Machine learning for anomaly detection: A systematic review
Anomaly detection has been used for decades to identify and extract anomalous
components from data. Many techniques have been used to detect anomalies. One of the …
components from data. Many techniques have been used to detect anomalies. One of the …
The evolution of android malware and android analysis techniques
With the integration of mobile devices into daily life, smartphones are privy to increasing
amounts of sensitive information. Sophisticated mobile malware, particularly Android …
amounts of sensitive information. Sophisticated mobile malware, particularly Android …
[HTML][HTML] MalDozer: Automatic framework for android malware detection using deep learning
Android OS experiences a blazing popularity since the last few years. This predominant
platform has established itself not only in the mobile world but also in the Internet of Things …
platform has established itself not only in the mobile world but also in the Internet of Things …
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 …
Effective android malware detection with a hybrid model based on deep autoencoder and convolutional neural network
Android security incidents occurred frequently in recent years. To improve the accuracy and
efficiency of large-scale Android malware detection, in this work, we propose a hybrid model …
efficiency of large-scale Android malware detection, in this work, we propose a hybrid model …
Evaluation of machine learning classifiers for mobile malware detection
Mobile devices have become a significant part of people's lives, leading to an increasing
number of users involved with such technology. The rising number of users invites hackers …
number of users involved with such technology. The rising number of users invites hackers …
[HTML][HTML] An in-depth review of machine learning based Android malware detection
It is estimated that around 70% of mobile phone users have an Android device. Due to this
popularity, the Android operating system attracts a lot of malware attacks. The sensitive …
popularity, the Android operating system attracts a lot of malware attacks. The sensitive …
Machine learning for cloud security: a systematic review
The popularity and usage of Cloud computing is increasing rapidly. Several companies are
investing in this field either for their own use or to provide it as a service for others. One of …
investing in this field either for their own use or to provide it as a service for others. One of …
{Explanation-Guided} backdoor poisoning attacks against malware classifiers
Training pipelines for machine learning (ML) based malware classification often rely on
crowdsourced threat feeds, exposing a natural attack injection point. In this paper, we study …
crowdsourced threat feeds, exposing a natural attack injection point. In this paper, we study …
Mobile malware attacks: Review, taxonomy & future directions
A pervasive increase in the adoption rate of smartphones with Android OS is noted in recent
years. Android's popular and attractive environment not only captured the attention of users …
years. Android's popular and attractive environment not only captured the attention of users …