# Early Ransomware Detection with Deep Learning Models
## 2 Background  
Traditionally, ransomware detection methods have relied on several key strategies. Signature-based detectionis the most common method used in traditional antivirus software. It matches known malware signatures--unique strings of data or characteristics of known malware--against files. While effective against known threats, this method struggles to detect new, unknown ransomware variants [16; 17]. Heuristic analysis uses algorithms to examine software or file behavior for suspicious characteristics. This method can potentially identify new ransomware based on behaviors similar to known malware, but its effectiveness depends on the sophistication of the heuristic rules [18]. Behavioral analysis monitors how programs behave and highlights odd behaviors--like quick file encryption--that could indicate ransomware. Although these tools need a baseline of typical behavior and can produce false positives, they may identify zero-day ransomware attacks (new and undiscovered threats) [18]. Sandboxing runs files or programs in a virtual environment (sandbox) to observe their behavior without risking the actual system. If malicious activities like unauthorized encryption are detected, the ransomware can be identified before it harms the real environment. However, some advanced ransomware can detect and evade sandboxes [19]. Honeyfiles (decoy folders) places decoy files or folders within a system. Monitoring these decoys for unauthorized encryption or modifications can signal a ransomware attack. While useful as an early warning system, it does not prevent ransomware from infecting genuine files [20]. Although each of these approaches has advantages, they also come with special difficulties when it comes to ransomware detection. One major obstacle is finding a balance between the necessity for quick, precise detection and the reduction in false positives and negatives. For this purpose, machine learning technologies, especially deep learning (DL), are now used because they provide strong defenses against ransomware and other sophisticated cyber threats. DL is used in malware classification [21], phishing detection [22], anomaly identification [23], and malware detection. By examining the order of operations in a system, which may include odd file-encryption activities, DL models have demonstrated high efficacy in detecting ransomware activities [24; 25]. DL can spot subtle and complicated patterns in data, which is important for detecting zero-day ransomware assaults; it can also handle enormous amounts of data, making it perfect for real-time threat identification. However, big and well-labeled datasets are necessary for efficient DL models, and their preparation can be costly and time-consuming [13]. Additionally, there is a chance that models will overfit and not generalize well on fresh and untested data. Training DL models demands substantial computational resources, which can be an obstacle for some organizations [14]. Next, we survey some of the most prominent works on ML-based ransomware detection. The study [26] significantly extends the realm of cybersecurity by utilizing an advanced dataset consisting of both ransomware and benign software samples collected from 2014 to early 2021. These samples underwent dynamic analysis to document API call sequences, capturing detailed behavioral footprints. The LightGBM model was used to classify the samples. The model demonstrated exceptional efficacy, achieving an accuracy of 0.987 in classifying software types. The work [27] presents a sophisticated approach to malware detection by distinguishing API call sequences using long short-term memory (LSTM) networks, which were not limited to ransomware. The dataset in this paper was sourced from Alibaba Cloud's cyber-security efforts, and it contains a comprehensive collection of malware samples, including ransomware.