Results 31 to 40 of about 1,405 (158)

Visualization techniques for malware behavior analysis

SPIE Proceedings, 2011
Malware spread via Internet is a great security threat, so studying their behavior is important to identify and classify them. Using SSDT hooking we can obtain malware behavior by running it in a controlled environment and capturing interactions with the target operating system regarding file, process, registry, network and mutex activities.
André R. A. Grégio, Rafael D. C. Santos   +1 more
openaire   +2 more sources

MalVis: Large-Scale Bytecode Visualization Framework for Explainable Android Malware Detection

Journal of Cybersecurity and Privacy
As technology advances, developers continually create innovative solutions to enhance smartphone security. However, the rapid spread of Android malware poses significant threats to devices and sensitive data.
Saleh J. Makkawy, Michael J. De Lucia, Kenneth E. Barner   +2 more
doaj   +1 more source

Enhancing Malware Analysis Using Data Visualization Through Shared Code and Attribute Analysis

IEEE Access
Malware analysis is a crucial area of cybersecurity, focusing on identifying, categorizing, and studying malicious software to prevent it from posing a threat to computer systems.
Narayandas Sai Ramana Vashista, K. Abhimanyu Kumar Patro   +1 more
doaj   +1 more source

Data‐Based Detection of Antagonistic Agents in a Robot Swarm Solving a Dynamic Coverage Task

International Journal of Mechanical System Dynamics, EarlyView.
ABSTRACT Robot swarms can be deployed as moving surveillance systems, for instance, as mobile anti‐poaching systems for monitoring wildlife and detecting poaching activities. Since poachers have an interest in evading detection, robots are at risk of being hijacked and manipulated to behave antagonistically, for example, to prevent the correct ...
Ingeborg Wenger, Peter Eberhard, Henrik Ebel   +2 more
wiley   +1 more source

MalGraphIQ: A tool for generating behavior representations of malware execution traces

SoftwareX
Understanding and interpreting malware behavior remains an open challenge in the field of cybersecurity. The dynamic analysis of malware execution traces has emerged as a promising approach for discovering behavioral insights that allow the visual ...
Razvan Raducu, Ricardo J. Rodríguez, Pedro Álvarez   +2 more
doaj   +1 more source

Android Malware Classification Based on Fuzzy Hashing Visualization

Machine Learning and Knowledge Extraction, 2023
The proliferation of Android-based devices has brought about an unprecedented surge in mobile application usage, making the Android ecosystem a prime target for cybercriminals.
Horacio Rodriguez-Bazan, Grigori Sidorov, Ponciano Jorge Escamilla-Ambrosio   +2 more
doaj   +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

Application of Entropy Visualization Method in Malware Classification [PDF]

Jisuanji gongcheng, 2017
Soaring malwares threat the security of information systems.For increasing identification efficiency and improving response speed,this paper presents a new malware visualization method for classification based on Shannon entropy,Jaccard index and K ...
REN Zhuojun,CHEN Guang
doaj   +1 more source

Modeling Correlation between Android Permissions Based on Threat and Protection Level Using Exploratory Factor Plane Analysis

Journal of Cybersecurity and Privacy, 2021
The evolution of mobile technology has increased correspondingly with the number of attacks on mobile devices. Malware attack on mobile devices is one of the top security challenges the mobile community faces daily.
Moses Ashawa, Sarah Morris
doaj   +1 more source

Tomtit‐Raven Evolutionary Selector‐Reinforced Attention‐Driven: A High‐Performance and Computationally Efficient Cyber Threat Detection Framework for Smart Grids

Energy Science &Engineering, Volume 14, Issue 3, Page 1431-1455, March 2026.
Overview of the proposed work. ABSTRACT Identifying cyber threats maintains the security and operational stability of smart grid systems because they experience escalating attacks that endanger both operating data reliability and system stability and electricity grid performance.
Priya R. Karpaga, Praveen Kumar Balachandran, Umawathy Techanamurthy, Muhammad Ammirrul Atiqi Mohd Zainuri   +3 more
wiley   +1 more source