A Comparison of Clustering Techniques for Malware Analysis

In this research, we apply clustering techniques to the malware detection problem. Our goal is to classify malware as part of a fully automated detection strategy. We compute clusters using the well-known �-means and EM clustering algorithms, with scores obtained from Hidden Markov Models (HMM). The previous work in this area consists of using HMM and �-means clustering technique to achieve the same. The current effort aims to extend it to use EM clustering technique for detection and also compare this technique with the �-means clustering

Novel methods for multi-view learning with applications in cyber security

Modern data is complex. It exists in many different forms, shapes and kinds. Vectors, graphs, histograms, sets, intervals, etc.: they each have distinct and varied structural properties. Tailoring models to the characteristics of various feature representations has been the subject of considerable research. In this thesis, we address the challenge of learning from data that is described by multiple heterogeneous feature representations. This situation arises often in cyber security contexts. Data from a computer network can be represented by a graph of user authentications, a time series of network traffic, a tree of process events, etc. Each representation provides a complementary view of the holistic state of the network, and so data of this type is referred to as multi-view data. Our motivating problem in cyber security is anomaly detection: identifying unusual observations in a joint feature space, which may not appear anomalous marginally. Our contributions include the development of novel supervised and unsupervised methods, which are applicable not only to cyber security but to multi-view data in general. We extend the generalised linear model to operate in a vector-valued reproducing kernel Hilbert space implied by an operator-valued kernel function, which can be tailored to the structural characteristics of multiple views of data. This is a highly flexible algorithm, able to predict a wide variety of response types. A distinguishing feature is the ability to simultaneously identify outlier observations with respect to the fitted model. Our proposed unsupervised learning model extends multidimensional scaling to directly map multi-view data into a shared latent space. This vector embedding captures both commonalities and disparities that exist between multiple views of the data. Throughout the thesis, we demonstrate our models using real-world cyber security datasets.Open Acces

Personality Identification from Social Media Using Deep Learning: A Review

Social media helps in sharing of ideas and information among people scattered around the world and thus helps in creating communities, groups, and virtual networks. Identification of personality is significant in many types of applications such as in detecting the mental state or character of a person, predicting job satisfaction, professional and personal relationship success, in recommendation systems. Personality is also an important factor to determine individual variation in thoughts, feelings, and conduct systems. According to the survey of Global social media research in 2018, approximately 3.196 billion social media users are in worldwide. The numbers are estimated to grow rapidly further with the use of mobile smart devices and advancement in technology. Support vector machine (SVM), Naive Bayes (NB), Multilayer perceptron neural network, and convolutional neural network (CNN) are some of the machine learning techniques used for personality identification in the literature review. This paper presents various studies conducted in identifying the personality of social media users with the help of machine learning approaches and the recent studies that targeted to predict the personality of online social media (OSM) users are reviewed