Results 11 to 20 of about 2,185,837 (169)

A Data Mining Classification Approach for Behavioral Malware Detection [PDF]

Journal of Computer Networks and Communications, 2016
Data mining techniques have numerous applications in malware detection. Classification method is one of the most popular data mining techniques. In this paper we present a data mining classification approach to detect malware behavior.
Monire Norouzi, Alireza Souri, Majid Samad Zamini   +2 more
doaj   +2 more sources

MalwareDNA: Simultaneous Classification of Malware, Malware Families, and Novel Malware [PDF]

, 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.
Alexandrov, Boian S., Bhattarai, Manish, Eren, Maksim E., Nicholas, Charles, Rasmussen, Kim   +4 more
core   +3 more sources

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

Malware Classification with BERT [PDF]

, 2021
Malware Classification is used to distinguish unique types of malware from each other. This project aims to carry out malware classification using word embeddings which are used in Natural Language Processing (NLP) to identify and evaluate the ...
Alvares, Joel Lawrence, Joel Lawrence Alvares   +1 more
core   +1 more source

Malware Classification using Km-SVM [PDF]

, 2020
Malware identification and classification is a problem faced even in this decade. This is majorly due to the fact that advance malware are more sophisticated in nature and have state of the art abilities to remain hidden or change their code/behaviour ...
Ghorpade, Ashish
core   +3 more sources

ConvProtoNet: Deep Prototype Induction towards Better Class Representation for Few-Shot Malware Classification

Applied Sciences, 2020
Traditional malware classification relies on known malware types and significantly large datasets labeled manually which limits its ability to recognize new malware classes.
Zhijie Tang, Peng Wang, Junfeng Wang
doaj   +1 more source

Generative Adversarial Network for Global Image-Based Local Image to Improve Malware Classification Using Convolutional Neural Network

Applied Sciences, 2020
Malware detection and classification methods are being actively developed to protect personal information from hackers. Global images of malware (in a program that includes personal information) can be utilized to detect or classify it.
Sejun Jang, Shuyu Li, Yunsick Sung
doaj   +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

Efficient Malware Classification by Binary Sequences with One-Dimensional Convolutional Neural Networks

Mathematics, 2022
The rapid increase of malware attacks has become one of the main threats to computer security. Finding the best way to detect malware has become a critical task in cybersecurity. Previous work shows that machine learning approaches could be a solution to
Wei-Cheng Lin, Yi-Ren Yeh
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