Results 61 to 70 of about 29,821 (199)

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

Comparison of Deep Learning and the Classical Machine Learning Algorithm for the Malware Detection

, 2018
Recently, Deep Learning has been showing promising results in various Artificial Intelligence applications like image recognition, natural language processing, language modeling, neural machine translation, etc.
Rathore, Hemant, Sahay, Sanjay K., Sewak, Mohit   +2 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

Gauss-Mapping Black Widow Optimization With Deep Extreme Learning Machine for Android Malware Classification Model

IEEE Access, 2023
Nowadays, the malware on the Android platform is found to be increasing. With the prevalent use of code obfuscation technology, the precision of antivirus software and classical detection techniques is low.
Ghadah Aldehim, Munya A. Arasi, Majdi Khalid, Sumayh S. Aljameel, Radwa Marzouk, Heba Mohsen, Ishfaq Yaseen, Sara Saadeldeen Ibrahim   +7 more
doaj   +1 more source

Image and video analysis using graph neural network for Internet of Medical Things and computer vision applications

CAAI Transactions on Intelligence Technology, EarlyView.
Abstract Graph neural networks (GNNs) have revolutionised the processing of information by facilitating the transmission of messages between graph nodes. Graph neural networks operate on graph‐structured data, which makes them suitable for a wide variety of computer vision problems, such as link prediction, node classification, and graph classification.
Amit Sharma, Ashutosh Sharma, Alexey Tselykh, Alexander Bozhenyuk, Byung‐Gyu Kim   +4 more
wiley   +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

From Ambiguous Queries to Verifiable Insights: A Task‐Driven Framework for LLM‐Powered SOC Analysis⋆

CAAI Transactions on Intelligence Technology, EarlyView.
ABSTRACT Security operations centre (SOC) analysts must investigate alerts, correlate threat intelligence and interpret heterogeneous telemetry under tight timing constraints. Although large language models (LLMs) offer strong understanding capabilities, directly applying them to SOC environments remains challenging due to semantic ambiguity in analyst
Huan Zhang, Haiyan Wang, Hao Tan, Liyi Zeng, Jingnan Li, Zhaoquan Gu   +5 more
wiley   +1 more source

Android malware detection method based on deep neural network

网络与信息安全学报, 2020
Android is increasingly facing the threat of malware attacks. It is difficult to effectively detect large-sample and multi-class malware for traditional machine learning methods such as support vector machine, method for Android malware detection and ...
CHAO Fan, YANG Zhi, DU Xuehui, SUN Yan
doaj   +1 more source

Cyberattacks on Small Banks and the Impact on Local Banking Markets

Journal of Money, Credit and Banking, EarlyView.
Abstract Cyberattacks on small banks have direct and spillover effects in local markets. Following successful cyberattacks, hacked small banks experience a decline in deposit growth rates. This effect of cyberattacks is not observed in hacked large banks.
FABIAN GOGOLIN, IVAN LIM, FRANCESCO VALLASCAS   +2 more
wiley   +1 more source

A Survey for Deep Reinforcement Learning Based Network Intrusion Detection

Applied AI Letters, Volume 7, Issue 2, June 2026.
This paper surveys deep reinforcement learning (DRL) for network intrusion detection, evaluating model efficiency, minority attack detection, and dataset imbalance. Findings show DRL achieves state‐of‐the‐art results on public datasets, sometimes surpassing traditional deep learning.
Wanrong Yang, Alberto Acuto, Yihang Zhou, Dominik Wojtczak   +3 more
wiley   +1 more source