Cyber-attacks on financial institutions and corporations are on the rise, particularly during pandemics. These attacks are becoming more sophisticated. Reports of hacking activities against government and commercial sector organisations have garnered a lot of attention in the last several years. By design, the focus of Cyber Threat Intelligence (CTI) is exclusively defensive. This is because most of the CTI-derived analysis output is intended to prevent breaches or facilitate early detection. So, there is a need to have a new mechanism for unmasking the attacker. In this research, we demonstrate cyber threat intelligence enrichment with counterintelligence and counterattack combined with certain new methods to exploit the adversary’s vulnerability and fully control the attacker’s system. Attackers use a VPN to establish an anonymous connection. A VPN creates a secure “tunnelling” to the internet, with the VPN server acting as a middleman between the attacker and the web. This provides anonymity because the attacker’s IP address seems to be that of the VPN rather than his own, masking the IP address. So, hackers used this application to create persistence because it is automatically launched each time a computer is restarted. As a result, we are attempting to eliminate the persistence by removing it from the startup and registry. This research will help firms detect and identify an assault in its earliest phases, allowing them to respond accordingly. This project will develop new and innovative strategies to bypass VPNs and other security measures in order to obtain correct source information. Companies will be able to identify new methods by which their systems are penetrated and rapidly harden them. Using counterattack and counterintelligence, a proposed technique can bypass a VPN and get adversarial intel. The main goal of this research is to find the attacker’s footprints or tracks and find out why the attack was planned in the first place.

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.

In a globalized world, the term malware is like a mysterious computer program that spreads violence and sows the downsizing of an operating system. Malware is often associated with cybercrime which deceives the user into seeing the edge of the machine. As soon as a criminal finds a beachhead within the victim's network, it will become commonplace to download more loads and exploit vulnerabilities, gain more control and access within the network. This paper examines the various methods used by malware writers to achieve persistence in a changing environment in Windows operating system. Our research is based on the solar wind attack that succeeded by its persistence mechanism. We perform some techniques such as Registry Persistence, Task scheduler Persistence, Startup, Basic Input/Output system endurance, PowerShell, etc. These methods help the criminal to use command and control whenever the attacker wants. Furthermore, our paper provides some valid solutions for how to handle this type of persistence in the real environment. Our techniques demonstrate the best combination about malware persistence and table provide the best solution against persistence.

In cybersecurity, malware plays a very significant role. With every passing day, attackers are coming up with new techniques and in most cases, they are successful and cause destruction to the data. On the other hand, anti-viruses or malware detection software detect and prevent malwares based on some detection rules. Malware consists of a lot of techniques such as social engineering, persistence, exfiltration, reverse connection, and password cracking are the most common techniques. Anti-viruses are also emerging and use machine learning algorithms to scan the behavior of malicious software and programs. In this paper, we discuss some very hard broken techniques that attackers are using in new attacks launched on the operating systems. We discuss the methods that how attackers use encryption, oligomorphic, metamorphism, obfuscation, code reusability, account violation and password-protected malware to trick anti-viruses and bypass scanning procedures. This paper also tells the reader about bypassing next-generation anti-viruses that use machine learning and analyze how old techniques are different and less applicable in a dynamic operating system environment.