An Enhanced Minimax Loss Function Technique in Generative Adversarial Network for Ransomware Behavior Prediction

Abstract

Recent ransomware attacks threaten not only personal files but also critical infrastructure like smart grids, necessitating early detection before encryption occurs. Current methods, reliant on pre-encryption data, suffer from insufficient and rapidly outdated attack patterns, despite efforts to focus on select features. Such an approach assumes that the same features remain unchanged. This approach proves ineffective due to the polymorphic and metamorphic characteristics of ransomware, which generate unique attack patterns for each new target, particularly in the pre-encryption phase where evasiveness is prioritized. As a result, the selected features quickly become obsolete. Therefore, this study proposes an enhanced Bi-Gradual Minimax (BGM) loss function for the Generative Adversarial Network (GAN) Algorithm that compensates for the attack patterns insufficiency to represents the polymorphic behavior at the earlier phases of the ransomware lifecycle. Unlike existing GAN-based models, the BGM-GAN gradually minimizes the maximum loss of the generator and discriminator in the network. This allows the generator to create artificial patterns that resemble the pre-encryption data distribution. The generator is used to craft evasive adversarial patterns and add them to the original data. Then, the generator and discriminator compete to optimize their weights during the training phase such that the generator produces realistic attack patterns, while the discriminator endeavors to distinguish between the real and crafted patterns. The experimental results show that the proposed BGM-GAN reached maximum accuracy of 0.98, recall (0.96), and a minimum false positive rate (0.14) which all outperform those obtained by the existing works. The application of BGM-GAN can be extended to early detect malware and other types of attacks.

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

“…Additionally, the dissection of ransom messages, which are crafted to coerce or psychologically manipulate victims into complying with the ransom demands, forms an integral part of understanding the ransomware's modus operandi [59], [34]. Such comprehensive exploration of ransomware features, from its technical execution to its economic and psychological impact, sheds light on the multifaceted nature of these cyber threats, thus providing a more holistic view of the ransomware landscape and its implications for both individuals and organizations [60], [16].…”

“…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].…”

“…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]. The use of LLMs in this context is not without challenges, as their effectiveness in real-world scenarios is still under scrutiny [16]. Nonetheless, the potential of these models in reshaping the landscape of ransomware response strategies cannot be overlooked [21], [2], [22].…”

Novel Ransomware Detection Exploiting Uncertainty and Calibration Quality Measures Using Deep Learning

“…Among these risks are the malware attacks utilized by attackers to steal data, disrupt operations, or hijack systems causing operational interruptions as well as impacts affecting reputation and compliance [2]. Ransomware is one type of malware that attackers use to lock user data using the operating system's own cryptographic utilities [3,4]. Ransomware presents a significant threat to organizations and individuals, as it encrypts files rendering computer systems inoperable.…”

An Incremental Mutual Information-Selection Technique for Early Ransomware Detection

“…The substantial impact of such attacks is evident from the global damages inflicted on various systems around the world [5][6][7][8]. Given the complexity of ransomware, showcased by variants like LockBit 2.0, it is clear that advanced preventative and remediation strategies are needed [9,10]. Research is being directed toward developing detection systems that leverage behavioral, network traffic, and machine learning techniques for real-time detection and categorization of ransomware threats [11][12][13].…”