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Deep Learning for Insider Threat Detection: Review, Challenges and Opportunities
[article]
2020
arXiv
pre-print
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While the problem of insider threat detection has been studied for a long time in both security and data mining communities, the traditional machine learning based detection approaches, which heavily rely ...
In this brief survey, we first introduce one commonly-used dataset for insider threat detection and review the recent literature about deep learning for such research. ...
In Section 5, we point out research opportunities of insider threat detection based on few-shot learning, self-supervised learning, deep marked temporal point process, multi-modal learning, deep survival ...
arXiv:2005.12433v1
fatcat:bmmog7g47vfmpmzdvd4tqd5v7u
A Review of Insider Threat Detection: Classification, Machine Learning Techniques, Datasets, Open Challenges, and Recommendations
2020
Applied Sciences
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of notable recent works on insider threat detection, which covers the analyzed behaviors, machine-learning techniques, dataset, detection methodology, and evaluation metrics. ...
This phenomenon indicates that threats require special detection systems, methods, and tools, which entail the ability to facilitate accurate and fast detection of a malicious insider. ...
insider threats using statistical methods and machine-learning techniques. ...
doi:10.3390/app10155208
fatcat:xcgn37pohnaqlipqrhvwfrkgee
Enterprise data breach: causes, challenges, prevention, and future directions
2017
Wiley Interdisciplinary Reviews Data Mining and Knowledge Discovery
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This review helps interested readers to learn about enterprise data leak threats, recent data leak incidents, various state-of-the-art prevention and detection techniques, new challenges, and promising ...
Despite a plethora of research efforts on safeguarding sensitive information from being leaked, it remains an active research problem. ...
Many of these context-based approaches are based on data mining or machine learning techniques. ...
doi:10.1002/widm.1211
fatcat:reuwnplyezenfpkjniywn62dey
Guest Editorial: Special Section on Cybersecurity Techniques for Managing Networked Systems
2020
IEEE Transactions on Network and Service Management
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[item 2) in the Appendix] introduce and evaluate a system based on machine learning for supporting user-centered insider threat detection. ...
infrastructures: novel techniques for graph-based detection of botnets, performance evaluation of insider threat detection methods, characterization of malicious IoT activities based on network traffic ...
doi:10.1109/tnsm.2020.2972769
fatcat:lftjjawjb5enhmf2ohmlsqu5ja
Evaluating Insider Threat Detection Workflow Using Supervised and Unsupervised Learning
2018
2018 IEEE Security and Privacy Workshops (SPW)
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In this research, we study and evaluate an insider threat detection workflow using supervised and unsupervised learning algorithms. ...
Insider threat is a prominent cyber-security danger faced by organizations and companies. ...
In [9] , Senator et al explored machine learning-based anomaly detection for detecting insider threats in the simulated corporate computer usage activities. ...
doi:10.1109/spw.2018.00043
dblp:conf/sp/LeZ18
fatcat:tgy2k44aj5ae7exv2llsshdyf4
Machine learning in cybersecurity: A review of threat detection and defense mechanisms
2024
World Journal of Advanced Research and Reviews
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Every approach is assessed based on its suitability for threat detection, demonstrating its advantages and constraints. ...
Machine Learning (ML) has become a potent tool in strengthening cybersecurity, providing the capacity to scrutinise extensive information, recognise trends, and improve threat detection and defence methods ...
Recommendation Improving accuracy in identifying cyber threats using machine learning is an important and ongoing area of research and development. ...
doi:10.30574/wjarr.2024.21.1.0315
fatcat:3utnipxtizbx3eqqobxqvfwp6u
Security, Trust, and Privacy in Machine Learning-Based Internet of Things
2022
Security and Communication Networks
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We believe that this Special Issue can provide useful hints on how to address security, privacy, and trust issues in machine learning-based IoT environments. ...
Acknowledgments We would like to take this opportunity to thank the Chief Editor Dr. Di Pietro and all staff from Security and Communication Networks, for supporting and guiding this Special Issue. ...
on discussing the security, trust, and privacy challenges in machine learning-based IoT. e potential topics focus on the application of machine learning techniques to address security, privacy, and trust ...
doi:10.1155/2022/9851463
fatcat:pyvkvmfcarcrxmo6fjppjxv4cq
Human-in-the-Loop Intelligence: Advancing AI-Centric Cybersecurity for the Future
2023
Quing: International Journal of Multidisciplinary Scientific Research and Development
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Malware Detection and Classification: AI models can effectively detect and classify malware based on code features, behavioural patterns, and network traffic analysis (Ahmed et al., 2018; Apruzzese and ...
Enhancing AI Integration and Scalability: Research should investigate methods for seamless integration of AI into existing cybersecurity infrastructure and scalable AI solutions for enterprisewide deployment ...
