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IEEE Access, 2023 Malware has emerged as a cyber security threat that continuously changes to target computer systems, smart devices, and extensive networks with the development of information technologies.
Nor Zakiah Gorment +3 more
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A Comprehensive Review on Malware Detection Approaches
IEEE Access, 2020 According to the recent studies, malicious software (malware) is increasing at an alarming rate, and some malware can hide in the system by using different obfuscation techniques.
Omer Aslan, Refik Samet
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Malware Detection Using Memory Analysis Data in Big Data Environment
Applied Sciences, 2022 Malware is a significant threat that has grown with the spread of technology. This makes detecting malware a critical issue. Static and dynamic methods are widely used in the detection of malware. However, traditional static and dynamic malware detection
Murat Dener, Gökçe Ok, Abdullah Orman
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Evaluation of Machine Learning Algorithms for Malware Detection. [PDF]
Sensors (Basel), 2023 This research study mainly focused on the dynamic malware detection. Malware progressively changes, leading to the use of dynamic malware detection techniques in this research study. Each day brings a new influx of malicious software programmes that pose
Akhtar MS, Feng T.
europepmc +2 more sources
A Survey on ML Techniques for Multi-Platform Malware Detection: Securing PC, Mobile Devices, IoT, and Cloud Environments [PDF]
SensorsMalware has emerged as a significant threat to end-users, businesses, and governments, resulting in financial losses of billions of dollars. Cybercriminals have found malware to be a lucrative business because of its evolving capabilities and ability to ...
Jannatul Ferdous +3 more
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PAD: Towards Principled Adversarial Malware Detection Against Evasion Attacks [PDF]
IEEE Transactions on Dependable and Secure Computing, 2023 Machine Learning (ML) techniques can facilitate the automation of malicious software (malware for short) detection, but suffer from evasion attacks.
Deqiang Li +5 more
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LEDA—Layered Event-Based Malware Detection Architecture [PDF]
SensorsThe rapid increase in new malware necessitates effective detection methods. While machine learning techniques have shown promise for malware detection, most research focuses on identifying malware through the content of executable files or full behavior ...
Radu Marian Portase +3 more
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IoT-Based Android Malware Detection Using Graph Neural Network With Adversarial Defense [PDF]
IEEE Internet of Things Journal, 2023 Since the Internet of Things (IoT) is widely adopted using Android applications, detecting malicious Android apps is essential. In recent years, Android graph-based deep learning research has proposed many approaches to extract relationships from the ...
Rahul Yumlembam +3 more
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Packed malware variants detection using deep belief networks [PDF]
MATEC Web of Conferences, 2020 Malware is one of the most serious network security threats. To detect unknown variants of malware, many researches have proposed various methods of malware detection based on machine learning in recent years.
Zhang Zhigang +3 more
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Automated Machine Learning for Deep Learning based Malware Detection [PDF]
Computers & security, 2023 Deep learning (DL) has proven to be effective in detecting sophisticated malware that is constantly evolving. Even though deep learning has alleviated the feature engineering problem, finding the most optimal DL model, in terms of neural architecture ...
Austin R. Brown +2 more
semanticscholar +1 more source