Imbalanced data classification and its application in cyber security

Abstract

Cyber security, also known as information technology security or simply as information security, aims to protect government organizations, companies and individuals by defending their computers, servers, electronic systems, networks, and data from malicious attacks. With the advancement of client-side on the fly web content generation techniques, it becomes easier for attackers to modify the content of a website dynamically and gain access to valuable information. The impact of cybercrime to the global economy is now more than ever, and it is growing day by day. Among various types of cybercrimes, financial attacks are widely spread and the financial sector is among most targeted. Both corporations and individuals are losing a huge amount of money each year. The majority portion of financial attacks is carried out by banking malware and web-based attacks. The end users are not always skilled enough to differentiate between injected content and actual contents of a webpage. Designing a real-time security system for ensuring a safe browsing experience is a challenging task. Some of the existing solutions are designed for client side and all the users have to install it in their system, which is very difficult to implement. In addition, various platforms and tools are used by organizations and individuals, therefore, different solutions are needed to be designed. The existing server-side solution often focuses on sanitizing and filtering the inputs. It will fail to detect obfuscated and hidden scripts. This is a realtime security system and any significant delay will hamper user experience. Therefore, finding the most optimized and efficient solution is very important. To ensure an easy installation and integration capabilities of any solution with the existing system is also a critical factor to consider. If the solution is efficient but difficult to integrate, then it may not be a feasible solution for practical use. Unsupervised and supervised data classification techniques have been widely applied to design algorithms for solving cyber security problems. The performance of these algorithms varies depending on types of cyber security problems and size of datasets. To date, existing algorithms do not achieve high accuracy in detecting malware activities. Datasets in cyber security and, especially those from financial sectors, are predominantly imbalanced datasets as the number of malware activities is significantly less than the number of normal activities. This means that classifiers for imbalanced datasets can be used to develop supervised data classification algorithms to detect malware activities. Development of classifiers for imbalanced data sets has been subject of research over the last decade. Most of these classifiers are based on oversampling and undersampling techniques and are not efficient in many situations as such techniques are applied globally. In this thesis, we develop two new algorithms for solving supervised data classification problems in imbalanced datasets and then apply them to solve malware detection problems. The first algorithm is designed using the piecewise linear classifiers by formulating this problem as an optimization problem and by applying the penalty function method. More specifically, we add more penalty to the objective function for misclassified points from minority classes. The second method is based on the combination of the supervised and unsupervised (clustering) algorithms. Such an approach allows one to identify areas in the input space where minority classes are located and to apply local oversampling or undersampling. This approach leads to the design of more efficient and accurate classifiers. The proposed algorithms are tested using real-world datasets. Results clearly demonstrate superiority of newly introduced algorithms. Then we apply these algorithms to design classifiers to detect malwares.Doctor of Philosoph