Insider Threat Detection using Deep Learning: A Review

Raut, Madhu; Dhavale, Sunita Vikrant; Singh, Amarjit; Mehra, Atul

doi:10.1109/iciss49785.2020.9315932

Cited by 11 publications

(12 citation statements)

References 9 publications

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“…Others do not focus on threat hunting [24]. Some of existing surveys fail to develop a future road map [25] [26]. Most of them do not discuss the role of AI [27].…”

Section: Existing Surveysmentioning

confidence: 99%

“…Multiple studies have initiated the partitioning of resources for threat hunting and mitigating is a somewhat new sector for businesses to consider [26]. The authors of [25] developed a deep learning model systems that will allow for constant moderation and quick detection of potential threats in their systems. Deep learning tools will assist in managing the rapid technological development and the consistently growing attack surfaces that create challenges for modern security frameworks [26].…”

Section: Surveys On Threat Managementmentioning

confidence: 99%

“…The authors of [25] have recognized the daunting task of leading Insider Threat Detection for organizations. It was understood that many malicious activities can go undetected with the different privileges associated with users, and the rate that digital footprints can get lost in large networks [25]. These researchers have compiled datasets to provide a clear understanding of Insider Threat Detection in Deep learning [25].…”

Section: Surveys On Ai-assisted Threat Managementmentioning

confidence: 99%

“…It was understood that many malicious activities can go undetected with the different privileges associated with users, and the rate that digital footprints can get lost in large networks [25]. These researchers have compiled datasets to provide a clear understanding of Insider Threat Detection in Deep learning [25]. In [25] it was discovered that Deep Learning solutions have the ability to enhance the capabilities of Log based Anomaly Detection when processing large sets of data.…”

Section: Surveys On Ai-assisted Threat Managementmentioning

confidence: 99%

See 3 more Smart Citations

Turning the Hunted into the Hunter via Threat Hunting: Life Cycle, Ecosystem, Challenges and the Great Promise of AI

Hillier¹,

Karroubi²

2022

Preprint

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The threat hunting lifecycle is a complex atmosphere that requires special attention from professionals to maintain security. This paper is a collection of recent work that gives a holistic view of the threat hunting ecosystem, identifies challenges, and discusses the future with the integration of artificial intelligence (AI). We specifically establish a life cycle and ecosystem for privacy-threat hunting in addition to identifying the related challenges. We also discovered how critical the use of AI is in threat hunting. This work paves the way for future work in this area as it provides the foundational knowledge to make meaningful advancements for threat hunting.

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“…Others do not focus on threat hunting [24]. Some of existing surveys fail to develop a future road map [25] [26]. Most of them do not discuss the role of AI [27].…”

Section: Existing Surveysmentioning

confidence: 99%

Section: Surveys On Threat Managementmentioning

confidence: 99%

Section: Surveys On Ai-assisted Threat Managementmentioning

confidence: 99%

Section: Surveys On Ai-assisted Threat Managementmentioning

confidence: 99%

See 2 more Smart Citations

Turning the Hunted into the Hunter via Threat Hunting: Life Cycle, Ecosystem, Challenges and the Great Promise of AI

Hillier¹,

Karroubi²

2022

Preprint

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show abstract

“…For this purpose, some ML-jobs have been integrated into the ELK. On other hand, [23] provide a survey on intrusion detection using Deep Learning technique and how a Deep Learning model could be integrated into the ELK Stack.…”

Section: Elk As a Siemmentioning

confidence: 99%

Importance of Machine Learning Techniques to Improve the Open Source Intrusion Detection Systems

Vadhil

Nanne

Salihi

2021

IJEEI

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Nowadays, it became difficult to ensure data security because of the rapid development of information technology according to the Vs of Big Data. To secure a network against malicious activities and to ensure data protection, an intrusion detection system played a very important role. The main objective was to obtain a high-performance solution capable of detecting different types of attacks around the system. The main aim of this paper is to study the lacks of traditional and open source Intrusion Detection Systems and the Machine Learning techniques commonly used to overcome these lacks. A comparison of some existing works by Intrusion Detection System type, detection method, algorithm and accuracy was provided.

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Insider Threat Defense Strategies: Survey and Knowledge Integration

Song,

Zhang,

et al. 2024

Lecture Notes in Computer Science

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Insider Threat Detection using Deep Learning: A Review

Cited by 11 publications

References 9 publications

Turning the Hunted into the Hunter via Threat Hunting: Life Cycle, Ecosystem, Challenges and the Great Promise of AI

Turning the Hunted into the Hunter via Threat Hunting: Life Cycle, Ecosystem, Challenges and the Great Promise of AI

Importance of Machine Learning Techniques to Improve the Open Source Intrusion Detection Systems

Insider Threat Defense Strategies: Survey and Knowledge Integration

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