Utilizing Role Based Modeling Language To Determine A System S Safety From An Advanced Persistent Threat
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Utilizing Role Based Modeling Language to Determine a System’s Safety from an Advanced Persistent Threat
With Advanced Persistent Threats (APTs) becoming a larger threat among the cyber world, it is important that software is designed with security in mind. In order to aide this process, software developers can use security design patterns when creating systems. This helps to ensure that all aspects of a system’s design are concerned with security, because any one point of weakness can still lead to the compromise of the entire system. We propose a method of determining if a system contains a specific security pattern, and we suggest several security patterns which might be helpful in deterring a specific APT. We utilize an algorithm based on graph homomorphism theory that gives a metric for how close a Unified Modeling Language (UML) model is to being a realization of a security pattern, from which it may be verified that a system is indeed a realization of the Role Based Modeling Language (RBML) model of the pattern. We find that our distance metric gives the desired result for several example applications.
Safety Causation Analysis in Sociotechnical Systems: Advanced Models and Techniques
This book provides a comprehensive view on theories, models, and techniques used to investigate and analyze incidents and safety causalities occurring in sociotechnical systems. Consisted of intricately interconnected components, sociotechnical systems are always prone to incidents. These incidents can ensue with adverse effects on employees and the public, the environment, and company's properties and reputation. Sometimes, a single incident has the potential to terminate the operation of a business forever. As incidents are multi-factorial and not easy to comprehend, they should be investigated systematically in a structured way so as to find their root causes and prevent them from recurring. Consequently, there have been developed many theories, models, and techniques aimed at accomplishing this goal. However, each approach has its own upsides and downsides, and there is no universal one applicable to all cases. Therefore, researchers and practitioners may sometimes find it difficult to select the most appropriate approach for the given case. After introducing theories, models, and techniques pertaining to incident investigation and safety causalities modeling, this book explains each one in details and discusses their pros and cons. The book aims to provide the audience with a step-by-step guidance for performing incident investigation and analysis. At the end of each chapter an example is analyzed by the introduced tool. Finally, the book offers criteria based on which an incident analysis technique can be selected.
Utilizing AI in Network and Mobile Security for Threat Detection and Prevention
Artificial intelligence (AI) revolutionizes how organizations protect their digital information against cyber threats. Traditional security methods are often insufficient when faced with sophisticated attacks. AI-powered systems utilize machine learning, deep learning, and advanced analytics to detect patterns, identify anomalies, and predict potential threats in real time. By analyzing network traffic and mobile device behavior, AI can recognize and respond to malicious activity before it causes harm. This proactive approach enhances security protocols, reduces human error, and strengthens defenses against a wide range of cyberattacks, from malware to data breaches. Further research may reveal AI as an indispensable tool for securing networks and mobile environments, providing smarter, more adaptive solutions for threat detection and prevention. Utilizing AI in Network and Mobile Security for Threat Detection and Prevention explores the role of AI in enhancing cybersecurity measures. It examines AI techniques in anomaly and intrusion detection, machine learning for malware analysis and detection, predictive analytics to cybersecurity scenarios, and ethical considerations in AI. This book covers topics such as ethics and law, machine learning, and data science, and is a useful resource for computer engineers, data scientists, security professionals, academicians, and researchers.