Related papers: Efficient Concept Drift Handling for Batch Android…
The rise in popularity of the Android platform has resulted in an explosion of malware threats targeting it. As both Android malware and the operating system itself constantly evolve, it is very challenging to design robust malware…
In the Machine Learning (ML) model development lifecycle, training candidate models using an offline holdout dataset and identifying the best model for the given task is only the first step. After the deployment of the selected model,…
Thousands of malicious applications targeting mobile devices, including the popular Android platform, are created every day. A large number of those applications are created by a small number of professional under-ground actors, however…
Analysing malware is important to understand how malicious software works and to develop appropriate detection and prevention methods. Dynamic analysis can overcome evasion techniques commonly used to bypass static analysis and provide…
Machine learning (ML) algorithms deployed in real-world environments are often faced with the challenge of adapting models to concept drift, where the task data distributions are shifting over time. The problem becomes even more difficult…
Android malware is a spreading disease in the virtual world. Anti-virus and detection systems continuously undergo patches and updates to defend against these threats. Most of the latest approaches in malware detection use Machine Learning…
Malware remains a big threat to cyber security, calling for machine learning based malware detection. While promising, such detectors are known to be vulnerable to evasion attacks. Ensemble learning typically facilitates countermeasures,…
Machine learning models are being increasingly used to automate decisions in almost every domain, and ensuring the performance of these models is crucial for ensuring high quality machine learning enabled services. Ensuring concept drift is…
A recent report indicates that there is a new malicious app introduced every 4 seconds. This rapid malware distribution rate causes existing malware detection systems to fall far behind, allowing malicious apps to escape vetting efforts and…
In this research, we compare malware detection techniques based on static, dynamic, and hybrid analysis. Specifically, we train Hidden Markov Models (HMMs ) on both static and dynamic feature sets and compare the resulting detection rates…
The emergence of mobile platforms with increased storage and computing capabilities and the pervasive use of these platforms for sensitive applications such as online banking, e-commerce and the storage of sensitive information on these…
Mobile malware has been growing in scale and complexity as smartphone usage continues to rise. Android has surpassed other mobile platforms as the most popular whilst also witnessing a dramatic increase in malware targeting the platform. A…
In real-world Federated Learning (FL) deployments, data distributions on devices that participate in training evolve over time. This leads to asynchronous data drift, where different devices shift at different times and toward different…
With today's abundant streams of data, the only constant we can rely on is change. For stream classification algorithms, it is necessary to adapt to concept drift. This can be achieved by monitoring the model error, and triggering counter…
Millions of new pieces of malicious software (i.e., malware) are introduced each year. This poses significant challenges for antivirus vendors, who use machine learning to detect and analyze malware, and must keep up with changes in the…
A trained ML model is deployed on another `test' dataset where target feature values (labels) are unknown. Drift is distribution change between the training and deployment data, which is concerning if model performance changes. For a…
Supervised Deep Learning requires plenty of labeled data to converge, and hence perform optimally for task-specific learning. Therefore, we propose a novel mechanism named DRo (for Deep Routing) for data-scarce domains like security. The…
Many studies have proposed machine-learning (ML) models for malware detection and classification, reporting an almost-perfect performance. However, they assemble ground-truth in different ways, use diverse static- and dynamic-analysis…
With the rapid advancement of machine learning (ML), ML-based Android malware detection has gained significant popularity due to its ability to automatically learn malicious patterns from Android apps. However, the lack of an in-depth and…
Since Google unveiled Android OS for smartphones, malware are thriving with 3Vs, i.e. volume, velocity, and variety. A recent report indicates that one out of every five business/industry mobile application leaks sensitive personal data.…