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In the Machine Learning approach, machine learning algorithms analyze and categorize ransomware behavior. Trained on datasets of both known ransomware and benign samples, these algorithms identify new ransomware based on learned characteristics. Machine learning techniques, such as Decision Trees, Support Vector Machines, and Artificial Neural Networks, are applied. Advantages include adaptability to new ransomware variations and scalability for handling large datasets.
Ransomware Detection Approach | Ref. | Description | Advantages | Disadvantages |
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Machine Learning | [7][8][9][10] | The most used machine learning techniques in ransomware detection include supervised learning, unsupervised learning, and semi-supervised learning. Supervised learning involves training a model on labeled data, where the input and output are both known. This allows the model to make predictions based on the relationships learned from the training data. Unsupervised learning involves training a model on data where the output is not known, and the model must find patterns and relationships within the data on its own. Semi-supervised learning is a combination of supervised and unsupervised learning, where the model is trained on a mix of labeled and unlabeled data. | One of the main advantages of using machine learning for ransomware detection is that it allows for the automatic identification of patterns and relationships within large datasets. This can be particularly useful for identifying new and emerging threats, as the model can learn from past data to identify patterns and make predictions about future threats. Machine learning algorithms can also be trained on a wide variety of data types, including text, images, and audio, which makes them useful for detecting ransomware in different formats. | Machine learning algorithms can be vulnerable to bias and can produce inaccurate results if the training data are not representative of the real-world data. They also require frequent retraining to ensure that they continue to perform well as the data distribution changes. |
Honeypot | [11][12][13][14][15] | Honeypots are a type of decoy system that is designed to attract and detect malware or cyber-attacks. They are used to lure attackers into a controlled and isolated environment, where their actions can be observed and studied. By setting up a honeypot, it is possible to monitor and track ransomware activity and identify new strains or variants of the malware. | One advantage of using a honeypot is that it allows researchers to gather valuable data and intelligence about the tactics, techniques, and procedures (TTPs) used by attackers. This information can be used to improve the effectiveness of ransomware detection and prevention measures. Additionally, honeypots can help mitigate the impact of ransomware attacks by preventing the malware from reaching the target system or data. | There are also some disadvantages to using honeypots. One potential issue is the risk of false positives, where legitimate activity is mistaken for malicious activity. Another issue is the cost and resources required to maintain and operate a honeypot, as well as the potential legal and ethical considerations. Additionally, honeypots may not be suitable for all types of environments or organizations and may not provide comprehensive protection against all types of ransomware attacks. |
Statistical | [16][17][18][19][20] | The statistical analysis approach involves collecting and analyzing data about ransomware behavior to identify patterns and trends. This can be done through various methods, such as collecting data about the frequency and types of ransom demands, the types of files targeted, and the tactics used by ransomware operators. | The advantage of using statistical analysis is that it allows researchers to gain a deeper understanding of ransomware behavior and identify key trends that can inform prevention and detection efforts. | The disadvantage of this approach is that it relies on the availability of accurate and comprehensive data, which may be difficult to obtain in some cases. Additionally, statistical analysis may not be able to identify specific instances of ransomware in real time, making it less effective for immediate detection and response. |