Supervised feature selection techniques in network intrusion detection: A critical review
Abstract Machine Learning (ML) techniques are becoming an invaluable support for network
intrusion detection, especially in revealing anomalous flows, which often hide cyber-threats …
intrusion detection, especially in revealing anomalous flows, which often hide cyber-threats …
Arms race in adversarial malware detection: A survey
Malicious software (malware) is a major cyber threat that has to be tackled with Machine
Learning (ML) techniques because millions of new malware examples are injected into …
Learning (ML) techniques because millions of new malware examples are injected into …
MLDroid—framework for Android malware detection using machine learning techniques
A Mahindru, AL Sangal - Neural Computing and Applications, 2021 - Springer
This research paper presents MLDroid—a web-based framework—which helps to detect
malware from Android devices. Due to increase in the popularity of Android devices …
malware from Android devices. Due to increase in the popularity of Android devices …
I-SiamIDS: an improved Siam-IDS for handling class imbalance in network-based intrusion detection systems
Abstract Network-based Intrusion Detection Systems (NIDSs) identify malicious activities by
analyzing network traffic. NIDSs are trained with the samples of benign and intrusive …
analyzing network traffic. NIDSs are trained with the samples of benign and intrusive …
Deep learning feature exploration for android malware detection
Android mobile devices and applications are widely deployed and used in industry and
smart city. Malware detection is one of the most powerful and effective approaches to …
smart city. Malware detection is one of the most powerful and effective approaches to …
ProDroid—An Android malware detection framework based on profile hidden Markov model
SK Sasidharan, C Thomas - Pervasive and Mobile Computing, 2021 - Elsevier
Popularity and openness have made the Android platform a potential target of malware
attacks. The hackers continuously evolve and improve attacking strategies to identify …
attacks. The hackers continuously evolve and improve attacking strategies to identify …
[HTML][HTML] On machine learning effectiveness for malware detection in Android OS using static analysis data
V Syrris, D Geneiatakis - Journal of Information Security and Applications, 2021 - Elsevier
Although various security mechanisms have been introduced in Android operating system in
order to enhance its robustness, sheer protection remains an open issue: malicious …
order to enhance its robustness, sheer protection remains an open issue: malicious …
FSDroid:-A feature selection technique to detect malware from Android using Machine Learning Techniques: FSDroid
A Mahindru, AL Sangal - Multimedia Tools and Applications, 2021 - Springer
With the recognition of free apps, Android has become the most widely used smartphone
operating system these days and it naturally invited cyber-criminals to build malware …
operating system these days and it naturally invited cyber-criminals to build malware …
Effective combining of feature selection techniques for machine learning-enabled IoT intrusion detection
The rapid advancement of technologies has enabled businesses to carryout their activities
seamlessly and revolutionised communications across the globe. There is a significant …
seamlessly and revolutionised communications across the globe. There is a significant …
Feature subset selection for malware detection in smart IoT platforms
Malicious software (“malware”) has become one of the serious cybersecurity issues in
Android ecosystem. Given the fast evolution of Android malware releases, it is practically not …
Android ecosystem. Given the fast evolution of Android malware releases, it is practically not …