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President Biden on July 9 said he made it “very clear” to Russian President Vladimir Putin that he expects crackdowns on Russia-based ransomware attacks even though they are not state-sponsored.

Ransomware attacks pose significant security threats to personal and corporate data and information. The owners of computer-based resources suffer from verification and privacy violations, monetary losses, and reputational damage due to successful ransomware assaults. As a result, it is critical to accurately and swiftly identify ransomware. Numerous methods have been proposed for identifying ransomware, each with its own advantages and disadvantages. The main objective of this research is to discuss current trends in and potential future debates on automated ransomware detection. This document includes an overview of ransomware, a timeline of assaults, and details on their background. It also provides comprehensive research on existing methods for identifying, avoiding, minimizing, and recovering from ransomware attacks. An analysis of studies between 2017 and 2022 is another advantage of this research. This provides readers with up-to-date knowledge of the most recent developments in ransomware detection and highlights advancements in methods for combating ransomware attacks. In conclusion, this research highlights unanswered concerns and potential research challenges in ransomware detection.

Ransomware is an ill-famed malware that has received recognition because of its lethal and irrevocable effects on its victims. The irreparable loss caused due to ransomware requires the timely detection of these attacks. Several studies including surveys and reviews are conducted on the evolution, taxonomy, trends, threats, and countermeasures of ransomware. Some of these studies were specifically dedicated to IoT and android platforms. However, there is not a single study in the available literature that addresses the significance of dynamic analysis for the ransomware detection studies for all the targeted platforms. This study also provides the information about the datasets collection from its sources, which were utilized in the ransomware detection studies of the diverse platforms. This study is also distinct in terms of providing a survey about the ransomware detection studies utilizing machine learning, deep learning, and blend of both techniques while capitalizing on the advantages of dynamic analysis for the ransomware detection. The presented work considers the ransomware detection studies conducted from 2019 to 2021. This study provides an ample list of future directions which will pave the way for future research.

Recently, ransomware attacks have been among the major threats that target a wide range of Internet and mobile users throughout the world, especially critical cyber physical systems. Due to its unique characteristics, ransomware has attracted the attention of security professionals and researchers toward achieving safer and higher assurance systems that can effectively detect and prevent such attacks. The state-of-the-art crypto ransomware early detection models rely on specific data acquired during the runtime of an attack’s lifecycle. However, the evasive mechanisms that these attacks employ to avoid detection often nullify the solutions that are currently in place. More effort is needed to keep up with an attacks’ momentum to take the current security defenses to the next level. This survey is devoted to exploring and analyzing the state-of-the-art in ransomware attack detection toward facilitating the research community that endeavors to disrupt this very critical and escalating ransomware problem. The focus is on crypto ransomware as the most prevalent, destructive, and challenging variation. The approaches and open issues pertaining to ransomware detection modeling are reviewed to establish recommendations for future research directions and scope.

Ransomware is a type of malware that locks access to or encrypts its victim’s files for a ransom to be paid to get back locked or encrypted data. With the invention of obfuscation techniques, it became difficult to detect its new variants. Identifying the exact malware category and family can help to prepare for possible attacks. Traditional machine learning-based approaches failed to detect and classify advanced obfuscated ransomware variants using existing pattern-matching and signature-based detection techniques. Deep learning-based approaches have proven helpful in both detection and classification by analyzing obfuscated ransomware deeply. Researchers have contributed mainly to detection and minimaly to family attribution. This research aims to address all these multi-class classification problems by leveraging the power of deep learning. We have proposed a novel group normalization-based bidirectional long short-term memory (GN-BiLSTM) method to detect and classify ransomware variants with high accuracy. To validate the technique, five other deep learning models are also trained on the CIC-MalMem-2022, an obfuscated malware dataset. The proposed approach outperformed with an accuracy of 99.99% in detection, 85.48% in category-wise classification, and 74.65% in the identification of ransomware families. To verify its effectiveness, models are also trained on 10,876 self-collected latest samples of 26 malware families and the proposed model has achieved 99.20% accuracy in detecting malware, 97.44% in classifying its category, and 96.23% in identifying its family. Our proposed approach has proven the best for detecting new variants of ransomware with high accuracy and can be implemented in real-world applications of ransomware detection.

