In this paper, we consider the relevance of timeline in the construction of datasets, to highlight its impact on the performance of a machine learning-based malware detection scheme. Typically, we show that simply picking a random set of known malware to train a malware detector, as it is done in many assessment scenarios from the literature, yields significantly biased results. In the process ...

Rhee, Junghwan Ph.D., Purdue University, August 2011. Data-Centric Approaches to Kernel Malware Defense. Major Professor: Dongyan Xu. An operating system kernel is the core of system software which is responsible for the integrity and operations of a conventional computer system. Authors of malicious software (malware) have been continuously exploring various attack vectors to tamper with the k...

Recently, a malware is growing rapidly and the number of malware applies various techniques to protect itself from the anti-virus solution detection. The reason of this phenomenon is that a longer resident on an infected host guarantees the more profit. As a result, these many protection techniques are applied to a malware, a representative of those is a Packing. It is not an exaggeration that ...

We present a novel malware detection approach based on metrics over quantitative data flow graphs. Quantitative data flow graphs (QDFGs) model process behavior by interpreting issued system calls as aggregations of quantifiable data flows. Due to the high abstraction level we consider QDFG metric based detection more robust against typical behavior obfuscation like bogus call injection or call ...

Recent researches have shown that machine learning based malware detection algorithms are very vulnerable under the attacks of adversarial examples. These works mainly focused on the detection algorithms which use features with fixed dimension, while some researchers have begun to use recurrent neural networks (RNN) to detect malware based on sequential API features. This paper proposes a novel...

CHI-SQUARED DISTANCE AND METAMORPHIC VIRUS DETECTION by Annie H. Toderici Malware are programs that are designed with a malicious intent. Metamorphic malware change their internal structure each generation while still maintaining their original behavior. As metamorphic malware become more sophisticated, it is important to develop efficient and accurate detection techniques. Current commercial a...

Rhee, Junghwan Ph.D., Purdue University, August 2011. Data-Centric Approaches to Kernel Malware Defense. Major Professor: Dongyan Xu. An operating system kernel is the core of system software which is responsible for the integrity and operations of a conventional computer system. Authors of malicious software (malware) have been continuously exploring various attack vectors to tamper with the k...

Machine learning has been used to detect new malware in recent years, while malware authors have strong motivation to attack such algorithms.Malware authors usually have no access to the detailed structures and parameters of the machine learning models used by malware detection systems, and therefore they can only perform black-box attacks. This paper proposes a generative adversarial network (...

Malware is every malicious code that has the potential to harm any computer or network. The amount of malware is increasing faster every year and poses a serious security threat. Hence, malware detection has become a critical topic in computer security. Currently, signature-based detection is the most extended method within commercial antivirus. Although this method is still used on most popula...