Distributed Network Behaviour Prediction Using Machine Learning and AgentBased Micro Simulation Distributed network behaviour is increasingly attracting huge attention both in academics and industrial initiatives, and most recently machine learning has been used in every possible field, leveraging its advantages.Typically, a distributed networking allows for the execution of distributed applications which results in complex behaviours among connected systems. This complexity in itself can create grey areas and vulnerabilities in the securities of these networks, therefore predicting the behaviour of these systems both at the macro and micro level has become essential. In the past most researchers have predicted network behaviour and network attack patterns by using aggregated data, but in this paper we focus on the application of machine learning at the individual user level such that the prediction of the individual network user behaviour pattern at the micro level becomes a substasive tool in creating a realistic agent based simulation of the whole distributed network, which in turn can serve as a test bed for predicting whatif scenarios such as network attacks on the target system or exposing vulnerabilities within the target system. The simulation result was validated by comparing the simulated interaction within the simulated network to the data logged in the server logs within the real life network system. This produced a correlation above 0.8, indicating a realistic model.

Machine Learning for DDoS Attack Classification Using Hive Plots Cyberattacks have been on the increase as computing power and data storage have become more accessible. The use of recent advances in machine learning across different fields has increased the potential adoption of new algorithms in solving important technological problems. In this paper we describe a novel application of machine learning for the detection and classification of distributed denial of service (DDoS) cybersecurity attacks. Attack pattern training data is obtained from honeypots which we created to impersonate various APIs on a cloud computing network. Attack characteristics including source IP address, country of origin, and time of attack are collected from our honeypots and visualized using a threeaxis hive plot. We then implemented and trained a nonprobabilistic binary linear attack pattern classifier. A support vector machine and a convolutional neural network were trained using a supervised learning model with labeled data sets. Experimental results suggest that our models can detect DDoS attacks with high accuracy rates.