Behind the Bait: Delving into PhishTank's hidden data.

Phishing constitutes a form of social engineering that aims to deceive individuals through email communication. Extensive prior research has underscored phishing as one of the most commonly employed attack vectors for infiltrating organizational networks. A prevalent method involves misleading the target by employing phishing URLs concealed through hyperlink strategies. PhishTank, a website employing the concept of crowd-sourcing, aggregates phishing URLs and subsequently verifies their authenticity. In the course of this study, we leveraged a Python script to extract data from the PhishTank website, amassing a comprehensive dataset comprising over 190,0000 phishing URLs. This dataset is a valuable resource that can be harnessed by both researchers and practitioners for enhancing phish- ing filters, fortifying firewalls, security education, and refining training and testing models, among other applications.

Quality assuring the quality assurance tool: applying safety-critical concepts to test framework development.

The quality of embedded systems is demonstrated by the performed tests. The quality of such tests is often dependent on the quality of one or more testing tools, especially in automated testing. Test automation is also central to the success of agile development. It is thus critical to ensure the quality of testing tools. This work explores how industries with agile processes can learn from safety-critical system development with regards to the quality assurance of the test framework development. Safety-critical systems typically need adherence to safety standards that often suggests substantial upfront documentation, plans and a long-term perspective on several development aspects. In contrast, agile approaches focus on quick adaptation, evolving software and incremental deliveries. This article identifies several approaches of quality assurance of software development tools in functional safety development and agile development. The extracted approaches are further analyzed and processed into candidate solutions, i.e., principles and practices for the test framework quality assurance applicable in an industrial context. An industrial focus group with experienced practitioners further validated the candidate solutions through moderated group discussions. The two main contributions from this study are: (i) 48 approaches and 25 derived candidate solutions for test framework quality assurance in four categories (development, analysis, run-time measures, and validation and verification) with related insights, e.g., a test framework should be perceived as a tool-chain and not a single tool, (ii) the perceived value of the candidate solutions in industry as collected from the focus group.

Optimum Design of PIλDµ Controller for an Automatic Voltage Regulator System Using Combinatorial Test Design.

Combinatorial test design is a plan of test that aims to reduce the amount of test cases systematically by choosing a subset of the test cases based on the combination of input variables. The subset covers all possible combinations of a given strength and hence tries to match the effectiveness of the exhaustive set. This mechanism of reduction has been used successfully in software testing research with t-way testing (where t indicates the interaction strength of combinations). Potentially, other systems may exhibit many similarities with this approach. Hence, it could form an emerging application in different areas of research due to its usefulness. To this end, more recently it has been applied in a few research areas successfully. In this paper, we explore the applicability of combinatorial test design technique for Fractional Order (FO), Proportional-Integral-Derivative (PID) parameter design controller, named as FOPID, for an automatic voltage regulator (AVR) system. Throughout the paper, we justify this new application theoretically and practically through simulations. In addition, we report on first experiments indicating its practical use in this field. We design different algorithms and adapted other strategies to cover all the combinations with an optimum and effective test set. Our findings indicate that combinatorial test design can find the combinations that lead to optimum design. Besides this, we also found that by increasing the strength of combination, we can approach to the optimum design in a way that with only 4-way combinatorial set, we can get the effectiveness of an exhaustive test set. This significantly reduced the number of tests needed and thus leads to an approach that optimizes design of parameters quickly.