Results 21 to 30 of about 12,725 (233)

Technique for IoT malware detection based on control flow graph analysis

Радіоелектронні і комп'ютерні системи, 2022
The Internet of Things (IoT) refers to the millions of devices around the world that are connected to the Internet. Insecure IoT devices designed without proper security features are the targets of many Internet threats.
Kira Bobrovnikova, Sergii Lysenko, Bohdan Savenko, Piotr Gaj, Oleg Savenko   +4 more
doaj   +1 more source

Morphological detection of malware [PDF]

2008 3rd International Conference on Malicious and Unwanted Software (MALWARE), 2008
In the field of malware detection, method based on syntactical consideration are usually efficient. However, they are strongly vulnerable to obfuscation techniques. This study proposes an efficient construction of a morphological malware detector based on a syntactic and a semantic analysis, technically on control flow graphs of programs (CFG).
Bonfante, Guillaume, Kaczmarek, Matthieu, Marion, Jean-Yves   +2 more
openaire   +2 more sources

An Adaptive Behavioral-Based Incremental Batch Learning Malware Variants Detection Model Using Concept Drift Detection and Sequential Deep Learning

IEEE Access, 2021
Malware variants are the major emerging threats that face cybersecurity due to the potential damage to computer systems. Many solutions have been proposed for detecting malware variants.
Abdulbasit A. Darem, Fuad A. Ghaleb, Asma A. Al-Hashmi, Jemal H. Abawajy, Sultan M. Alanazi, Afrah Y. Al-Rezami   +5 more
doaj   +1 more source

Detecting Malware with Information Complexity [PDF]

Entropy, 2020
Malware concealment is the predominant strategy for malware propagation. Black hats create variants of malware based on polymorphism and metamorphism. Malware variants, by definition, share some information. Although the concealment strategy alters this information, there are still patterns on the software.
Nadia Alshahwan, Earl T. Barr, David Clark 0001, George Danezis, Héctor D. Menéndez 0001   +4 more
openaire   +4 more sources

The rise of obfuscated Android malware and impacts on detection methods [PDF]

PeerJ Computer Science, 2022
The various application markets are facing an exponential growth of Android malware. Every day, thousands of new Android malware applications emerge. Android malware hackers adopt reverse engineering and repackage benign applications with their malicious
Wael F. Elsersy, Ali Feizollah, Nor Badrul Anuar   +2 more
doaj   +2 more sources

HMLET: Hunt Malware Using Wavelet Transform on Cross-Platform

IEEE Access, 2022
As the importance of cyberspace grows, malicious software (malware) is threatening not only individuals but also countries. In addition, numerous malware is still circulating in cyberspace, and as technology advances, new or advanced malware are emerging.
Sangmin Park, Sanghoon Jeon, Huy Kang Kim   +2 more
doaj   +1 more source

Automated System-Level Malware Detection Using Machine Learning: A Comprehensive Review

Applied Sciences, 2023
Malware poses a significant threat to computer systems and networks. This necessitates the development of effective detection mechanisms. Detection mechanisms dependent on signatures for attack detection perform poorly due to high false negatives.
Nana Kwame Gyamfi, Nikolaj Goranin, Dainius Ceponis, Habil Antanas Čenys   +3 more
doaj   +1 more source

Investigation of bypassing malware defences and malware detections [PDF]

2011 7th International Conference on Information Assurance and Security (IAS), 2011
Nowadays, malware incident is one of the most expensive damages caused by attackers. Malwares are caused different attacks, so considerations and implementations of malware defences for internal networks are important. In this papers, different techniques such as repacking, reverse engineering and hex editing for bypassing host-based Anti Virus (AV ...
Farid Daryabar, Ali Dehghantanha, Nur Izura Udzir   +2 more
openaire   +1 more source

Adaptive secure malware efficient machine learning algorithm for healthcare data

CAAI Transactions on Intelligence Technology, EarlyView., 2023
Abstract Malware software now encrypts the data of Internet of Things (IoT) enabled fog nodes, preventing the victim from accessing it unless they pay a ransom to the attacker. The ransom injunction is constantly accompanied by a deadline. These days, ransomware attacks are too common on IoT healthcare devices.
Mazin Abed Mohammed, Abdullah Lakhan, Dilovan Asaad Zebari, Karrar Hameed Abdulkareem, Jan Nedoma, Radek Martinek, Usman Tariq, Majed Alhaisoni, Prayag Tiwari   +8 more
wiley   +1 more source

Deep Android Malware Detection [PDF]

Proceedings of the Seventh ACM on Conference on Data and Application Security and Privacy, 2017
In this paper, we propose a novel android malware detection system that uses a deep convolutional neural network (CNN). Malware classification is performed based on static analysis of the raw opcode sequence from a disassembled program. Features indicative of malware are automatically learned by the network from the raw opcode sequence thus removing ...
Niall McLaughlin, Jesús Martínez del Rincón, BooJoong Kang, Suleiman Y. Yerima, Paul Miller 0003, Sakir Sezer, Yeganeh Safaei, Erik Trickel, Ziming Zhao 0001, Adam Doupé, Gail-Joon Ahn   +10 more
openaire   +3 more sources