Explainable intrusion detection systems (x-ids): A survey of current methods, challenges, and opportunities

S Neupane, J Ables, W Anderson, S Mittal… - IEEE …, 2022 - ieeexplore.ieee.org
The application of Artificial Intelligence (AI) and Machine Learning (ML) to cybersecurity
challenges has gained traction in industry and academia, partially as a result of widespread …

Automated machine learning for deep learning based malware detection

A Brown, M Gupta, M Abdelsalam - Computers & Security, 2024 - Elsevier
Deep learning (DL) has proven to be effective in detecting sophisticated malware that is
constantly evolving. Even though deep learning has alleviated the feature engineering …

A survey on adversarial attacks for malware analysis

K Aryal, M Gupta, M Abdelsalam, P Kunwar… - IEEE …, 2024 - ieeexplore.ieee.org
Machine learning-based malware analysis approaches are widely researched and
deployed in critical infrastructures for detecting and classifying evasive and growing …

Creating cybersecurity knowledge graphs from malware after action reports

A Piplai, S Mittal, A Joshi, T Finin, J Holt, R Zak - IEEE Access, 2020 - ieeexplore.ieee.org
After Action Reports (AARs) provide incisive analysis of cyber-incidents. Extracting cyber-
knowledge from these sources would provide security analysts with credible information …

RWArmor: a static-informed dynamic analysis approach for early detection of cryptographic windows ransomware

MA Ayub, A Siraj, B Filar, M Gupta - International Journal of Information …, 2024 - Springer
Ransomware attacks have captured news headlines worldwide for the last few years due to
their criticality and intensity. Ransomware-as-a-service (RaaS) kits are aiding adversaries to …

Recurrent neural networks based online behavioural malware detection techniques for cloud infrastructure

JC Kimmel, AD Mcdole, M Abdelsalam, M Gupta… - IEEE …, 2021 - ieeexplore.ieee.org
Several organizations are utilizing cloud technologies and resources to run a range of
applications. These services help businesses save on hardware management, scalability …

Analyzing machine learning approaches for online malware detection in cloud

JC Kimmell, M Abdelsalam… - 2021 IEEE International …, 2021 - ieeexplore.ieee.org
The variety of services and functionality offered by various cloud service providers (CSP)
have exploded lately. Utilizing such services has created numerous opportunities for …

Analyzing and explaining black-box models for online malware detection

H Manthena, JC Kimmel, M Abdelsalam… - IEEE Access, 2023 - ieeexplore.ieee.org
In recent years, a significant amount of research has focused on analyzing the effectiveness
of machine learning (ML) models for malware detection. These approaches have ranged …

Explainable Malware Analysis: Concepts, Approaches and Challenges

H Manthena, S Shajarian, J Kimmell… - arxiv preprint arxiv …, 2024 - arxiv.org
Machine learning (ML) has seen exponential growth in recent years, finding applications in
various domains such as finance, medicine, and cybersecurity. Malware remains a …

Creating an explainable intrusion detection system using self organizing maps

J Ables, T Kirby, W Anderson, S Mittal… - 2022 IEEE …, 2022 - ieeexplore.ieee.org
Modern Artificial Intelligence (AI) enabled Intrusion Detection Systems (IDS) are complex
black boxes. This means that a security analyst will have little to no explanation or …