Results 71 to 80 of about 5,558 (210)

MACHINE LEARNING APPLICATIONS IN MALWARE CLASSIFICATION: A METAANALYSIS LITERATURE REVIEW [PDF]

, 2023
With a text mining and bibliometrics approach, this study reviews the literature on the evolution of malware classification using machine learning.
Nelson, Tjada, O\u27Brien, Austin, Noteboom, Cherie   +2 more
core   +4 more sources

DAEMON: Dataset/Platform-Agnostic Explainable Malware Classification Using Multi-Stage Feature Mining

IEEE Access, 2021
Numerous metamorphic and polymorphic malicious variants are generated automatically on a daily basis. In order to do that, malware vendors employ mutation engines that transform the code of a malicious program while retaining its functionality, aiming to
Ron Korine, Danny Hendler
doaj   +1 more source

DQN‐Guided Subset‐Induced OCSVM Kernel Approximation for Imbalanced Anomaly Detection

IEEJ Transactions on Electrical and Electronic Engineering, EarlyView.
Anomaly detection under limited normal data remains a fundamental challenge due to severe class imbalance and scarcity of anomalies. We propose a novel framework that reformulates support vector selection in One‐Class SVM as a sequential decision‐making problem.
Wenqian Yu, Jiaying Wu, Jinglu Hu
wiley   +1 more source

Exploring discriminatory features for automated malware classification [PDF]

, 2013
. The ever-growing malware threat in the cyber space calls for tech-niques that are more effective than widely deployed signature-based detection systems and more scalable than manual reverse engineering by forensic experts.
Nathan Brown, Deguang Kong, Guanhua Yan
core   +1 more source

Malware classification dataset, code and results [PDF]

, 2023
Malware classification dataset, code and ...
Md Ashikur Rahman
core   +1 more source

CyberSentinel: A Transparent Defense Framework for Malware Detection in High-Stakes Operational Environments

Sensors
Malware classification is a crucial step in defending against potential malware attacks. Despite the significance of a robust malware classifier, existing approaches reveal notable limitations in achieving high performance in malware classification. This
Mainak Basak, Myung-Mook Han
doaj   +1 more source

Similarity-Based Malware Classification Using Graph Neural Networks

Applied Sciences, 2022
This work proposes a novel malware identification model that is based on a graph neural network (GNN). The function call relationship and function assembly content obtained by analyzing the malware are used to generate a graph that represents the ...
Yu-Hung Chen, Jiann-Liang Chen, Ren-Feng Deng   +2 more
doaj   +1 more source

Malware family classification via efficient Huffman features [PDF]

, 2021
As malware evolves and becomes more complex, researchers strive to develop detection and classification schemes that abstract away from the internal intricacies of binary code to represent malware without the need for architectural knowledge or invasive ...
O’Shaughnessy, Stephen, Breitinger, Frank   +1 more
core   +1 more source

Graph neural network‐based attack prediction for communication‐based train control systems

CAAI Transactions on Intelligence Technology, EarlyView.
Abstract The Advanced Persistent Threats (APTs) have emerged as one of the key security challenges to industrial control systems. APTs are complex multi‐step attacks, and they are naturally diverse and complex. Therefore, it is important to comprehend the behaviour of APT attackers and anticipate the upcoming attack actions.
Junyi Zhao, Tao Tang, Bing Bu, Qichang Li   +3 more
wiley   +1 more source

EXAMINING THREAT GROUPS FROM THE OUTSIDE: GENERATING HIGH-LEVEL OVERVIEWS OF PERSISTENT AND TRADITIONAL COMPROMISES [PDF]

, 2014
Analyzing threats that have compromised electronic devices is important to compromised organizations, researchers, and law enforcement. Examination of network and host based logs and network traffic is effective in identifying threats, the impact, and ...
Horneman, Angela
core