Results 51 to 60 of about 3,386 (193)

A Neural Network-Based Approach for Cryptographic Function Detection in Malware

IEEE Access, 2020
Cryptographic technology has been commonly used in malware for hiding their static characteristics and malicious behaviors to avoid the detection of anti-virus engines and counter the reverse analysis from security researchers.
Li Jia, Anmin Zhou, Peng Jia, Luping Liu, Yan Wang, Liang Liu   +5 more
doaj   +1 more source

PAIRED: An Explainable Lightweight Android Malware Detection System

IEEE Access, 2022
With approximately 2 billion active devices, the Android operating system tops all other operating systems in terms of the number of devices using it. Android has gained wide popularity not only as a smartphone operating system, but also as an operating ...
Mohammed M. Alani, Ali Ismail Awad
doaj   +1 more source

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

Systems 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

An Improved Method of Detecting Macro Malware on an Imbalanced Dataset

IEEE Access, 2020
In spear-phishing attacks, macro malware written in VBA (Visual Basic for Applications) is often used to compromise the target computers. Macro malware is often obfuscated in several ways to evade detection.
Mamoru Mimura
doaj   +1 more source

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

Systems 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, John M. “Mike” Borky, Thomas H. Bradley   +2 more
wiley   +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

Multimodal Approach for Malware Detection

, 2019
Although malware detection is a very active area of research, few works were focused on using physical properties (e.g., power consumption) and multimodal features for malware detection.
Hernandez Jimenez, Jarilyn M
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

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

Obfuscated Malware Detection and Classification in Network Traffic Leveraging Hybrid Large Language Models and Synthetic Data

Sensors
Android malware detection remains a critical issue for mobile security. Cybercriminals target Android since it is the most popular smartphone operating system (OS). Malware detection, analysis, and classification have become diverse research areas.
Mehwish Naseer, Farhan Ullah, Samia Ijaz, Hamad Naeem, Amjad Alsirhani, Ghadah Naif Alwakid, Abdullah Alomari   +6 more
doaj   +1 more source