A Comprehensive Analysis of Explainable AI for Malware Hunting
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 …
researchers have proposed to detect malware using intelligent techniques, such as Machine …
Explainable Malware Analysis: Concepts, Approaches and Challenges
Machine learning (ML) has seen exponential growth in recent years, finding applications in
various domains such as finance, medicine, and cybersecurity. Malware remains a …
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
Deep learning classifiers achieve state-of-the-art performance in various risk detection
applications. They explore rich semantic representations and are supposed to automatically …
applications. They explore rich semantic representations and are supposed to automatically …
Analyzing and explaining black-box models for online malware detection
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 …
of machine learning (ML) models for malware detection. These approaches have ranged …
GAGE: Genetic algorithm-based graph explainer for malware analysis
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 …
(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 …
as the primary method for its detection and analysis. However, the opaque nature of …
Explainable Deep Learning Models for Dynamic and Online Malware Classification
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 …
advanced preventive measures and remedial strategies. While several successful AI-based …
ML-FEED: Machine Learning Framework for Efficient Exploit Detection
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 …
security vulnerability exploits. Unfortunately, state-of-the-art ML models like long short-term …
An analysis of android malware classification services
The increasing number of Android malware forced antivirus (AV) companies to rely on
automated classification techniques to determine the family and class of suspicious …
automated classification techniques to determine the family and class of suspicious …
Malware Detection Tool Based on Emulator State Analysis
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 …
technology for malware detection is considered. The problem of malware using anti …