Results 61 to 70 of about 81,305 (190)

Major Cybersecurity Breaches: Shaping Corporate Cybersecurity Policies and Closing the Gaps

Journal of Corporate Accounting &Finance, EarlyView.
ABSTRACT As digitalization accelerates, cybercrime has intensified in both scale and impact over the past two decades. This study aims to critically examine major cybersecurity events, assess them through the lens of routine activity theory, examine insight from three other established criminological and organizational theories, and address central ...
Laura K. Rickett, Deborah Smith
wiley   +1 more source

Semantics-aware malware detection [PDF]

2005 IEEE Symposium on Security and Privacy (S&P'05), 2005
A malware detector is a system that attempts to determine whether a program has malicious intent. In order to evade detection, malware writers (hackers) frequently use obfuscation to morph malware. Malware detectors that use a pattern-matching approach (such as commercial virus scanners) are susceptible to obfuscations used by hackers.
Christodorescu, Mihai, Somesh Jha, Sanjit A. Seshia, Song, Dawn, Bryant, Randal   +4 more
openaire   +1 more source

Finding Minimum‐Cost Explanations for Predictions Made by Tree Ensembles

Software: Practice and Experience, EarlyView.
ABSTRACT The ability to reliably explain why a machine learning model arrives at a particular prediction is crucial when used as decision support by human operators of critical systems. The provided explanations must be provably correct, and preferably without redundant information, called minimal explanations.
John Törnblom, Emil Karlsson, Simin Nadjm‐Tehrani   +2 more
wiley   +1 more source

Multifamily malware models

Journal of Computer Virology and Hacking Techniques, 2020
When training a machine learning model, there is likely to be a tradeoff between accuracy and the diversity of the dataset. Previous research has shown that if we train a model to detect one specific malware family, we generally obtain stronger results as compared to a case where we train a single model on multiple diverse families. However, during the
Basole, Samanvitha, Di Troia, Fabio, Stamp, Mark   +2 more
openaire   +3 more sources

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

DroidDetectMW: A Hybrid Intelligent Model for Android Malware Detection

Applied Sciences, 2023
Malicious apps specifically aimed at the Android platform have increased in tandem with the proliferation of mobile devices. Malware is now so carefully written that it is difficult to detect.
Fatma Taher, Omar AlFandi, Mousa Al-kfairy, Hussam Al Hamadi, Saed Alrabaee   +4 more
doaj   +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

Machine Learning Aided Static Malware Analysis: A Survey and Tutorial

, 2018
Malware analysis and detection techniques have been evolving during the last decade as a reflection to development of different malware techniques to evade network-based and host-based security protections.
Andrii Shalaginov, D Krishna Sandeep Reddy, Farid Daryabar, Igor Santos, Reinaldo Jose Mangialardo, Smita Naval, Steve Watson, Teuvo Kohonen, Yanfang Ye   +8 more
core   +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

An Open Source, Extensible Malware Analysis Platform

MATEC Web of Conferences, 2018
A malware (such as viruses, ransomware) is the main source of bringing serious security threats to the IT systems and their users now-adays. In order to protect the systems and their legitimate users from these threats, anti-malware applications are ...
Michalopoulos P., Ieronymakis V., Khan M.T., Serpanos D.   +3 more
doaj   +1 more source