Ransomware Attacks in Cyber-Physical Systems: Countermeasure of Attack Vectors Through Automated Web Defenses

Ransomware has appeared to be the most damaging and devastating type of malware attack in any cyber physical system. The resilience of a web browser to deal with the malware attack is of significance importance, however, evaluating the performance of a browser to tackle these attacks is a challenging task. Due to various automation techniques, web applications can be tested without human intervention. Technologies such as Junit, Chakram, and Selenium are useful in automated testing but the problem is that the attacker uses harmful code and automated web approaches to distribute their malware. In this research,our contribution is twofold. Firstly, we examine a new attack vector that cyber adversaries can possibly use in the future to infect an operating system with a malware. Currently, attackers use various techniques to gain access to victims’ personal computers. Secondly, we present a novel automated web defence to countermeasure these malware attacks. The proposed research aims to provide a better understanding of the new computer virus-spreading techniques that intruders can use in the future. We provide the insight of these attacks and present ways to countermeasure the attacks and to reduce the attack surface. Experiments and flow diagrams have been used to demonstrate the attack and defence approach. To offer malware lateral movement and to encrypt the date of users’ device, we use Selenium automation tool on a social media platform. For our experimentation, we developed an application which has been tested on a variety of browsers including Google Chrome, Firefox, and Safari. Our research has revealed that we have an 85 percent success rate when testing in a head-on environment. We have expanded our experiments on headless applications and interestingly, the accuracy rate improved and the probability of success increased to 95 percent. Lastly, we have demonstrated a unique method for detecting and stopping web automation that is generally applicable.