Results 51 to 60 of about 707,343 (186)

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

Mission Aware Cyber‐Physical Security

Systems Engineering, EarlyView.
ABSTRACT Perimeter cybersecurity, while essential, has proven insufficient against sophisticated, coordinated, and cyber‐physical attacks. In contrast, mission‐centric cybersecurity emphasizes finding evidence of attack impact on mission success, allowing for targeted resource allocation to mitigate vulnerabilities and protect critical assets.
Georgios Bakirtzis, Bryan Carter, Cody H. Fleming, Carl R. Elks   +3 more
wiley   +1 more source

Sec-Lib: Protecting Scholarly Digital Libraries From Infected Papers Using Active Machine Learning Framework

IEEE Access, 2019
Researchers from academia and the corporate-sector rely on scholarly digital libraries to access articles. Attackers take advantage of innocent users who consider the articles' files safe and thus open PDF-files with little concern.
Nir Nissim, Aviad Cohen, Jian Wu, Andrea Lanzi, Lior Rokach, Yuval Elovici, Lee Giles   +6 more
doaj   +1 more source

Evaluation of Machine Learning Algorithms for Malware Detection

Italian National Conference on Sensors, 2023
This research study mainly focused on the dynamic malware detection. Malware progressively changes, leading to the use of dynamic malware detection techniques in this research study. Each day brings a new influx of malicious software programmes that pose
M. Akhtar, Tao Feng
semanticscholar   +1 more source

Dynamic Analysis of Executables to Detect and Characterize Malware

, 2018
It is needed to ensure the integrity of systems that process sensitive information and control many aspects of everyday life. We examine the use of machine learning algorithms to detect malware using the system calls generated by executables-alleviating ...
Aimone, James B., Doak, Justin E., Draelos, Timothy J., Ingram, Joe B., James, Conrad D., Lamb, Christopher C., Smith, Michael R.   +6 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

Malware Detection with Artificial Intelligence: A Systematic Literature Review

ACM Computing Surveys, 2023
In this survey, we review the key developments in the field of malware detection using AI and analyze core challenges. We systematically survey state-of-the-art methods across five critical aspects of building an accurate and robust AI-powered malware ...
Matthew Gaber, Mohiuddin Ahmed, Helge Janicke   +2 more
semanticscholar   +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

Evading Classifiers by Morphing in the Dark

, 2017
Learning-based systems have been shown to be vulnerable to evasion through adversarial data manipulation. These attacks have been studied under assumptions that the adversary has certain knowledge of either the target model internals, its training ...
Chang, Ee-Chien, Dang, Hung, Huang, Yue
core   +1 more source

AI‐Powered Anomaly Detection for Secure Internet of Things (IoT): Optimising XGBoost and Deep Learning With Bayesian Optimisation

CAAI Transactions on Intelligence Technology, EarlyView.
ABSTRACT Intelligent and adaptive defence systems that can quickly thwart changing cyberthreats are becoming more and more necessary in the dynamic and data‐intensive Internet of things (IoT) environment. Using the NSL‐KDD benchmark dataset, this paper presents an improved anomaly detection system that combines an optimised sequential neural network ...
Seong‐O Shim, Lal Hussain, Mohammed A. Alqarni, Faisal S. Alsubaei, Rasha Jamal Atwah   +4 more
wiley   +1 more source