Malware Detection using Machine Learning and Deep Learning

Research shows that over the last decade, malware has been growing exponentially, causing substantial financial losses to various organizations. Different anti-malware companies have been proposing solutions to defend attacks from these malware. The velocity, volume, and the complexity of malware are posing new challenges to the anti-malware community. Current state-of-the-art research shows that recently, researchers and anti-virus organizations started applying machine learning and deep learning methods for malware analysis and detection. We have used opcode frequency as a feature vector and applied unsupervised learning in addition to supervised learning for malware classification. The focus of this tutorial is to present our work on detecting malware with 1) various machine learning algorithms and 2) deep learning models. Our results show that the Random Forest outperforms Deep Neural Network with opcode frequency as a feature. Also in feature reduction, Deep Auto-Encoders are overkill for the dataset, and elementary function like Variance Threshold perform better than others. In addition to the proposed methodologies, we will also discuss the additional issues and the unique challenges in the domain, open research problems, limitations, and future directions.Comment: 11 Pages and 3 Figure

AnyNovel: detection of novel concepts in evolving data streams: An application for activity recognition

A data stream is a flow of unbounded data that arrives continuously at high speed. In a dynamic streaming environment, the data changes over the time while stream evolves. The evolving nature of data causes essentially the appearance of new concepts. This novel concept could be abnormal such as fraud, network intrusion, or a sudden fall. It could also be a new normal concept that the system has not seen/trained on before. In this paper we propose, develop, and evaluate a technique for concept evolution in evolving data streams. The novel approach continuously monitors the movement of the streaming data to detect any emerging changes. The technique is capable of detecting the emergence of any novel concepts whether they are normal or abnormal. It also applies a continuous and active learning for assimilating the detected concepts in real time. We evaluate our approach on activity recognition domain as an application of evolving data streams. The study of the novel technique on benchmarked datasets showed its efficiency in detecting new concepts and continuous adaptation with low computational cost

Mind your step: the effects of mobile phone use on gaze behavior in stair climbing

Stair walking is a hazardous activity and a common cause of fatal and non-fatal falls. Previous studies have assessed the role of eye movements in stair walking by asking people to repeatedly go up and down stairs in quiet and controlled conditions, while the role of peripheral vision was examined by giving participants specific fixation instructions or working memory tasks. We here extend this research to stair walking in a natural environment with other people present on the stairs and a now common secondary task: Using one's mobile phone. Results show that using the mobile phone strongly draws one's attention away from the stairs, but that the distribution of gaze locations away from the phone is little influenced by using one's phone. Phone use also increased the time needed to walk the stairs, but handrail use remained low. These results indicate that limited foveal vision suffices for adequate stair walking in normal environments, but that mobile phone use has a strong influence on attention, which may pose problems when unexpected obstacles are encountered