Security controls are mechanisms or policies designed for cloud based services to reduce risk, protect information, and ensure compliance with security regulations. The development of security controls is traditionally a labor-intensive and time-consuming process. This paper explores the use of Generative AI to accelerate the generation of security controls. We specifically focus on generating Gherkin codes which are the domain-specific language used to define the behavior of security controls in a structured and understandable format. By leveraging large language models and in-context learning, we propose a structured framework that reduces the time required for developing security controls from 2-3 days to less than one minute. Our approach integrates detailed task descriptions, step-by-step instructions, and retrieval-augmented generation to enhance the accuracy and efficiency of the generated Gherkin code. Initial evaluations on AWS cloud services demonstrate promising results, indicating that GenAI can effectively streamline the security control development process, thus providing a robust and dynamic safeguard for cloud-based infrastructures.

Hardware Trojans (HTs) are undesired design or manufacturing modifications that can severely alter the security and functionality of digital integrated circuits. HTs can be inserted according to various design criteria, e.g., nets switching activity, observability, controllability, etc. However, to our knowledge, most HT detection methods are only based on a single criterion, i.e., nets switching activity. This paper proposes a multi-criteria reinforcement learning (RL) HT detection tool that features a tunable reward function for different HT detection scenarios. The tool allows for exploring existing detection strategies and can adapt new detection scenarios with minimal effort. We also propose a generic methodology for comparing HT detection methods fairly. Our preliminary results show an average of 84.2% successful HT detection in ISCAS-85 benchmark

Deep Learning is the subset of Machine Learning that primarily deals with Neural Networks. Deep Learning skills are the key skills that students today need to be able to thrive in the global economy. Deep learning skills can help them land prestigious job positions at FAANG companies. FAANG is an acronym that indicates the stocks of five prominent American technology companies: Facebook, Amazon, Apple, Netflix, and Google. Read on to find out more about the key deep learning skills in demand for FAANG.

Cybersecurity is the overall activity of protecting computers, networks, and data from malicious electronic attacks. It is an activity that compares to physical security, a more traditional security activity that controls access to buildings or other objects in the real world. While many high-tech physical security vendors have a combination of physical and cybersecurity in their org charts, cybersecurity is an activity that focuses on protecting assets from malicious logins and code, not on property intrusion or theft. Cybersecurity is a broad concept that encompasses several specific fields of activity. There are many classification methods. For example, there is Kaspersky Lab's classification system, and Mindcore has such a system.

While the world is still recuperating from the pandemic, businesses, large and small, which banked on technology, were able to move on quickly with their operations. This has led to a rise in demand for tech-based job roles such as data analyst, data scientist, cloud architect, and security engineer, among others. The year 2021 is set to drive massive growth for such roles as organisations are looking to create a skilled talent pool for a better digital continuity. For those of you looking to ride the digital wave and make the best of this situation, equipping yourself with new-age skills is the key to powering ahead in your careers. If you are interested in building a career in information technology, here are the top skills you must have on your wish-list while selecting your course post-class 12.

In Japan, information security engineers are lacking. So, I am focused on artificial intelligence (AI) technology to solve the lack of human resources. And, I have developed the AI to detect vulnerabilities on web apps called SAIVS (Spider Artificial Intelligence Vulnerabilities Scanner). The goal of SAIVS is to obtain ability of equal or higher than vulnerability diagnosis members. But, it is possible to detect vulnerabilities on web apps like a human.

Dino Distefano is a research scientist at Facebook, London, U.K., and a professor of computer science at Queen Mary University of London, U.K. Manuel Fähndrich is a software engineer at Facebook Research, Seattle, WA, USA. Francesco Logozzo is a software engineer at Facebook Research, Seattle, WA, USA. Peter W. O'Hearn is a research scientist at Facebook, London, U.K. and a professor of computer science at University College London, U.K.

The security operations center (SOC) at the University of Texas A&M System serves 11 universities and seven state agencies. But with just seven full-time analysts and a risk-rich environment of 174,000 students and faculty, triaging security events was overwhelming. Security analysts had to look at network flow traffic and logs from disparate systems to determine which security events posed threats that needed investigating. The division of labor was typical: Tier-1 analysts looked at alerts, Tier-2 analysts hunted down likely attacks, and a security engineer dreamed up better ways to make the infrastructure more secure. And even the most knowledgeable analysts took a long time to connect disparate data points to come up with a threat profile.

There's a good chance you've considered the implications of machine learning for your security team. As data increases, the skill gap widens, and hackers' strategies get more complex, businesses struggle to detect and address cyberattacks. Machine learning enables behavioral analytics and cognitive security to detonate attachments before they arrive in someone's inbox, or correlate types of activity across a network of thousands of users. The ability to stop attacks before they occur is powerful, but how should security leaders start the process of making their systems smarter with machine learning? Avnet CISO Sean Valcamp advises perfecting your security posture first.

There's a good chance you've considered the implications of machine learning for your security team. As data increases, the skill gap widens, and hackers' strategies get more complex, businesses struggle to detect and address cyberattacks. Machine learning enables behavioral analytics and cognitive security to detonate attachments before they arrive in someone's inbox, or correlate types of activity across a network of thousands of users. The ability to stop attacks before they occur is powerful, but how should security leaders start the process of making their systems smarter with machine learning? Avnet CISO Sean Valcamp advises perfecting your security posture first.