Results 71 to 80 of about 5,547 (215)
, 2020 There are several websites, applications and resources that a user visits every day. Some of the resources have malicious threats and harmful entities.
Raza, Muhammad Hassan, Dubey, Shubhankar
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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 +3 more
wiley +1 more source
Humans vs. Machines in Malware Classification
, 2022 International audienceToday, the classification of a file as either benign or malicious is performed by a combination of deterministic indicators (such as antivirus rules), Machine Learning classifiers, and, more importantly, the judgment of human ...
Han, Yufei +3 more
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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
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 +4 more
wiley +1 more source
MCPDS: image-based malware classification method using PE metadata alone
CybersecurityIn response to the increasing threat posed by the exponential growth of malware in cybersecurity, researchers have developed a number of malware classification methods based on malware images and deep learning in recent years.
Yonglin Zhao +5 more
doaj +1 more source
File Entropy Signal Analysis Combined With Wavelet Decomposition for Malware Classification
IEEE Access, 2020 With the rapid development of the Internet, malware variants have increased exponentially, which poses a key threat to cyber security. Persistent efforts have been made to classify malware variants, but there are still many challenges, including the ...
Hui Guo +5 more
doaj +1 more source
Genetic boosting classification for malware detection [PDF]
2016 IEEE Congress on Evolutionary Computation (CEC), 2016 In the last few years virus writers have made use of new obfuscation techniques with the aim of hindering malware in order to difficult their detection by Anti-Virus engines. Strategies to reverse this trend involve executing potentially malicious programs and monitor the actions they perform in runtime, what is known as dynamic analysis. In this paper
Alejandro Martín +2 more
openaire +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 +5 more
wiley +1 more source
Android Malware Detection Technology Based on Deep Convolutional Neural Network
四川大学学报. 自然科学版, 2020 The rapid iteration of the Android system and its open source features have resulted in many variants of Android malware, which brings great challenges to the classification and detection of Android malware.
GAO Yang-Chen +3 more
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