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2024-07-31 10:00:00 | Les attaques de ransomwares sont-elles toujours une menace croissante en 2024? Are Ransomware Attacks Still a Growing Threat in 2024? (lien direct) |
The content of this post is solely the responsibility of the author. LevelBlue does not adopt or endorse any of the views, positions, or information provided by the author in this article. Ransomware attacks continue to pose a growing threat to organizations as it has emerged as the number one threat, affecting 66% of organizations in 2023 and pulling over $1 billion from the victims. These attacks have increased in frequency and sophistication, resulting in significant financial loss, operation disruption, theft of sensitive data, and reduced productivity rates. Also, it damages the organization\'s reputation and results in the loss of customer trust and compliance violations. An organization needs a comprehensive protection strategy to reduce the frequency of these attacks and the risks they pose. Ransomware Business Model: How These Attacks Are Evolving? In the past, ransomware attacks mainly relied on phishing emails, remote desktop protocol exploits, and vulnerable ports to increase their chances of success. Additionally, these attacks employ evasion techniques to bypass traditional security measures like firewalls or antivirus software. These methods have resulted in famous attacks like WannaCry, TeslaCrypt, and NotPetya. With time, ransomware attackers have evolved and have become more sophisticated, targeted, and profitable for cybercriminals. Below is an insight into the latest trends that hackers adopt to launch a successful ransomware attack: Exploiting Zero-Day Vulnerabilities The shift in ransomware gangs and their sophisticated tactics and procedures (TTPs) raise the number of ransomware attacks. . Previously, REvil, Conti, and LockBit were the famous ransomware gangs, but now Clop, Cuban, and Play are gaining immense popularity by employing advanced hacking techniques like zero-day vulnerabilities. Sophos\'s State of Ransomware 2024 revealed exploited vulnerabilities as the root cause of ransomware attacks. The Clop ransomware gang has used the zero-day vulnerability in the MOVEit Transfer platform to steal the sensitive data of different organizations. This group also targeted the GoAnywhere zero-day vulnerability in January 2023, affecting 130 organizations, and exploited the Accellion FTA servers in 2020. Similarly, Cuban and Play used the same attacking technique to compromise the unpatched Microsoft Exchange servers. Double and Triple Extortion Another reason for the rise in ransomware attacks is the introduction of the double or triple extortion technique. Cybersecurity firm Venafi reported that 83% of ransomware attacks included multiple ransom demands in 2022. Cybercriminals encrypt the data, exfiltrate sensitive information, and threaten to release it or sell it on the dark web if the ransom is not paid in a double extortion scheme. This tactic prove | Ransomware Malware Tool Vulnerability Threat Studies Legislation Prediction Medical Technical | NotPetya Wannacry Deloitte | |
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2024-04-29 14:00:00 | De l'assistant à l'analyste: la puissance de Gemini 1.5 Pro pour l'analyse des logiciels malveillants From Assistant to Analyst: The Power of Gemini 1.5 Pro for Malware Analysis (lien direct) |
Executive Summary A growing amount of malware has naturally increased workloads for defenders and particularly malware analysts, creating a need for improved automation and approaches to dealing with this classic threat. With the recent rise in generative AI tools, we decided to put our own Gemini 1.5 Pro to the test to see how it performed at analyzing malware. By providing code and using a simple prompt, we asked Gemini 1.5 Pro to determine if the file was malicious, and also to provide a list of activities and indicators of compromise. We did this for multiple malware files, testing with both decompiled and disassembled code, and Gemini 1.5 Pro was notably accurate each time, generating summary reports in human-readable language. Gemini 1.5 Pro was even able to make an accurate determination of code that - at the time - was receiving zero detections on VirusTotal. In our testing with other similar gen AI tools, we were required to divide the code into chunks, which led to vague and non-specific outcomes, and affected the overall analysis. Gemini 1.5 Pro, however, processed the entire code in a single pass, and often in about 30 to 40 seconds. Introduction The explosive growth of malware continues to challenge traditional, manual analysis methods, underscoring the urgent need for improved automation and innovative approaches. Generative AI models have become invaluable in some aspects of malware analysis, yet their effectiveness in handling large and complex malware samples has been limited. The introduction of Gemini 1.5 Pro, capable of processing up to 1 million tokens, marks a significant breakthrough. This advancement not only empowers AI to function as a powerful assistant in automating the malware analysis workflow but also significantly scales up the automation of code analysis. By substantially increasing the processing capacity, Gemini 1.5 Pro paves the way for a more adaptive and robust approach to cybersecurity, helping analysts manage the asymmetric volume of threats more effectively and efficiently. Traditional Techniques for Automated Malware Analysis The foundation of automated malware analysis is built on a combination of static and dynamic analysis techniques, both of which play crucial roles in dissecting and understanding malware behavior. Static analysis involves examining the malware without executing it, providing insights into its code structure and unobfuscated logic. Dynamic analysis, on the other hand, involves observing the execution of the malware in a controlled environment to monitor its behavior, regardless of obfuscation. Together, these techniques are leveraged to gain a comprehensive understanding of malware. Parallel to these techniques, AI and machine learning (ML) have increasingly been employed to classify and cluster malware based on behavioral patterns, signatures, and anomalies. These methodologies have ranged from supervised learning, where models are trained on labeled datasets, to unsupervised learning for clustering, which identifies patterns without predefined labels to group similar malware. | Malware Hack Tool Vulnerability Threat Studies Prediction Cloud Conference | Wannacry | ★★★ |
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