Results 61 to 70 of about 5,547 (215)
Classification of Malware Models
, 2019 Automatically classifying similar malware families is a challenging problem. In this research, we attempt to classify malware families by applying machine learning to machine learning models.
Sethi, Akriti
openaire +4 more sources
Modelling Critical Impeding Factors of Gamification Adoption: An ISM‐MICMAC Analysis
Global Business and Organizational Excellence, EarlyView.ABSTRACT Gamification is a transformative technology that attracts consumers and motivates them toward desired actions through fun and engagement. Despite its growing popularity and influence on user behavior, gamification faces significant challenges in acceptance and implementation due to behavioral, technological, economic, and regulatory factors ...
Wamika Sharma +4 more
wiley +1 more source
SensorsMalware classification is a crucial step in defending against potential malware attacks. Despite the significance of a robust malware classifier, existing approaches reveal notable limitations in achieving high performance in malware classification. This
Mainak Basak, Myung-Mook Han
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
Malware family classification via efficient Huffman features [PDF]
, 2021 As malware evolves and becomes more complex, researchers strive to develop detection and classification schemes that abstract away from the internal intricacies of binary code to represent malware without the need for architectural knowledge or invasive ...
O’Shaughnessy, Stephen +1 more
core +1 more source
Similarity-Based Malware Classification Using Graph Neural Networks
Applied Sciences, 2022 This work proposes a novel malware identification model that is based on a graph neural network (GNN). The function call relationship and function assembly content obtained by analyzing the malware are used to generate a graph that represents the ...
Yu-Hung Chen +2 more
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 +2 more
wiley +1 more source
EXAMINING THREAT GROUPS FROM THE OUTSIDE: GENERATING HIGH-LEVEL OVERVIEWS OF PERSISTENT AND TRADITIONAL COMPROMISES [PDF]
, 2014 Analyzing threats that have compromised electronic devices is important to compromised organizations, researchers, and law enforcement. Examination of network and host based logs and network traffic is effective in identifying threats, the impact, and ...
Horneman, Angela
core
IEEE Access, 2023 Nowadays, the malware on the Android platform is found to be increasing. With the prevalent use of code obfuscation technology, the precision of antivirus software and classical detection techniques is low.
Ghadah Aldehim +7 more
doaj +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