Ransomware is a type of new malware that is extremely dangerous and causes serious problems, affecting several organizations and individuals around the world. Ransomware attacks nearly doubled in the first half of 2021, according to statists. In 2020, there were approximately 304 million ransomware attacks worldwide in different parts of the world. The increase was 62% compared to last year and is the second largest increase since 2016. Many researchers are already talking about ransomware and its impact. However, much more research into ransomware is needed to provide further in-depth analysis and study of ransomware. This paper focuses specifically on the impact of ransomware on Windows platforms. Since Windows is the most widely used and well-known platform, it was chosen for the analysis. It monitors the infection process, how it occurs, as well as the various methods used by ransomware families to encrypt. In conclusion, this paper suggests that securing Windows is possible if system files and registry are closely monitored.

Android ransomware has emerged as a major threat to mobile ecosystems, leveraging obfuscated payloads and dynamic command-and-control channels to evade conventional detection systems. Existing approaches often rely on static, batch-trained models that lack adaptability to evolving threat behaviors, resulting in degraded accuracy over time due to concept drift. This presents a critical challenge for real-time deployment, as new ransomware variants continually mutate their signatures and alter network traffic patterns to evade detection. To bridge this gap, this study proposes a robust ensemble-based machine learning framework for proactive detection of Android ransomware using network traffic metadata. The framework integrates advanced classifiers, including Light Gradient Boosting Machine, eXtreme Gradient Boosting Machine, and Random Forest, with Synthetic Minority Oversampling Technique enhanced stratified cross-validation to mitigate class imbalance and improve generalizability. Furthermore, explainable artificial intelligence methods such as SHapley Additive exPlanations and Local Interpretable Model-Agnostic Explanations are employed to enhance interpretability and analyst trust. In the context of ransomware detection, the importance of online learning lies in its ability to adapt to evolving threat patterns in real time. Ransomware frequently mutates payload signatures and obfuscates behavioral traces, causing traditional models to deteriorate under changing data distributions. To address this, we conducted a concept drift evaluation using an incremental LightGBM model, tested on chronologically partitioned traffic data across five temporal blocks. This approach enables continuous adaptation to new data streams without requiring full retraining, thereby maintaining detection robustness and reducing false negatives in production. Experimental results on a balanced dataset demonstrate that LightGBM achieves the highest classification performance, indicating the efficacy and adaptability of the proposed framework for real-time Android ransomware mitigation in dynamic network environments.

Effective cybersecurity measures safeguard against financial loss, reputational damage and operational disruptions so that your business can operate with confidence and resilience. Investing in robust cybersecurity not only defends against potential threats but also fosters a culture of security that supports long-term success and growth. Ashley Perkins, vice president of Cox Business in Arkansas, leads a business-to-business sales force and order management team that is responsible for commercial service, including sales, service delivery, assurance and customer support, across Arkansas, Oklahoma and Kansas.

Crypto ransomware is malware that locks its victim’s file for ransom using an encryption algorithm. Its popularity has risen at an alarming rate among the cyber community due to several successful worldwide attacks. The encryption employed had caused irreversible damage to the victim’s digital files, even when the victim chose to pay the ransom. As a result, cybercriminals have found ransomware a lucrative and profitable cyber-extortion approach. The increasing computing power, memory, cryptography, and digital currency advancement have caused ransomware attacks. It spreads through phishing emails, encrypting sensitive data, and causing harm to the designated client. Most research in ransomware detection focuses on detecting during the encryption and post-attack phase. However, the damage done by crypto-ransomware is almost impossible to reverse, and there is a need for an early detection mechanism. For early detection of crypto-ransomware, behavior-based detection techniques are the most effective. This work describes RENTAKA, a framework based on machine learning for the early detection of crypto-ransomware. The features extracted are based on the phases of the ransomware lifecycle. This experiment included five widely used machine learning classifiers: Naïve Bayes, kNN, Support Vector Machines, Random Forest, and J48. This study proposed a pre-encryption detection framework for crypto-ransomware using a machine learning approach. Based on our experiments, support vector machines (SVM) performed with the best accuracy and TPR, 97.05% and 0.995, respectively.