A Comprehensive Analysis of Explainable AI for Malware Hunting

M Saqib, S Mahdavifar, BCM Fung… - ACM Computing …, 2024 - dl.acm.org
In the past decade, the number of malware variants has increased rapidly. Many
researchers have proposed to detect malware using intelligent techniques, such as Machine …

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 …

FINER: Enhancing State-of-the-art Classifiers with Feature Attribution to Facilitate Security Analysis

Y He, J Lou, Z Qin, K Ren - Proceedings of the 2023 ACM SIGSAC …, 2023 - dl.acm.org
Deep learning classifiers achieve state-of-the-art performance in various risk detection
applications. They explore rich semantic representations and are supposed to automatically …

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 …

GAGE: Genetic algorithm-based graph explainer for malware analysis

M Saqib, BCM Fung, P Charland… - 2024 IEEE 40th …, 2024 - ieeexplore.ieee.org
Malware analysts often prefer reverse engineering using Call Graphs, Control Flow Graphs
(CFGs), and Data Flow Graphs (DFGs), which involves the utilization of black-box Deep …

Position: The Explainability Paradox-Challenges for XAI in Malware Detection and Analysis

L Rui, O Gadyatskaya - 2024 IEEE European Symposium on …, 2024 - ieeexplore.ieee.org
Malware poses a significant threat to global cy-bersecurity, with machine learning emerging
as the primary method for its detection and analysis. However, the opaque nature of …

Explainable Deep Learning Models for Dynamic and Online Malware Classification

Q Card, D Simpson, K Aryal, M Gupta… - arxiv preprint arxiv …, 2024 - arxiv.org
In recent years, there has been a significant surge in malware attacks, necessitating more
advanced preventive measures and remedial strategies. While several successful AI-based …

ML-FEED: Machine Learning Framework for Efficient Exploit Detection

T Saha, T Al Rahat, N Aaraj, Y Tian… - 2022 IEEE 4th …, 2022 - ieeexplore.ieee.org
Machine learning (ML)-based methods have recently become attractive for detecting
security vulnerability exploits. Unfortunately, state-of-the-art ML models like long short-term …

An analysis of android malware classification services

M Rashed, G Suarez-Tangil - Sensors, 2021 - mdpi.com
The increasing number of Android malware forced antivirus (AV) companies to rely on
automated classification techniques to determine the family and class of suspicious …

Malware Detection Tool Based on Emulator State Analysis

P Rehida, O Savenko, A Kashtalian… - 2023 IEEE 12th …, 2023 - ieeexplore.ieee.org
This work is devoted to the problem of malware detection. Main features of using sandbox
technology for malware detection is considered. The problem of malware using anti …