Android Malware has emerged as a consequence of the increasing popularity of smartphones and tablets. While most previous work focuses on inherent characteristics of Android apps to detect malware, this study analyses indirect features and meta-data to identify patterns in malware applications. Our experiments show that: (1) the permissions used by an application offer only moderate performance...

The ever-growing malware threat in the cyber space calls for techniques that are more effective than widely deployed signature-based detection systems and more scalable than manual reverse engineering by forensic experts. To counter large volumes of malware variants, machine learning techniques have been applied recently for automated malware classification. Despite the successes made from thes...

Machine learning is a popular approach to signatureless malware detection because it can generalize to never-before-seen malware families and polymorphic strains. This has resulted in its practical use for either primary detection engines or for supplementary heuristic detection by anti-malware vendors. Recent work in adversarial machine learning has shown that deep learning models are suscepti...

We propose a malware detection approach based on the characteristic behaviors of human users. We explore the humanmalware differences and utilize them to aid the detection of infected hosts. There are two main research challenges in this study: one is how to select characteristic behavior features, and the other is how to prevent malware forgeries. We aim to address both questions in this poster.

Malware is a pervasive problem in both personal computing devices and distributed computing systems. Identification of malware variants and their families offers a great benefit in early detection resulting in a reduction of the analyses time needed. In order to classify malware, most of the current approaches are based on the analysis of the unpacked and unencrypted binaries. However, most of ...

Device-to-Device (D2D) communication is expected to be a key feature supported by 5G networks, especially due to the proliferation of Mobile Edge Computing (MEC), which has a prominent role in reducing network stress by shifting computational tasks from the Internet to the mobile edge. Apart from being part of MEC, D2D can extend cellular coverage allowing users to communicate directly when tel...

With malware detection techniques increasingly adopting machine learning approaches, the creation of precise training sets becomes more and more important. Large data sets of realistic web traffic, correctly classified as benign or malicious are needed, not only to train classic and deep learning algorithms, but also to serve as evaluation benchmarks for existing malware detection products. Int...

Smartphones and mobile tablets are rapidly becoming indispensable in daily life. Android has been the most popular mobile operating system since 2012. However, owing to the open nature of Android, countless malwares are hidden in a large number of benign apps in Android markets that seriously threaten Android security. Deep learning is a new area of machine learning research that has gained inc...

Identify a malicious data in a several types of files is a challenging task. Malware is a computer virus this is also a name given to a group of malicious data like umbrella to all types of malicious data like virus, worm, Trojan and so on. Several methods have been devised to smooth the progress of malware analysis and one of them is through visualization techniques. The visualization techniqu...