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Adversarial machine learning applied to intrusion and malware scenarios: a systematic review
Cyber-security is the practice of protecting computing systems and networks from digital
attacks, which are a rising concern in the Information Age. With the growing pace at which …
attacks, which are a rising concern in the Information Age. With the growing pace at which …
[HTML][HTML] Android malware family classification and analysis: Current status and future directions
Android receives major attention from security practitioners and researchers due to the influx
number of malicious applications. For the past twelve years, Android malicious applications …
number of malicious applications. For the past twelve years, Android malicious applications …
An effective end-to-end android malware detection method
Android has rapidly become the most popular mobile operating system because of its open
source, rich hardware selectivity, and millions of applications (Apps). Meanwhile, the open …
source, rich hardware selectivity, and millions of applications (Apps). Meanwhile, the open …
Android malware detection through hybrid features fusion and ensemble classifiers: The AndroPyTool framework and the OmniDroid dataset
Cybersecurity has become a major concern for society, mainly motivated by the increasing
number of cyber attacks and the wide range of targeted objectives. Due to the popularity of …
number of cyber attacks and the wide range of targeted objectives. Due to the popularity of …
The Threat of Adversarial Attacks on Machine Learning in Network Security--A Survey
Machine learning models have made many decision support systems to be faster, more
accurate, and more efficient. However, applications of machine learning in network security …
accurate, and more efficient. However, applications of machine learning in network security …
Backdoor attack on machine learning based android malware detectors
Machine learning (ML) has been widely used for malware detection on different operating
systems, including Android. To keep up with malware's evolution, the detection models …
systems, including Android. To keep up with malware's evolution, the detection models …
Explaining black-box android malware detection
Machine-learning models have been recently used for detecting malicious Android
applications, reporting impressive performances on benchmark datasets, even when trained …
applications, reporting impressive performances on benchmark datasets, even when trained …
Malware detection using static analysis in Android: a review of FeCO (features, classification, and obfuscation)
Android is a free open-source operating system (OS), which allows an in-depth
understanding of its architecture. Therefore, many manufacturers are utilizing this OS to …
understanding of its architecture. Therefore, many manufacturers are utilizing this OS to …
Aimed: Evolving malware with genetic programming to evade detection
Genetic Programming (GP) has previously proved to achieve valuable results on the fields of
image processing and arcade learning. Similarly, it can be used as an adversarial learning …
image processing and arcade learning. Similarly, it can be used as an adversarial learning …
Maloid-DS: Labeled Dataset for Android Malware Forensics
Billions of people globally use Android devices (https://backlinko. com/iphone-vs-android-
statistics). As such, these devices are highly targeted by security attackers. One of the most …
statistics). As such, these devices are highly targeted by security attackers. One of the most …