The challenge in engaging malware activities involves the correct identification and classification of different malware variants. Various malwares incorporate code obfuscation methods that alters their code signatures effectively countering antimalware detection techniques utilizing static methods and signature database. In this study, we utilized an approach of converting a malware binary int...

The meteoric growth of the Android mobile platform has made it a main target of cyber-criminals. Mobile malware specifically targeting Android has surged and grown in tandem with the rising popularity of the platform [3, 5, 4, 6]. In response, the honus is on defenders to increase the difficulty of malware development to curb its rampant growth, and to devise effective detection mechanisms spec...

Run-time malware detection strategies are efficient and robust, which get more and more attention. In this paper, we use I/O Request Package (IRP) sequences for malware detection. N-gram will be used to analyze IRP sequences for feature extraction. Integrated use of Negative Selection Algorithm (NSA) and Positive Selection Algorithm (PSA), we get more than 96% true positive rate and 0% false po...

Software security is a big data problem. The volume of new software artifacts created far outpaces the current capacity of software analysis. This gap has brought an urgent challenge to our security community—scalability. If our techniques cannot cope with an ever increasing volume of software, we will always be one step behind attackers. Thus developing scalable analysis to bridge the gap is e...

Due to the rapid advancement of mobile communication technology, mobile devices nowadays can support a variety of data services that are not traditionally available. With the growing popularity of mobile devices in the last few years, attacks targeting them are also surging. Existing mobile malware detection techniques, which are often borrowed from solutions to Internet malware detection, do n...

In recent years, the diffusion of malicious software through various channels has gained the request for intelligent techniques capable of timely detecting new malware spread. In this work, we focus on the application of Deep Learning methods for malware detection, by evaluating their effectiveness when malware are represented by high-level, and lowlevel features respectively. Experimental resu...

An enormous number of malware samples pose a major threat to our networked society. Antivirus software and intrusion detection systems are widely implemented on the hosts and networks as fundamental countermeasures. However, they may fail to detect evasive malware. Thus, setting a high priority for new varieties of malware is necessary to conduct in-depth analyses and take preventive measures. ...

This master thesis presents overview on advanced persistent threat (APT) definition and explanation of it. One of the most dangerous APT named: ”Snake” will be presented along with other similar APT’s. Various virtual environments like e.g. VirtualBox will be investigated in order to understand how APT malware behaves in these environments. The central focus of this master thesis lies on detect...

Mobile devices, such as smartphones, have become an important part of modern lives. However, as these devices have tremendously become popular they are attracting a range of attacks. Malware is one of the serious threats posed to smartphones by the attackers. Due to the limited resources of mobile devices malware detection on these devices remains a challenge. Malware detection techniques based...