Biblio
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2021. Predicting Cyber Attacks in a Proxy Server using Support Vector Machine (SVM) Learning Algorithm. 2021 IST-Africa Conference (IST-Africa). :1–11.
This study used the support vector machine (SVM) algorithm to predict Denial of Service (DoS) and Distributed Denial of Service (DDoS) attacks on a proxy server. Proxy-servers are prone to attacks such as DoS and DDoS and existing detection and prediction systems are inefficient. Three convex optimization problems using the Gaussian, linear and non-linear kernel methods were solved using the SVM module to detect the attacks. The SVM module and proxy server were implemented in Python and javascript respectively and made to run on a local network. Four other computers running on the same network where made to each communicate with the proxy server (two dedicated to attack the server). The server was able to detect and filter out the malicious requests from the attacking clients. Hence, the SVM module can effectively predict cyber attacks and can be integrated into any server to detect such attacks for improved security.
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2020. Identifying Ubiquitious Third-Party Libraries in Compiled Executables Using Annotated and Translated Disassembled Code with Supervised Machine Learning. 2020 IEEE Security and Privacy Workshops (SPW). :157–162.
The size and complexity of the software ecosystem is a major challenge for vendors, asset owners and cybersecurity professionals who need to understand the security posture of these systems. Annotated and Translated Disassembled Code is a graph based datastore designed to organize firmware and software analysis data across builds, packages and systems, providing a highly scalable platform enabling automated binary software analysis tasks including corpora construction and storage for machine learning. This paper describes an approach for the identification of ubiquitous third-party libraries in firmware and software using Annotated and Translated Disassembled Code and supervised machine learning. Annotated and Translated Disassembled Code provide matched libraries, function names and addresses of previously unidentified code in software as it is being automatically analyzed. This data can be ingested by other software analysis tools to improve accuracy and save time. Defenders can add the identified libraries to their vulnerability searches and add effective detection and mitigation into their operating environment.