A Taxonomy for Threat Actors’ Persistence Techniques

Villalón-Huerta, Antonio; Marco-Gisbert, Héctor; Ripoll, Ismael

doi:10.1016/j.cose.2022.102855

Cited by 2 publications

(1 citation statement)

References 53 publications

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“…Unauthorized access or malicious activity carried out by threat actors [8,9] to acquire control of network resources [10], exploit vulnerabilities [11,12], or undermine system integrity are examples of network intrusion [13,14]. As cyber-attacks grow in sophistication and size, the requirement for powerful network intrusion detection systems (NIDS) to protect the integrity and security of these critical digital infrastructures has become critical [15,16].…”

Section: Introductionmentioning

confidence: 99%

A novel approach to intrusion detection using zero-shot learning hybrid partial labels

Raza,

Shaikh,

Akhtar

et al. 2024

Mehran Univ. res. j. eng. technol.

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Computer networks have become the backbone of our interconnected world in today's technologically driven landscape. Unauthorized access or malicious activity carried out by threat actors to acquire control of network resources, exploit vulnerabilities, or undermine system integrity are examples of network intrusion. ZSL(Zero-Shot Learning) is a machine learning paradigm that addresses the problem of detecting and categorizing objects or concepts that were not present in the training data. . Traditional supervised learning algorithms for intrusion detection frequently struggle with insufficient labeled data and may struggle to adapt to unexpected assault patterns. In this article We have proposed a unique zero-shot learning hybrid partial label model suited to a large image-based network intrusion dataset to overcome these difficulties. The core contribution of this study is the creation and successful implementation of a novel zero-shot learning hybrid partial label model for network intrusion detection, which has a remarkable accuracy of 99.12%. The suggested system lays the groundwork for future study into other feature selection techniques and the performance of other machine learning classifiers on larger datasets. Such research can advance the state-of-the-art in intrusion detection and improve our ability to detect and prevent the network attacks. We hope that our research will spur additional research and innovation in this critical area of cybersecurity.

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Section: Introductionmentioning

confidence: 99%

A novel approach to intrusion detection using zero-shot learning hybrid partial labels

Raza,

Shaikh,

Akhtar

et al. 2024

Mehran Univ. res. j. eng. technol.

View full text Add to dashboard Cite

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Explainable deep learning approach for advanced persistent threats (APTs) detection in cybersecurity: a review

Mutalib,

Sabri,

Wahab

et al. 2024

Artif Intell Rev

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In recent years, Advanced Persistent Threat (APT) attacks on network systems have increased through sophisticated fraud tactics. Traditional Intrusion Detection Systems (IDSs) suffer from low detection accuracy, high false-positive rates, and difficulty identifying unknown attacks such as remote-to-local (R2L) and user-to-root (U2R) attacks. This paper addresses these challenges by providing a foundational discussion of APTs and the limitations of existing detection methods. It then pivots to explore the novel integration of deep learning techniques and Explainable Artificial Intelligence (XAI) to improve APT detection. This paper aims to fill the gaps in the current research by providing a thorough analysis of how XAI methods, such as Shapley Additive Explanations (SHAP) and Local Interpretable Model-agnostic Explanations (LIME), can make black-box models more transparent and interpretable. The objective is to demonstrate the necessity of explainability in APT detection and propose solutions that enhance the trustworthiness and effectiveness of these models. It offers a critical analysis of existing approaches, highlights their strengths and limitations, and identifies open issues that require further research. This paper also suggests future research directions to combat evolving threats, paving the way for more effective and reliable cybersecurity solutions. Overall, this paper emphasizes the importance of explainability in enhancing the performance and trustworthiness of cybersecurity systems.

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A Taxonomy for Threat Actors’ Persistence Techniques

Cited by 2 publications

References 53 publications

A novel approach to intrusion detection using zero-shot learning hybrid partial labels

A novel approach to intrusion detection using zero-shot learning hybrid partial labels

Explainable deep learning approach for advanced persistent threats (APTs) detection in cybersecurity: a review

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