Ransomware Detection Based On Opcode Behavior Using K-Nearest Neighbors Algorithm

Abstract

Ransomware is a malware that represents a serious threat to a user’s information privacy. By investigating howransomware works, we may be able to recognise its atomic behaviour. In return, we will be able to detect theransomware at an earlier stage with better accuracy. In this paper, we propose Control Flow Graph (CFG) asan extracting opcode behaviour technique, combined with 4-gram (sequence of 4 “words”) to extract opcodesequence to be incorporated into Trojan Ransomware detection method using K-Nearest Neighbors (K-NN)algorithm. The opcode CFG 4-gram can fully represent the detailed behavioural characteristics of Trojan Ransomware.The proposed ransomware detection method considers the closest distance to a previously identifiedransomware pattern. Experimental results show that the proposed technique using K-NN, obtains the best accuracyof 98.86% for 1-gram opcode and using 1-NN classifier.

Evaluating Large Language Models in Ransomware Negotiation: A Comparative Analysis of ChatGPT and Claude

“…Advancements in heuristic analysis have also been a key development in this field [39], [12]. Heuristic analysis involves examining the characteristics of a file or program to determine if it behaves like ransomware, even if it does not match a known signature [32], [47], [17]. This method allows for the detection of ransomware that has been deliberately obfuscated or altered to evade signature-based systems [48], [49].…”

“…The advent of LLMs in cybersecurity, offering a blend of artificial intelligence and natural language processing capabilities, presents a novel solution in this battle against ransomware [13], [14], [15]. These models are not only capable of understanding complex language constructs but can also simulate negotiation tactics that were traditionally the realm of human experts [16], [17]. This ability to simulate human-like negotiation strategies could be instrumental in mitigating the fallout from ransomware attacks, particularly in situations where communication with the attackers is inevitable [18], [19], [20].…”

Entropy and Memory Forensics in Ransomware Analysis: Utilizing LLaMA-7B for Advanced Pattern Recognition

“…The domain of memory forensics has ascended in its pertinence, enabling detailed examination of ransomware within its active context, thus permitting the extraction of critical insights about its execution behavior [21], [22]. However, contemporary ransomware's ingenuity poses a consistent challenge to a spectrum of advanced research methodologies developed for ransomware mitigation [19], [16], [23]. Such methods encompass behavior-based detection, which scrutinizes system activities for abnormalities; anomaly detection systems that leverage statistical models to identify outliers in data patterns; signature-based filtering, which relies on known data patterns to detect ransomware; heuristic analysis, using experience-based techniques for identifying suspicious behavior; dynamic analysis in secure sandbox environments that isolate the ransomware to observe its behavior without risk to real systems; and comprehensive endpoint detection and response solutions that monitor end-user devices for indicators of compromise [24], [25].…”

Entropy and Memory Forensics in Ransomware Analysis: Utilizing LLaMA-7B for Advanced Pattern Recognition

“…The domain of memory forensics has ascended in its pertinence, enabling detailed examination of ransomware within its active context, thus permitting the extraction of critical insights about its execution behavior [21], [22]. However, contemporary ransomware's ingenuity poses a consistent challenge to a spectrum of advanced research methodologies developed for ransomware mitigation [19], [16], [23]. Such methods encompass behavior-based detection, which scrutinizes system activities for abnormalities; anomaly detection systems that leverage statistical models to identify outliers in data patterns; signature-based filtering, which relies on known data patterns to detect ransomware; heuristic analysis, using experience-based techniques for identifying suspicious behavior; dynamic analysis in secure sandbox environments that isolate the ransomware to observe its behavior without risk to real systems; and comprehensive endpoint detection and response solutions that monitor end-user devices for indicators of compromise [24], [25].…”