The Wyner Variational Autoencoder for Unsupervised Multi-Layer Wireless Fingerprinting

Wireless fingerprinting refers to a device identification method leveraging hardware imperfections and wireless channel variations as signatures. Beyond physical layer characteristics, recent studies demonstrated that user behaviours could be identified through network traffic, e.g., packet length, without decryption of the payload. Inspired by these results, we propose a multi-layer fingerprinting framework that jointly considers the multi-layer signatures for improved identification performance. In contrast to previous works, by leveraging the recent multi-view machine learning paradigm, i.e., data with multiple forms, our method can cluster the device information shared among the multi-layer features without supervision. Our information-theoretic approach can be extended to supervised and semi-supervised settings with straightforward derivations. In solving the formulated problem, we obtain a tight surrogate bound using variational inference for efficient optimization. In extracting the shared device information, we develop an algorithm based on the Wyner common information method, enjoying reduced computation complexity as compared to existing approaches. The algorithm can be applied to data distributions belonging to the exponential family class. Empirically, we evaluate the algorithm in a synthetic dataset with real-world video traffic and simulated physical layer characteristics. Our empirical results show that the proposed method outperforms the state-of-the-art baselines in both supervised and unsupervised settings

Factors of Employee’s E-Learning Effectiveness: A Multi-Level Study Based on Socio-Technical Systems Theory

Application of e-learning in enterprises provides the advantages of lower training cost, richer learning content, higher information consistency, and easier update of content. Despite the fact that enterprises have the intention to introduce e-learning, there is not a complete framework to which they can refer to ensure the benefits of e-learning for employee training or learning and understand which important factors affect employee’s e-learning effectiveness. Relative to the difficulties of introducing e-learning in management practice, the academic achievements in this aspect also seem very limited. Most the existing papers are focused on discussion and survey of e-learning in school, and very few of them are dedicated to empirical research of e-learning in corporate environment. Besides, these studies discuss e-learning only at the technical or the individual level without a comprehensive investigation into the factors affecting e-learning effectiveness with multi-level theoretic framework. This paper applies the socio-technical systems theory to review and integrate theories about employee e-learning from a macro view. To make up the insufficiency of related research, literature review and case research are conducted first. Based on the interview results, an analysis model is constructed to thoroughly explore factors affecting employee’s e-learning effectiveness. Later, through a questionnaire survey on employees’ adoption of e-learning and subsequent multi-level data analysis, hypotheses on the relationship of the influencing factors and the research model are verified. Results show that e-learning effectiveness (usefulness of e-learning, continuance intention to use, and e-learning performance) is simultaneously or alternately affected by direct or moderating factors of the technical system and the social system at the work environment level and the individual level. Compared with the existing research, this paper uses a more comprehensive system view to construct the theoretical model and empirically verify it. The results can be a reference for future researchers and managers of e-learning in enterprises

Game Theory Meets Network Security: A Tutorial at ACM CCS

The increasingly pervasive connectivity of today's information systems brings up new challenges to security. Traditional security has accomplished a long way toward protecting well-defined goals such as confidentiality, integrity, availability, and authenticity. However, with the growing sophistication of the attacks and the complexity of the system, the protection using traditional methods could be cost-prohibitive. A new perspective and a new theoretical foundation are needed to understand security from a strategic and decision-making perspective. Game theory provides a natural framework to capture the adversarial and defensive interactions between an attacker and a defender. It provides a quantitative assessment of security, prediction of security outcomes, and a mechanism design tool that can enable security-by-design and reverse the attacker's advantage. This tutorial provides an overview of diverse methodologies from game theory that includes games of incomplete information, dynamic games, mechanism design theory to offer a modern theoretic underpinning of a science of cybersecurity. The tutorial will also discuss open problems and research challenges that the CCS community can address and contribute with an objective to build a multidisciplinary bridge between cybersecurity, economics, game and decision theory

Communication Theoretic Data Analytics

Widespread use of the Internet and social networks invokes the generation of big data, which is proving to be useful in a number of applications. To deal with explosively growing amounts of data, data analytics has emerged as a critical technology related to computing, signal processing, and information networking. In this paper, a formalism is considered in which data is modeled as a generalized social network and communication theory and information theory are thereby extended to data analytics. First, the creation of an equalizer to optimize information transfer between two data variables is considered, and financial data is used to demonstrate the advantages. Then, an information coupling approach based on information geometry is applied for dimensionality reduction, with a pattern recognition example to illustrate the effectiveness. These initial trials suggest the potential of communication theoretic data analytics for a wide range of applications.Comment: Published in IEEE Journal on Selected Areas in Communications, Jan. 201

Multi-Layer Cyber-Physical Security and Resilience for Smart Grid

The smart grid is a large-scale complex system that integrates communication technologies with the physical layer operation of the energy systems. Security and resilience mechanisms by design are important to provide guarantee operations for the system. This chapter provides a layered perspective of the smart grid security and discusses game and decision theory as a tool to model the interactions among system components and the interaction between attackers and the system. We discuss game-theoretic applications and challenges in the design of cross-layer robust and resilient controller, secure network routing protocol at the data communication and networking layers, and the challenges of the information security at the management layer of the grid. The chapter will discuss the future directions of using game-theoretic tools in addressing multi-layer security issues in the smart grid.Comment: 16 page