Threat intelligence using machine learning packet dissection

Sowinski-Mydlarz, Viktor, Li, Jun, Ouazzane, Karim and Vassilev, Vassil (2021) Threat intelligence using machine learning packet dissection. In: 20th International Conference on Security & Management (SAM'21), 26 - 29 July 2021, Las Vegas, USA. (In Press)


In this research we compare different methods to examine network packets using supervised learning to predict possible intrusions. Although there have been many attempts to use Machine Learning for automated packet analysis, our application simplifies the process by taking any packet data source for analysis in a container ready for deploying on a private or public cloud without the need to pre-process the packet data. The packet is dissected extracting numerical data, describing the packet numbers, the time and length of the packets. Categorical variables are the source and destination IP addresses, protocol used and packet info/flag. The use of filters allows to recognize any type of packet.

Four machine learning models, i.e., Neural Networks, Support Vector Machines, Logistic Regression and Linear Regression, are applied respectively to calculate the probability of suspicious packets. Subsequently, the outcomes are compared. In default mode, the suspicious packets and their context of source, destination, length, and protocol are discovered. During the testing against trojan malware, the models can detect the suspicious packets sent to a bogus website and attempts at downloading malware by means of packet payload analysis. The initial Neural Network model shows an accuracy of 85% on testing data, which is further enhanced with the incremental learning cycles to 88% after 20 updates with class weighting. The Support Vector Machine model performs slightly better than the initial Neural Network with an accuracy of 92%, while the Logistic Regression and Linear Regression models perform faster but with a lower accuracy at 70%..

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