Results 71 to 80 of about 4,213 (184)

Graph–Time IoT IDS: Requirement‐Aligned Impact Evaluation

Engineering Reports, Volume 8, Issue 7, July 2026.
A multi‐view intrusion detection framework (IMPACT‐MVG) combines temporal behavior modeling and graph‐based interaction analysis to detect IoT network attacks. Impact‐centric evaluation using the ICSec score shows that the approach reduces operational damage from intrusions while maintaining efficient, explainable, and privacy‐aware security monitoring.
Kumkum Dubey, Isha Batra, Abhishek Bajpai, Arun Malik, Mohammed Ali Alshara, Tariqullah Jan, Naveed Ahmad, Ruhul Amin Khalil   +7 more
wiley   +1 more source

A Comprehensive Review of AI‐Powered Energy Systems

Energy Science &Engineering, Volume 14, Issue 7, Page 3554-3580, July 2026.
The role of Artificial Intelligence (AI) in developing next‐generation energy systems is getting more day by day. Therefore, incorporating AI enables real‐time decision‐making and advanced grid management, which are essential for optimizing the use of intermittent renewable sources like wind and solar power.
Armin Razmjoo, Pedram Asef, Masoumeh Javadi, Mousa Marzband, Shady S. Refaat, Muhyaddin Rawa   +5 more
wiley   +1 more source

Detecting Malware with Information Complexity

CoRR, 2015
This work focuses on a specific front of the malware detection arms-race, namely the detection of persistent, disk-resident malware. We exploit normalised compression distance (NCD), an information theoretic measure, applied directly to binaries. Given a zoo of labelled malware and benign-ware, we ask whether a suspect program is more similar to our ...
Nadia Alshahwan, Earl T. Barr, David Clark 0001, George Danezis   +3 more
openaire   +2 more sources

Obfuscated Memory Malware Detection

CoRR
Providing security for information is highly critical in the current era with devices enabled with smart technology, where assuming a day without the internet is highly impossible. Fast internet at a cheaper price, not only made communication easy for legitimate users but also for cybercriminals to induce attacks in various dimensions to breach privacy
Sharmila S. P, Aruna Tiwari, Narendra S. Chaudhari   +2 more
openaire   +2 more sources

Malware Clustering Using Family Dependency Graph

IEEE Access, 2019
Malware brings a major security threat on the Internet today. It is not surprising that much research has concentrated on detecting malware. Unfortunately, the current malware detection approaches suffer from ineffective detection of new malware samples.
Binlin Cheng, Qiang Tong, Jianhong Wang, Wenhui Tian   +3 more
doaj   +1 more source

From Static to AI-Driven Detection: A Comprehensive Review of Obfuscated Malware Techniques

IEEE Access
The frequency of cyber attacks targeting individuals, businesses, and organizations globally has escalated in recent years. The evolution of obfuscated malware, designed to evade detection, has been unprecedented, employing new and sophisticated ...
Saranya Chandran, Sreelakshmi R. Syam, Sriram Sankaran, Tulika Pandey, Krishnashree Achuthan   +4 more
doaj   +1 more source

A New Approach to Malware Detection [PDF]

, 2009
Malware has become one of the most serious threats to computer users. Early techniques based on syntactic signatures can be easily bypassed using program obfuscation. A promising direction is to combine Control Flow Graph (CFG) with instruction-level information.
Hongying Tang, Bo Zhu 0001, Kui Ren 0001
openaire   +1 more source

A study of the relationship of malware detection mechanisms using Artificial Intelligence

ICT Express
Implementation of malware detection using Artificial Intelligence (AI) has emerged as a significant research theme to combat evolving various types of malwares.
Jihyeon Song, Sunoh Choi, Jungtae Kim, Kyungmin Park, Cheolhee Park, Jonghyun Kim, Ikkyun Kim   +6 more
doaj   +1 more source

Optimal Weighted Voting-Based Collaborated Malware Detection for Zero-Day Malware: A Case Study on VirusTotal and MalwareBazaar

Future Internet
We propose a detection system incorporating a weighted voting mechanism that reflects the vote’s reliability based on the accuracy of each detector’s examination, which overcomes the problem of cooperative detection. Collaborative malware detection is an
Naonobu Okazaki, Shotaro Usuzaki, Tsubasa Waki, Hyoga Kawagoe, Mirang Park, Hisaaki Yamaba, Kentaro Aburada   +6 more
doaj   +1 more source

Static analysis framework for permission-based dataset generation and android malware detection using machine learning

EURASIP Journal on Information Security
Since Android is the popular mobile operating system worldwide, malicious attackers seek out Android smartphones as targets. The Android malware can be identified through a number of established detection techniques.
Amarjyoti Pathak, Th. Shanta Kumar, Utpal Barman   +2 more
doaj   +1 more source