Neural Machine Translation Inspired Binary Code Similarity Comparison beyond Function Pairs

Binary code analysis allows analyzing binary code without having access to the corresponding source code. A binary, after disassembly, is expressed in an assembly language. This inspires us to approach binary analysis by leveraging ideas and techniques from Natural Language Processing (NLP), a rich area focused on processing text of various natural languages. We notice that binary code analysis and NLP share a lot of analogical topics, such as semantics extraction, summarization, and classification. This work utilizes these ideas to address two important code similarity comparison problems. (I) Given a pair of basic blocks for different instruction set architectures (ISAs), determining whether their semantics is similar or not; and (II) given a piece of code of interest, determining if it is contained in another piece of assembly code for a different ISA. The solutions to these two problems have many applications, such as cross-architecture vulnerability discovery and code plagiarism detection. We implement a prototype system INNEREYE and perform a comprehensive evaluation. A comparison between our approach and existing approaches to Problem I shows that our system outperforms them in terms of accuracy, efficiency and scalability. And the case studies utilizing the system demonstrate that our solution to Problem II is effective. Moreover, this research showcases how to apply ideas and techniques from NLP to large-scale binary code analysis.Comment: Accepted by Network and Distributed Systems Security (NDSS) Symposium 201

Passive and Active Currency Portfolio Optimisation

This thesis examines the performance of currency-only portfolios with different strategies, in out-of-sample analysis. I first examine a number of passive portfolio strategies into currency market in out-of-sample analysis. The strategies I applied in this chapter include sample-based mean-variance portfolio and its extension, minimum variance portfolio, and equally-weighted risk contribution model. Moreover, I consider GDP portfolio and Trade portfolio as market value portfolio for currency market. With naïve portfolio, there are 12 different asset allocation models. In my out-of-sample analysis, naïve portfolio performs reasonably well among all 12 portfolios, and transaction cost does not seriously affect the results prior to transaction cost analysis. The results are robust across different estimation windows and perspectives of investors from different countries. Next, more portfolio strategies are examined to compare with naïve portfolio in currency market. The first portfolio strategy called ‘optimal constrained portfolio’ in this chapter is derived from the idea of maximising the quadratic utility function. In addition, the timing strategies, a set of simple active portfolio strategies, are also considered. In my out-of-sample analysis with rolling sample approach, naïve portfolio can be beaten by all the strategies discussed in this chapter. In chapter six, the characteristics of currency are exploited to construct a currency only portfolio. Firstly, the pre-sample test proves that the characteristics, both fundamental and financial, are relevant to the portfolio construction. I then examine the performance of parametric portfolio policies. The results show that while fundamental characteristics can bring investor benefits of active portfolio management, financial characteristics cannot. Moreover, I find the relationship between characteristics of currency and weights of optimal portfolio. The overall results show that currencies can be thought of as an asset in their own right to construct optimal portfolios, which have better performance than naïve portfolio, if suitable strategies are used. In addition, ‘lesser’ currencies, indeed, bring significant benefits to the investors

Fuzzy Interpolation Systems and Applications

Fuzzy inference systems provide a simple yet effective solution to complex non-linear problems, which have been applied to numerous real-world applications with great success. However, conventional fuzzy inference systems may suffer from either too sparse, too complex or imbalanced rule bases, given that the data may be unevenly distributed in the problem space regardless of its volume. Fuzzy interpolation addresses this. It enables fuzzy inferences with sparse rule bases when the sparse rule base does not cover a given input, and it simplifies very dense rule bases by approximating certain rules with their neighbouring ones. This chapter systematically reviews different types of fuzzy interpolation approaches and their variations, in terms of both the interpolation mechanism (inference engine) and sparse rule base generation. Representative applications of fuzzy interpolation in the field of control are also revisited in this chapter, which not only validate fuzzy interpolation approaches but also demonstrate its efficacy and potential for wider applications

Designing a Security System Administration Course for Cybersecurity with a Companion Project

In the past few years, an incident response-oriented cybersecurity program has been constructed at University of Central Oklahoma. As a core course in the newly-established curricula, Secure System Administration focuses on the essential knowledge and skill set for system administration. To enrich students with hands-on experience, we also develop a companion coursework project, named PowerGrader. In this paper, we present the course structure as well as the companion project design. Additionally, we survey the pertinent criterion and curriculum requirements from the widely recognized accreditation units. By this means, we demonstrate the importance of a secure system administration course within the context of cybersecurity educationComment: Accepted by the 37th Annual CCSC: Southeastern Conferenc

Gaze-Informed egocentric action recognition for memory aid systems

Egocentric action recognition has been intensively studied in the fields of computer vision and clinical science with applications in pervasive health-care. The majority of the existing egocentric action recognition techniques utilize the features extracted from either the entire contents or the regions of interest in video frames as the inputs of action classifiers. The former might suffer from moving backgrounds or irrelevant foregrounds usually associated with egocentric action videos, while the latter may be impaired by the mismatch between the calculated and the ground truth regions of interest. This paper proposes a new gaze-informed feature extraction approach, by which the features are extracted from the regions around the gaze points and thus representing the genuine regions of interest from a first person of view. The activity of daily life can then be classified based only on the identified regions using the extracted gaze-informed features. The proposed approach has been further applied to a memory support system for people with poor memory, such as those with Amnesia or dementia, and their carers. The experimental results demonstrate the efficacy of the proposed approach in egocentric action recognition and thus the potential of the memory support tool in health care