Técnicas de machine Learning para la detección de Ransomware: Revisión sistemática de Literatura

El ransomware es uno de los problemas de seguridad informática más críticos, es un tipo de malware que cifra o bloquea la información de la víctima para solicitar el pago de un rescate y devolverles el acceso a sus datos. La presente investigación tuvo el propósito de identificar las técnicas y/o algoritmos de Machine Learning (ML) utilizadas para la detección y clasificación de las diferentes familias ransomware, así como las herramientas de software que se utilizan para la aplicación de estos algoritmos. Está revisión sistemática de literatura (RSL) se apoyó en la metodología propuesta por Bárbara Kitchenham y en el uso de la herramienta Parsifal. Los resultados obtenidos muestran que los algoritmos y/o técnicas de machine learning más utilizados son: Random Forest (RF) con el 23 %, Decisión Tree (DT) con un 14 %, Long Short-Term Memory (LSTM) utilizado en un 9 %, Support Vector Machine Learning (SVM) y Deep Neural Network (DNN) con el 6 %. Las herramientas más utilizadas para la aplicación de los algoritmos de machine learning, fueron Cuckoo Sandbox y Weka Framework con el 17 %. Llegando a la conclusión que el machine learning permite detectar en las etapas iniciales patrones de diferentes familias ransomware

Ransomware and Malware Sandboxing

The threat of ransomware that encrypts data on a device and asks for payment to decrypt the data affects individual users, businesses, and vital systems including healthcare. This threat has become increasingly more prevalent in the past few years. To understand ransomware through malware analysis, care must be taken to sandbox the ransomware in an environment that allows for a detailed and comprehensive analysis while also preventing it from being able to further spread. Modern malware often takes measures to detect whether it has been placed into an analysis environment to prevent examination. In this work, several notable pieces of ransomware were placed into sandbox environments to discover how they might obfuscate themselves for evading analysis and to determine ways they propagate. The goal of the work is to identify and understand these how these obfuscation and propagation techniques function in a sandbox, so that mitigation methods can be developed

A fine-tuning of decision tree classifier for ransomware detection based on memory data

Ransomware has evolved into a pervasive and extremely disruptive cybersecurity threat, causing substantial operational and financial damage to individuals and businesses. This article explores the critical domain of Ransomware detection and employs Machine Learning (ML) classifiers, particularly Decision Tree (DT), for Ransomware detection. The article also delves into the usefulness of DT in identifying Ransomware attacks, leveraging the innate ability of DT to recognize complex patterns within datasets. Instead of merely introducing DT as a detection method, we adopt a comprehensive approach, emphasizing the importance of fine-tuning DT hyperparameters. The optimization of these parameters is essential for maximizing the DT capability to identify Ransomware threats accurately. The obfuscated-MalMem2022 dataset, which is well-known for its extensive and challenging Ransomware-related data, was utilized to evaluate the effectiveness of DT in detecting Ransomware. The implementation uses the versatile Python programming language, renowned for its efficiency and adaptability in data analysis and ML tasks. Notably, the DT classifier consistently outperforms other classifiers in Ransomware detection, including K-Nearest Neighbors, Gradient Boosting Tree, Naive Bayes, and Linear Support Vector Classifier. For instance, the DT demonstrated exceptional effectiveness in distinguishing between Ransomware and benign data, as evidenced by its remarkable accuracy of 99.97%

Análisis de los ataques tipo Ransomware realizados durante el Covid 19 a las Mipymes colombianas, por causa de vulnerabilidades presentes en las infraestructuras TI y en el proceso de transformación digital en las organizaciones

Investigar y analizar el comportamiento del Ransomware (Secuestro de archivos) en las empresas de Bogotá, identificando su entrega, despliegue, Destrucción y Negociación, para obtener los accesos necesarios para encriptar los archivos aprovechando las vulnerabilidades de las compañías, siendo muy rentable en pago con criptomonedas para los ciberdelincuentes y provocando daños graves en las empresas colombianas.Investigate and analyze the behavior of Ransomware (File Hijacking) in companies in Bogotá, identifying its delivery, deployment, Destruction and Negotiation, to obtain the necessary access to encrypt the files taking advantage of the vulnerabilities of the companies, being very profitable in payment with cryptocurrencies for cybercriminals and causing serious damage to Colombian companies

Developing an Effective Detection Framework for Targeted Ransomware Attacks in Brownfield Industrial Internet of Things

The Industrial Internet of Things (IIoT) is being interconnected with many critical industrial activities, creating major cyber security concerns. The key concern is with edge systems of Brownfield IIoT, where new devices and technologies are deployed to interoperate with legacy industrial control systems and leverage the benefits of IoT. These edge devices, such as edge gateways, have opened the way to advanced attacks such as targeted ransomware. Various pre-existing security solutions can detect and mitigate such attacks but are often ineffective due to the heterogeneous nature of the IIoT devices and protocols and their interoperability demands. Consequently, developing new detection solutions is essential. The key challenges in developing detection solutions for targeted ransomware attacks in IIoT systems include 1) understanding attacks and their behaviour, 2) designing accurate IIoT system models to test attacks, 3) obtaining realistic data representing IIoT systems' activities and connectivities, and 4) identifying attacks. This thesis provides important contributions to the research focusing on investigating targeted ransomware attacks against IIoT edge systems and developing a new detection framework. The first contribution is developing the world's first example of ransomware, specifically targeting IIoT edge gateways. The experiments' results demonstrate that such an attack is now possible on edge gateways. Also, the kernel-related activity parameters appear to be significant indicators of the crypto-ransomware attacks' behaviour, much more so than for similar attacks in workstations. The second contribution is developing a new holistic end-to-end IIoT security testbed (i.e., Brown-IIoTbed) that can be easily reproduced and reconfigured to support new processes and security scenarios. The results prove that Brown-IIoTbed operates efficiently in terms of its functions and security testing. The third contribution is generating a first-of-its-kind dataset tailored for IIoT systems covering targeted ransomware attacks and their activities, called X-IIoTID. The dataset includes connectivity- and device-agnostic features collected from various data sources. The final contribution is developing a new asynchronous peer-to-peer federated deep learning framework tailored for IIoT edge gateways for detecting targeted ransomware attacks. The framework's effectiveness has been evaluated against pre-existing datasets and the newly developed X-IIoTID dataset