Results 11 to 20 of about 31,501 (227)

MalwareDNA: Simultaneous Classification of Malware, Malware Families, and Novel Malware

2023 IEEE International Conference on Intelligence and Security Informatics (ISI), 2023
Malware is one of the most dangerous and costly cyber threats to national security and a crucial factor in modern cyber-space. However, the adoption of machine learning (ML) based solutions against malware threats has been relatively slow. Shortcomings in the existing ML approaches are likely contributing to this problem.
Eren, Maksim E., Bhattarai, Manish, Rasmussen, Kim, Alexandrov, Boian S., Nicholas, Charles   +4 more
openaire   +2 more sources

Binary and Multi-Class Malware Threads Classification

Applied Sciences, 2022
The security of a computer system can be harmed by specific applications, such as malware. Malware comprises unwanted, dangerous enemies that aim to compromise the security and generate significant loss.
Ismail Taha Ahmed, Norziana Jamil, Marina Md. Din, Baraa Tareq Hammad   +3 more
doaj   +1 more source

Robust Malware Family Classification Using Effective Features and Classifiers

Applied Sciences, 2022
Malware development has significantly increased recently, posing a serious security risk to both consumers and businesses. Malware developers continually find new ways to circumvent security research’s ongoing efforts to guard against malware attacks ...
Baraa Tareq Hammad, Norziana Jamil, Ismail Taha Ahmed, Zuhaira Muhammad Zain, Shakila Basheer   +4 more
doaj   +1 more source

Identifying the Author Group of Malwares through Graph Embedding and Human-in-the-Loop Classification

Applied Sciences, 2021
Malware are developed for various types of malicious attacks, e.g., to gain access to a user’s private information or control of the computer system. The identification and classification of malware has been extensively studied in academic societies and ...
Dong-Kyu Chae, Sung-Jun Park, Eujeanne Kim, Jiwon Hong, Sang-Wook Kim   +4 more
doaj   +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

Detection of Exceptional Malware Variants Using Deep Boosted Feature Spaces and Machine Learning

Applied Sciences, 2021
Malware is a key component of cyber-crime, and its analysis is the first line of defence against cyber-attack. This study proposes two new malware classification frameworks: Deep Feature Space-based Malware classification (DFS-MC) and Deep Boosted ...
Muhammad Asam, Shaik Javeed Hussain, Mohammed Mohatram, Saddam Hussain Khan, Tauseef Jamal, Amad Zafar, Asifullah Khan, Muhammad Umair Ali, Umme Zahoora   +8 more
doaj   +1 more source

DroidDetectMW: A Hybrid Intelligent Model for Android Malware Detection

Applied Sciences, 2023
Malicious apps specifically aimed at the Android platform have increased in tandem with the proliferation of mobile devices. Malware is now so carefully written that it is difficult to detect.
Fatma Taher, Omar AlFandi, Mousa Al-kfairy, Hussam Al Hamadi, Saed Alrabaee   +4 more
doaj   +1 more source

A multilabel fuzzy relevance clustering system for malware attack attribution in the edge layer of cyber-physical networks [PDF]

, 2020
The rapid increase in the number of malicious programs has made malware forensics a daunting task and caused users’ systems to become in danger. Timely identification of malware characteristics including its origin and the malware sample family would ...
Alaeiyan, M, Conti, M, Dargahi, T, Dehghantanha, A, Parsa, S   +4 more
core   +2 more sources

Klasifikasi Malware Family menggunakan Metode k-Nearest Neighbor (k-NN) [PDF]

, 2021
Smartphones based on Android OS have the most users today because they are comfortable to use and offer a variety of features. As a result, many malware developers have made Android OS their main target. Every year, new types of malware families emerge
Akbi, Denar Regata, Nastiti, Vinna Rahmayanti Setyaning, Yogasware, Achmad Rizal   +2 more
core   +1 more source

On Deceiving Malware Classification with Section Injection

Machine Learning and Knowledge Extraction, 2023
We investigate how to modify executable files to deceive malware classification systems. This work’s main contribution is a methodology to inject bytes across a malware file randomly and use it both as an attack to decrease classification accuracy but ...
Adeilson Antonio da Silva, Mauricio Pamplona Segundo   +1 more
doaj   +1 more source