Wireless network security testing involves evaluating the security measures and protocols of a wireless network to identify vulnerabilities and ensure the network is protected against unauthorized access and attacks. The testing process typically includes several steps and techniques, and so benefit greatly from test automation with OpenTAP
Every year for the last four, Keysight has collaborated with faculty at the University of California Santa Cruz (UCSC) Baskin Engineering School to sponsor senior projects in test automation. This year (Spring 2024), one of those projects focused on leveraging LLMs (Large Language Models) to streamline the creation of OpenTAP plugins in Python.
Penetration testing (a.k.a. "pen testing") plays a crucial role in identifying and mitigating security risks. It helps organizations strengthen defenses, protect sensitive data, and improve overall security posture. By leveraging test automation in general and OpenTAP in particular in penetration testing, organizations can improve the efficiency, accuracy, and effectiveness of their security testing efforts, helping them identify and remediate security vulnerabilities before they can be exploited by attackers.
Test automation, like the technology and products it targets, is in a state of constant change.
MarketsAndMarkets reports that the global test automation market size is expected to grow from USD 24.7 billion in 2022 to USD 52.7 billion by 2027, at a CAGR of 16.4%. To accompany the pace of the ever-changing testing landscape, you should be familiar with the latest test automation trends.
This second blog in our series on using OpenTAP for security testing, we take a deep dive into Vulnerability Assessment.
A software vulnerability describes a weakness or flaw in a software system that can be exploited by attackers to compromise the security of the system, its data, or its users. Vulnerabilities can exist in various components of software, including operating systems, drivers, applications, libraries, and firmware.
A source-available license grants users access to source code, allowing them to view, modify, and sometimes distribute the code. However, there may be restrictions or limitations imposed. These restrictions most often include limitations on commercial use, restrictions on redistributing modified versions, or requirements to share modifications with the original developer without necessarily making them public.
Dual Licensing (or multi-licensing) is the practice of releasing source code under multiple licenses. Most open source software is published and distributed under the rubric of a single license: Apache, GPL, Mozilla or about one hundred other licenses recognized by the Open Source Initiative.
Dual licensing, while adding complexity to use and management of open source software, serves several needs and application use cases:
In this era of technological advancement, a key debate centers around the use of internal tools, traditional closed-source solutions, or the untapped potential of open source. Historically, internally-developed "roll-your-own" (RYO) tools have offered testers greater control and a higher degree of adaptability than those acquired from third-parties. However, as testing scenarios grow in complexity, the imperative for a more streamlined and collaborative approach becomes challenges original assumptions
Every year brings changes in the open source ecosystem. Myriad new projects, new applications and frameworks, new foundation working groups and new business trends. 2023 was no exception.
Open source software is no longer viewed as a novelty; indeed it is today thoroughly mainstream, to the point of banality. Nonetheless, the emergence of new projects and the progress of existing ones, the use of open source licenses, the role of open source code and the ups and downs of the open source ecosystem continue to present both challenges and opportunities.
Artificial Intelligence (AI) is a focal domain for developers, for end-users and for the venture capital community. It’s as hot a commodity as Linux and open source were two decades ago. But AI and open source share more than just hype. Across natural language processing (NLP), Machine Learning (ML), Computer Vision, and Robotics, both AI and open source drive the democratization of technology, and open source is helping to drive the utility and ubiquity of AI platforms and applications.
Cybersecurity sits at top of mind for IT professionals and these days, even for device manufacturers. Cybersecurity testing includes a range of assessments and evaluations that focus on various aspects of security posture. These tests help identify vulnerabilities, weaknesses, and potential threats in systems, networks, and applications.
This first blog in a series calls out the various types of cybersecurity testing and how and if OpenTAP can facilitate and control each.
