DECEPTION BASED TECHNIQUES AGAINST RANSOMWARES: A SYSTEMATIC REVIEW

Ransomware is the most prevalent emerging business risk nowadays. It seriously affects business continuity and operations. According to Deloitte Cyber Security Landscape 2022, up to 4000 ransomware attacks occur daily, while the average number of days an organization takes to identify a breach is 191. Sophisticated cyber-attacks such as ransomware typically must go through multiple consecutive phases (initial foothold, network propagation, and action on objectives) before accomplishing its final objective. This study analyzed decoy-based solutions as an approach (detection, prevention, or mitigation) to overcome ransomware. A systematic literature review was conducted, in which the result has shown that deception-based techniques have given effective and significant performance against ransomware with minimal resources. It is also identified that contrary to general belief, deception techniques mainly involved in passive approaches (i.e., prevention, detection) possess other active capabilities such as ransomware traceback and obstruction (thwarting), file decryption, and decryption key recovery. Based on the literature review, several evaluation methods are also analyzed to measure the effectiveness of these deception-based techniques during the implementation process

Data Flooding against Ransomware: Concepts and Implementations

Ransomware is one of the most infamous kinds of malware, particularly the “crypto” subclass, which encrypts users’ files, asking for some monetary ransom in exchange for the decryption key. Recently, crypto-ransomware grew into a scourge for enterprises and governmental institutions. The most recent and impactful cases include an oil company in the US, an international Danish shipping company, and many hospitals and health departments in Europe. Attacks result in production lockdowns, shipping delays, and even risks to human lives. To contrast ransomware attacks (crypto, in particular), we propose a family of solutions, called Data Flooding against Ransomware, tackling the main phases of detection, mitigation, and restoration, based on a mix of honeypots, resource contention, and moving target defence. These solutions hinge on detecting and contrasting the action of ransomware by flooding specific locations (e.g., the attack location, sensible folders, etc.) of the victim’s disk with files. Besides the abstract definition of this family of solutions, we present an open-source tool that implements the mitigation and restoration phases, called Ranflood. In particular, Ranflood supports three flooding strategies, apt for different attack scenarios. At its core, Ranflood buys time for the user to counteract the attack, e.g., to access an unresponsive, attacked server and shut it down manually. We benchmark the efficacy of Ranflood by performing a thorough evaluation over 6 crypto-ransomware (e.g., WannaCry, LockBit) for a total of 78 different attack scenarios, showing that Ranflood consistently lowers the amount of files lost to encryption

Cyber Threat Intelligence : Challenges and Opportunities

The ever increasing number of cyber attacks requires the cyber security and forensic specialists to detect, analyze and defend against the cyber threats in almost realtime. In practice, timely dealing with such a large number of attacks is not possible without deeply perusing the attack features and taking corresponding intelligent defensive actions, this in essence defines cyber threat intelligence notion. However, such an intelligence would not be possible without the aid of artificial intelligence, machine learning and advanced data mining techniques to collect, analyse, and interpret cyber attack evidences. In this introductory chapter we first discuss the notion of cyber threat intelligence and its main challenges and opportunities, and then briefly introduce the chapters of the book which either address the identified challenges or present opportunistic solutions to provide threat intelligence.Comment: 5 Page

Encountering social engineering activities with a novel honeypot mechanism

Communication and conducting businesses have eventually transformed to be performed through information and communication technology (ICT). While computer network security challenges have become increasingly significant, the world is facing a new era of crimes that can be conducted easily, quickly, and, on top of all, anonymously. Because system penetration is primarily dependent on human psychology and awareness, 80% of network cyberattacks use some form of social engineering tactics to deceive the target, exposing systems at risk, regardless of the security system's robustness. This study highlights the significance of technological solutions in making users more safe and secure. Throughout this paper, a novel approach to detecting and preventing social engineering attacks will be proposed, combining multiple security systems, and utilizing the concept of Honeypots to provide an automated prevention mechanism employing artificial intelligence (AI). This study aims to merge AI and honeypot with intrusion prevention system (IPS) to detect social engineering attacks, threaten the attacker, and restrict his session to keep users away from these manipulation tactics

Implementation of Decoy Deception based Detection System for Ransomware Attack

Ransomware poses a dangerous threat to  cybersecurity. Data as well as rights owned by the user are  adversely impacted. The situation has become considerably  more critical as a result of the emergence of new ransomware  varieties and Ransomware-as-a-Service. In this paper, we  presented a novel deception-based and behaviour-based  method for real-time ransomware detection. In order to avoid  any loss before ransomware is discovered, we build pretend  files and directories for nefarious behaviours. We conducted a  pilot study using Locky, and the results demonstrate the  effectiveness of our strategy with little system resource usage  and geographical cost.&nbsp

ANALYSIS OF CLIENT-SIDE ATTACKS THROUGH DRIVE-BY HONEYPOTS

Client-side cyberattacks on Web browsers are becoming more common relative to server-side cyberattacks. This work tested the ability of the honeypot (decoy) client software Thug to detect malicious or compromised servers that secretly download malicious files to clients, and to classify what it downloaded. Prior to using Thug we did TCP/IP fingerprinting to assess Thug’s ability to impersonate different Web browsers, and we created our own malicious Web server with some drive-by exploits to verify Thug’s functions; Thug correctly identified 85 out of 86 exploits from this server. We then tested Thug’s analysis of delivered exploits from two sets of real Web servers; one set was obtained from random Internet addresses of Web servers, and the other came from a commercial blacklist. The rates of malicious activity on 37,415 random websites and 83,667 blacklisted websites were 5.6% and 1.15%, respectively. Thug’s interaction with the blacklisted Web servers found 163 unique malware files. We demonstrated the usefulness and efficiency of client-side honeypots in analyzing harmful data presented by malicious websites. These honeypots can help government and industry defenders to proactively identify suspicious Web servers and protect users.OUSD(R&E)Outstanding ThesisLieutenant, United States NavyApproved for public release. Distribution is unlimited