The predictive analytics model identified a new type of malware based on its behaviour, even though signature-based methods had not previously detected it. ...
doi:10.54368/qijmsrd.2.3.0011
fatcat:s5rzl4h3izh7xpjqd644bdlj7a
Unsupervised User-Based Insider Threat Detection Using Bayesian Gaussian Mixture Models
[article]
2022
arXiv
pre-print
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In this paper, we propose an unsupervised insider threat detection system based on audit data using Bayesian Gaussian Mixture Models. ...
Nonetheless, the detection of such threats is challenging, precisely because of the ability of the authorized personnel to easily conduct malicious actions and because of the immense size and diversity ...
Related Work Previous work in the field of unsupervised insider threat detection based on audit data can principally be grouped into two categories: signature-based and machine learning based techniques ...
arXiv:2211.14437v1
fatcat:vjtnoxeffnejfnwvnqgsp743vi
A PREDICTIVE USER BEHAVIOUR ANALYTIC MODEL FOR INSIDER THREATS IN CYBERSPACE
2022
International Journal of Communication Networks and Information Security
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This indicated that the developed hybrid approach was able to learn from sequences of user actions in a time and frequency domain and improves the detection rate of insider threats in cyberspace. ...
Insider threat in cyberspace is a recurring problem since the user activities in a cyber network are often unpredictable. ...
In this research, a hybrid technique for the insider threat detection model using a deep learning approach to increase the detection of insider threats in cyberspace was developed. ...
doi:10.17762/ijcnis.v14i1.5208
fatcat:m2yt2mb5i5c55j35vrhnqwqbi4
A Machine Learning-based Approach for Detecting Malicious Activities in Cloud Computing Environments
2023
International Journal of Computer Engineering in Research Trends
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, this research underscores the potential of machine learning as a formidable tool in the arsenal against cyber threats in cloud computing. ...
Our approach not only demonstrates high efficacy in threat detection but also underscores the broader potential of machine learning in shaping the future of cloud security. ...
Conclusion In our comprehensive analysis of threat vectors in cloud environments, we underscored the effectiveness of a machine learning-based approach for detecting malicious activities. ...
doi:10.22362/ijcert/2023/v10/i09/v10i094
fatcat:oy7iynki6ba35mpe6g74xqhbuy
Unified Psycholinguistic Framework: An Unobtrusive Psychological Analysis Approach towards Insider Threat Prevention and Detection
2019
Journal of Information Science Theory and Practice
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The existing body of research in psycholinguistics suggests that automated text analysis of electronic communications can be an alternative for predicting and detecting insider threat through unobtrusive ...
An insider threat is a threat that comes from people within the organization being attacked. It can be described as a function of the motivation, opportunity, and capability of the insider. ...
One of the major obstacles in using the machine learning approach is the paucity of labeled data. ...
doi:10.1633/jistap.2019.7.1.5
doaj:a8958b68d1aa47a78e5faa11e58ccf9e
fatcat:h7sil4p7krc3des7hrob2u243u
Next-Generation Cyber Threat Detection and Mitigation Strategies: A Focus on Artificial Intelligence and Machine Learning
2024
Journal of Computer Science and Technology Studies
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The principal objective of this research was to examine strategies for detecting and mitigating cyber threats in the next generation, by underscoring Artificial Intelligence (AI) and Machine Learning ( ...
The results of the research ascertained that by employing Feedzai's AI-based software combined with the random forest algorithms, future financial institutions can achieve real-time fraud detection and ...
Funding: This research received no external funding.
Conflicts of Interest: The authors declare no conflict of interest. ...
doi:10.32996/jcsts.2024.6.1.19
fatcat:m7oxrc7kenarjoaghg47p5qami
Evaluating Machine Learning Classifiers for Defensive Cyber Operations
2016
Military Cyber Affairs
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Anomaly detection offers the ability to detect unknown threats, but despite over 15 years of active research, the operationalization of anomaly detection and machine learning for Defensive Cyberspace Operations ...
Today's defensive cyber sensors are dominated by signature-based analytical methods that require continuous maintenance and lack the ability to detect unknown threats. ...
to detect insider threat activities. ...
doi:10.5038/2378-0789.2.1.1005
fatcat:v275ptxky5bp7domvtjzgfcdei
New insider threat detection method based on recurrent neural networks
2020
Indonesian Journal of Electrical Engineering and Computer Science
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In this study, we propose a new conceptual method for insider threat detection on the basis of the behaviors of an insider. ...
Most organizations that implement traditional cybersecurity techniques, such as intrusion detection systems, fail to detect insider threats given the lack of extensive knowledge on insider behavior patterns ...
Figure 1 . 1 Proposed method
Figure 2 . 2 GatedNew insider threat detection method based on recurrent neural networks (Mohammed Nasser Al-Mhiqani)
's classification result on the GRU. ...
doi:10.11591/ijeecs.v17.i3.pp1474-1479
fatcat:kuyiaz2t25bbtbowfrjqt5vvs4
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