Results 71 to 80 of about 24,379 (232)

A Systems‐Level Approach to Address Risks and Ethics in Artificial Intelligence Systems

open access: yesSystems Engineering, EarlyView.
ABSTRACT Artificial intelligence (AI) is rapidly changing the world, from completely controlling routine or mundane tasks like text and image generation, to powering advanced algorithms that control critical systems. The recent advances in generative AI quickly overwhelmed multiple industries from education to finance as first adopters rushed (and ...
Vincent P. Paglioni, Torrey Mortenson
wiley   +1 more source

Assessment of a Model‐Based Approach to Achieve Authorization to Operate

open access: yesSystems Engineering, EarlyView.
ABSTRACT Accreditation of United States Government (USG) Information Systems (IS) is required to assure their function and security before delivery to the operational environment. However, in many cases, the baseline document‐based accreditation processes are sources of cost and schedule overruns.
Edan C. Sanchez   +2 more
wiley   +1 more source

An Open Source, Extensible Malware Analysis Platform

open access: yesMATEC 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.   +3 more
doaj   +1 more source

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

open access: yesIEEJ 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

vinayakumarr/Android-Malware-Detection v1

open access: yes, 2019
Android malware detection using static and dynamic ...
Vinayakumar R
core   +1 more source

ConvProtoNet: Deep Prototype Induction towards Better Class Representation for Few-Shot Malware Classification

open access: yesApplied Sciences, 2020
Traditional malware classification relies on known malware types and significantly large datasets labeled manually which limits its ability to recognize new malware classes.
Zhijie Tang, Peng Wang, Junfeng Wang
doaj   +1 more source

Generating Synthetic Malware Samples Using Generative AI

open access: yesIEEE Access
Malware attacks have a significant negative impact on organizations of varied scales in the field of cybersecurity. Recently, malware researchers have increasingly turned to machine learning techniques to combat sophisticated obfuscation methods used in ...
Tiffany Bao   +4 more
doaj   +1 more source

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

open access: yesCAAI 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

A3CM: Automatic Capability Annotation for Android Malware

open access: yesIEEE Access, 2019
Android malware poses serious security and privacy threats to the mobile users. Traditional malware detection and family classification technologies are becoming less effective due to the rapid evolution of the malware landscape, with the emerging of so ...
Junyang Qiu   +6 more
doaj   +1 more source

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

open access: yesCAAI 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

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