Detecting unknown anomalous program behavior using API system calls

This paper presents the detection techniques of anomalous programs based on the analysis of their system call traces. We collect the API calls for the tested executable programs from Microsoft detour system and extract the features for our classification task using the previously established n-gram technique. We propose three different feature extraction approaches in this paper. These are frequency-based, time-based and a hybrid approach which actually combines the first two approaches. We use the well-known classifier algorithms in our experiments using WEKA interface to classify the malicious programs from the benign programs. Our empirical evidence demonstrates that the proposed feature extraction approaches can detect malicious programs over 88% which is quite promising for the contemporary similar research.

Program-level support for protecting an application from untrustworthy components

Many software applications extend their functionality by dynamically loading executable components into their allocated address space. Such components, exemplified by browser plugins and other software add-ons, not only enable reusability, but also promote programming simplicity, as they reside in the same address space as their host application, supporting easy sharing of complex data structures and pointers. However, such components are also often of unknown provenance and quality and may be riddled with accidental bugs or, in some cases, deliberately malicious code. Statistics show that such component failures account for a high percentage of software crashes and vulnerabilities. Enabling isolation of such fine-grained components is therefore necessary to increase the stability, security and resilience of computer programs. This thesis addresses this issue by showing how host applications can create isolation domains for individual components, while preserving the benefits of a single address space, via a new architecture for software isolation called LibVM. Towards this end, we define a specification which outlines the functional requirements for LibVM, identify the conditions under which these functional requirements can be met, define an abstract Application Programming Interface (API) that encompasses the general problem of isolating shared libraries, thus separating policy from mechanism, and prove its practicality with two concrete implementations based on hardware virtualization and system call interpositioning, respectively. The results demonstrate that hardware isolation minimises the difficulties encountered with software based approaches, while also reducing the size of the trusted computing base, thus increasing confidence in the solution’s correctness. This thesis concludes that, not only is it feasible to create such isolation domains for individual components, but that it should also be a fundamental operating system supported abstraction, which would lead to more stable and secure applications.

Malware detection: program run length against detection rate

N-gram analysis is an approach that investigates the structure of a program using bytes, characters or text strings. This research uses dynamic analysis to investigate malware detection using a classification approach based on N-gram analysis. A key issue with dynamic analysis is the length of time a program has to be run to ensure a correct classification. The motivation for this research is to find the optimum subset of operational codes (opcodes) that make the best indicators of malware and to determine how long a program has to be monitored to ensure an accurate support vector machine (SVM) classification of benign and malicious software. The experiments within this study represent programs as opcode density histograms gained through dynamic analysis for different program run periods. A SVM is used as the program classifier to determine the ability of different program run lengths to correctly determine the presence of malicious software. The findings show that malware can be detected with different program run lengths using a small number of opcodes

Malicious code detection architecture inspired by human immune system

Malicious code is a threat to computer systems globally. In this paper, we outline the evolution of malicious code attacks. The threat is evolving, leaving challenges for attackers to improve attack techniques and for researchers and security specialists to improve detection accuracy. We present a novel architecture for an effective defense against malicious code attack, inspired by the human immune system. We introduce two phases of program execution: Adolescent and Mature Phase. The first phase uses a malware profile matching mechanism, whereas the second phase uses a program profile matching mechanism. Both mechanisms are analogous to the innate immune system

The Design, Implementation and Outcomes of a Mentoring Program

This paper reports on the design, implementation and outcomes of a mentoring program involving 18 employees in the IT Division of WorkCover Queensland. The paper provides some background information to the development of the program and the design and implementation phases including recruitment and matching of participants, orientation and training, and the mentoring process including transition and/or termination. The paper also outlines the quantitative and qualitative evaluation processes that occurred and the outcomes of that evaluation. Results indicated a wealth of positive individual, mentoring, and organisational outcomes. The organisation and semi-structured processes provided in the program are considered as major contributing factors to the successful outcomes of the program. These outcomes are likely to have long-term benefits for the individuals involved, the IT Division, and the broader organisation