Analyzing ransomware attacks: identifying common patterns and unique characteristics for enhanced cybersecurity strategies

Ransomware is the most widespread menace to cyberspace not only for individuals and corporations, but for key infrastructures on the global scale as well. In these attacks, the data of the victims is encrypted with a key and then a ransom is demanded, often leading to immense financial and reputational loss. In this research, past studies addressing the problem of ransomware attacks are reviewed systematically in order to both trace distinctive features and common regularities of attacks for the purposes of developing better protection against cyber threats. The review covers perspectives assessment of the fundamental approaches such as signature verification, behavioural and ML models, network anomaly detection, and blocking. Although signature approaches have shown effectiveness in detecting already known targets, they perform poorly when dealing with zero-day ransomware. These include behavioural analysis and ML based techniques which are more flexible and scalable but struggle with false positives and heavy computation. It was shown that ML approaches are more efficient than all the others targeting identified-zero-day threats, while ML plus blockchain induced paradigms can solve existing problems. The study proposes an AI-blockchain framework integrating federated learning, homomorphic encryption, and dynamic honeypots for improved real-time detection and response.