Multitask Learning for Network Traffic Classification

Traffic classification has various applications in today's Internet, from resource allocation, billing and QoS purposes in ISPs to firewall and malware detection in clients. Classical machine learning algorithms and deep learning models have been widely used to solve the traffic classification task. However, training such models requires a large amount of labeled data. Labeling data is often the most difficult and time-consuming process in building a classifier. To solve this challenge, we reformulate the traffic classification into a multi-task learning framework where bandwidth requirement and duration of a flow are predicted along with the traffic class. The motivation of this approach is twofold: First, bandwidth requirement and duration are useful in many applications, including routing, resource allocation, and QoS provisioning. Second, these two values can be obtained from each flow easily without the need for human labeling or capturing flows in a controlled and isolated environment. We show that with a large amount of easily obtainable data samples for bandwidth and duration prediction tasks, and only a few data samples for the traffic classification task, one can achieve high accuracy. We conduct two experiment with ISCX and QUIC public datasets and show the efficacy of our approach

Cryptography and Its Applications in Information Security

Nowadays, mankind is living in a cyber world. Modern technologies involve fast communication links between potentially billions of devices through complex networks (satellite, mobile phone, Internet, Internet of Things (IoT), etc.). The main concern posed by these entangled complex networks is their protection against passive and active attacks that could compromise public security (sabotage, espionage, cyber-terrorism) and privacy. This Special Issue “Cryptography and Its Applications in Information Security” addresses the range of problems related to the security of information in networks and multimedia communications and to bring together researchers, practitioners, and industrials interested by such questions. It consists of eight peer-reviewed papers, however easily understandable, that cover a range of subjects and applications related security of information

Graph-Based Machine Learning for Passive Network Reconnaissance within Encrypted Networks

Network reconnaissance identifies a network’s vulnerabilities to both prevent and mitigate the impact of cyber-attacks. The difficulty of performing adequate network reconnaissance has been exacerbated by the rising complexity of modern networks (e.g., encryption). We identify that the majority of network reconnaissance solutions proposed in literature are infeasible for widespread deployment in realistic modern networks. This thesis provides novel network reconnaissance solutions to address the limitations of the existing conventional approaches proposed in literature. The existing approaches are limited by their reliance on large, heterogeneous feature sets making them difficult to deploy under realistic network conditions. In contrast, we devise a bipartite graph-based representation to create network reconnaissance solutions that rely only on a single feature (e.g., the Internet protocol (IP) address field). We exploit a widely available feature set to provide network reconnaissance solutions that are scalable, independent of encryption, and deployable across diverse Internet (TCP/IP) networks. We design bipartite graph embeddings (BGE); a graph-based machine learning (ML) technique for extracting insight from the structural properties of the bipartite graph-based representation. BGE is the first known graph embedding technique designed explicitly for network reconnaissance. We validate the use of BGE through an evaluation of a university’s enterprise network. BGE is shown to provide insight into crucial areas of network reconnaissance (e.g., device characterisation, service prediction, and network visualisation). We design an extension of BGE to acquire insight within a private network. Private networks—such as a virtual private network (VPN)—have posed significant challenges for network reconnaissance as they deny direct visibility into their composition. Our extension of BGE provides the first known solution for inferring the composition of both the devices and applications acting behind diverse private networks. This thesis provides novel graph-based ML techniques for two crucial aims of network reconnaissance—device characterisation and intrusion detection. The techniques developed within this thesis provide unique cybersecurity solutions to both prevent and mitigate the impact of cyber-attacks.Thesis (Ph.D.) -- University of Adelaide, School of Electrical and Electronic Engineering , 202