Nowadays, cyber-attacks have become a common and persistent issue affecting various human activities in modern societies. Due to the continuously evolving landscape of cyber-attacks and the growing concerns around “black box” models, there has been a strong demand for novel explainable and interpretable intrusion detection systems with online learning abilities. In this paper, a novel soft prototype-based autonomous fuzzy inference system (SPAFIS) is proposed for network intrusion detection . SPAFIS learns from network traffic data streams online on a chunk-by-chunk basis and autonomously identifies a set of meaningful, human-interpretable soft prototypes to build an IF-THEN fuzzy rule base for classification. Thanks to the utilization of soft prototypes, SPAFIS can precisely capture the underlying data structure and local patterns, and perform internal reasoning and decision-making in a human-interpretable manner based on the ensemble properties and mutual distances of data. To maintain a healthy and compact knowledge base , a pruning scheme is further introduced to SPAFIS, allowing itself to periodically examine the learned solution and remove redundant soft prototypes from its knowledge base. Numerical examples on public network intrusion detection datasets demonstrated the efficacy of the proposed SPAFIS in both offline and online application scenarios, outperforming the state-of-the-art alternatives.

With the continuous reduction of integrated circuit processing size and increasing integration density, power supply noise seriously threatens further improvement of high-speed digital system performance, which can cause jitter, latency, and even system malfunction. Power supply noise is strongly related to the design quality of the power distribution network and the activity of transient currents. In this paper, a method for estimating the worst-case voltage supply based on the ramp time of the transient current is proposed. At the same time, the worst-case current activity can also be obtained which can be used to guide the core or field programmable gate array (FPGA) design for a given power distribution network. The ramp current is first encoded and divided into segments. Then, two dynamic matrices are used to track the worst-case accumulated voltage noise and worst-case current activities. The proposed method is compared with state-of-the-art approaches showing consistent advantages in estimating the worst-case power supply noise. Its accuracy is also validated by comparing the simulated and measured data.

Passwords remain the most widely used form of user authentication, despite advancements in other methods. However, their limitations, such as susceptibility to attacks, especially weak passwords defined by human users, are well-documented. The existence of weak human-defined passwords has led to repeated password leaks from websites, many of which are of large scale. While such password leaks are unfortunate security incidents, they provide security researchers and practitioners with good opportunities to learn valuable insights from such leaked passwords, in order to identify ways to improve password policies and other security controls on passwords. Researchers have proposed different data visualisation techniques to help analyse leaked passwords. However, many approaches rely solely on frequency analysis, with limited exploration of distance-based graphs. This paper reports PassViz, a novel method that combines the edit distance with the t-SNE (t-distributed stochastic neighbour embedding) dimensionality reduction algorithm [19] for visualising and analysing leaked passwords in a 2-D space. We implemented PassViz as an easy-to-use command-line tool for visualising large-scale password databases, and also as a graphical user interface (GUI) to support interactive visual analytics of small password databases. Using the “000webhost” leaked database as an example, we show how PassViz can be used to visually analyse different aspects of leaked passwords and to facilitate the discovery of previously unknown password patterns. Overall, our approach empowers researchers and practitioners to gain valuable insights and improve password security through effective data visualisation and analysis.

Cognitive modeling tools have been widely used by researchers and practitioners to help design, evaluate, and study computer user interfaces (UIs). Despite their usefulness, large-scale modeling tasks can still be very challenging due to the amount of manual work needed. To address this scalability challenge, we propose CogTool+, a new cognitive modeling software framework developed on top of the well-known software tool CogTool. CogTool+ addresses the scalability problem by supporting the following key features: (1) a higher level of parameterization and automation; (2) algorithmic components; (3) interfaces for using external data; and (4) a clear separation of tasks, which allows programmers and psychologists to define reusable components (e.g., algorithmic modules and behavioral templates) that can be used by UI/UX researchers and designers without the need to understand the low-level implementation details of such components. CogTool+ also supports mixed cognitive models required for many large-scale modeling tasks and provides an offline analyzer of simulation results. In order to show how CogTool+ can reduce the human effort required for large-scale modeling, we illustrate how it works using a pedagogical example, and demonstrate its actual performance by applying it to large-scale modeling tasks of two real-world user-authentication systems.

Observation Resilient Authentication Schemes (ORAS) are a class of shared secret challenge–response identification schemes where a user mentally computes the response via a cognitive function to authenticate herself such that eavesdroppers cannot readily extract the secret. Security evaluation of ORAS generally involves quantifying information leaked via observed challenge–response pairs. However, little work has evaluated information leaked via human behavior while interacting with these schemes. A common way to achieve observation resilience is by including a modulus operation in the cognitive function. This minimizes the information leaked about the secret due to the many-to-one map from the set of possible secrets to a given response. In this work, we show that user behavior can be used as a side channel to obtain the secret in such ORAS. Specifically, the user’s eye-movement patterns and associated timing information can deduce whether a modulus operation was performed (a fundamental design element) to leak information about the secret. We further show that the secret can still be retrieved if the deduction is erroneous, a more likely case in practice. We treat the vulnerability analytically and propose a generic attack algorithm that iteratively obtains the secret despite the “faulty” modulus information. We demonstrate the attack on five ORAS and show that the secret can be retrieved with considerably less challenge–response pairs than non-side-channel attacks (e.g., algebraic/statistical attacks). In particular, our attack is applicable on Mod10, a one-time-pad-based scheme, for which no non-side-channel attack exists. We field test our attack with a small-scale eye-tracking user study.

Quality of Experience has recently become a paramount research topic in multimedia systems, especially in the emerging areas of high-definition and 3D video content. There has been a significant amount of research focusing on 3D content production, compression and delivery. However very little research has been dedicated to the emerging challenges in assessing user experience when interacting with the 3D video content. Interaction tasks such as pointing and selection are critical to the consumer's experience of the 3D video technology. This paper studies the impact of pointing modalities on the quality of interaction experience with stereoscopic 3D television. The conducted user study compares and evaluates three pointing modalities: standard mouse-based interaction, virtual laser pointer and hand gesture modality, using the ISO 9241-9 standard for multi-directional tapping task. The results suggest that the virtual laser pointer modality can provide better quality of interaction experience than other modalities in terms of user performance and user satisfaction.

The recent proliferation of stereoscopic three dimensional (3D) video technology has fostered a large body of research into 3D video capture, production, compression and delivery. However, little research has been dedicated to the design practices of stereoscopic 3D video interaction. Interaction tasks such as pointing and selection are critical to the consumer's experience of the 3D video technology. This paper presents investigation of pointing modalities in the context of stereoscopic 3D television (TV). Adopting the ISO 9241-9 standard for multi-directional tapping task, the conducted user study compares and evaluates three pointing modalities: standard mouse-based interaction, virtual laser pointer implemented using Wiimote, and hand movement modality using Kinect. The results suggest that the virtual laser pointer modality is more advantageous than other modalities in terms of user performance and user comfort. In addition, this paper discusses the impact of disparity levels on the pointing tasks.