intrusions by inspecting the network traffic, to ensure its confidentiality, integrity, and availability. Despite enormous efforts by the researchers, IDS still faces challenges in improving detection accu

4. Machine Learning and Deep Learning Methods for Intrusion Detection Systems: A Survey

   Hongyu Liu, Bo Lang · 2019 · Applied Sciences · 1,102 cites

   Networks play important roles in modern life, and cyber security has become a vital research area. An intrusion detection system (IDS) which is an important cyber security technique, monitors the state of software and hardware running in the network. Despite decades of development, existing IDSs still face challenges in improving the detection accuracy, reducing the false alarm rate and detecting unknown attacks. To solve the above problems, many researchers have focused on developing IDSs that capitalize on machine learning methods. Machine learning methods can automatically discover the essential differences between normal data and abnormal data with high accuracy. In addition, machine lea

5. A survey of emerging threats in cybersecurity

   Julian Jang‐Jaccard, ‪Surya Nepal‬ · 2014 · Journal of Computer and System Sciences · 893 cites

6. Cybersecurity data science: an overview from machine learning perspective

   Iqbal H. Sarker, A. S. M. Kayes, Shahriar Badsha, et al. · 2020 · Journal Of Big Data · 748 cites

   Abstract In a computing context, cybersecurity is undergoing massive shifts in technology and its operations in recent days, and data science is driving the change. Extracting security incident patterns or insights from cybersecurity data and building corresponding data-driven model , is the key to make a security system automated and intelligent. To understand and analyze the actual phenomena with data, various scientific methods, machine learning techniques, processes, and systems are used, which is commonly known as data science. In this paper, we focus and briefly discuss on cybersecurity data science , where the data is being gathered from relevant cybersecurity sources, and the analyti

7. A Survey on Security Threats and Defensive Techniques of Machine Learning: A Data Driven View

   Qiang Liu, Pan Li, Wentao Zhao, et al. · 2018 · IEEE Access · 428 cites

   Machine learning is one of the most prevailing techniques in computer science, and it has been widely applied in image processing, natural language processing, pattern recognition, cybersecurity, and other fields. Regardless of successful applications of machine learning algorithms in many scenarios, e.g., facial recognition, malware detection, automatic driving, and intrusion detection, these algorithms and corresponding training data are vulnerable to a variety of security threats, inducing a significant performance decrease. Hence, it is vital to call for further attention regarding security threats and corresponding defensive techniques of machine learning, which motivates a comprehensiv

8. Chained Anomaly Detection Models for Federated Learning: An Intrusion Detection Case Study

   Davy Preuveneers, Vera Rimmer, Ilias Tsingenopoulos, et al. · 2018 · Applied Sciences · 321 cites

   The adoption of machine learning and deep learning is on the rise in the cybersecurity domain where these AI methods help strengthen traditional system monitoring and threat detection solutions. However, adversaries too are becoming more effective in concealing malicious behavior amongst large amounts of benign behavior data. To address the increasing time-to-detection of these stealthy attacks, interconnected and federated learning systems can improve the detection of malicious behavior by joining forces and pooling together monitoring data. The major challenge that we address in this work is that in a federated learning setup, an adversary has many more opportunities to poison one of the l