Dr. Di Ma from the Internet DNS Beijing Engineering Research Center (ZDNS) has completed the report for one of the grants that was allocated in 2018 for implementation in 2019, titled “RPKI Monitor and Visualizer for Detecting and Alerting for RPKI Errors”.
This project implements an RPKI security mechanism that detects and counters adverse actions in the RPKI, which helps mitigate risks to global routing system. The mechanism is implemented by two components: the monitor, which detects erroneous or malicious RPKI changes, and the visualizer, which displays graphically the validation process passed to it by the validator and the alert information issued by the monitor.
The project achieved the following objectives:
- Develop an RPKI Monitor to detect RPKI problems due to mistakes by or attacks against CAs and repositories, and generate alerts to the affected parties to remedy the problems. It also provides suggestions to guide RPs in deciding whether to accept or defer accepting those changes.
- Develop an RPKI Visualizer to display graphically the validation process and involved RPKI data passed to it by the validator and the alert information issued by the Monitor.
The report is publicly available.
Prof. Winston Seah from the School of Engineering and Computer Science at the Victoria University of Wellington has completed the report for one of the grants that was allocated in 2017 for implementation in 2018, titled “Scalable Traffic Classification in Internet of Things (IoT) for Network Anomaly Detection”.
The project focused on accurate traffic classification in the Internet of Things (IoT). The IoT comprises large numbers of heterogeneous simple devices running single applications, often with little to no security features making them easily compromised and used as tools in cyberattacks. As we become more connected and reliant on the Internet, any form of disruption in connectivity due network anomalies can result in adverse consequences, ranging from loss of productivity and revenue, to destruction of critical infrastructure and loss of life. In the last decade, cyberattacks have increased at an alarming rate, even just based on the reported incidents. We need to be able to classify new traffic types coming from IoT devices accurately and promptly, so that anomalous traffic can be identified and dealt with quickly.
Payload-based (PB) techniques although can reach high accuracy, but suffers from several limitations. The limitations of PB classification are expected to be addressed by statistical-based (SB) techniques. SB approaches are based on flow features and the traffic is classified using Machine Learning algorithms (MLAs). SB classification assumes that specific flow-level features such as flow duration, inter-arrival time, transmitted bytes, packet length and packet size can distinguish different types of traffic flows. We studied how unsupervised machine learning can be applied to network anomaly detection in the dynamic IoT environment where previously unencountered traffic types and patterns are regularly emerging and need to be identified and classified. This project involves the study and selection of appropriate MLAs (to be implemented as a proof-of-concept prototype) and identification of those flow features which have the highest impact on the traffic classification accuracy. This project contributes to making safer cyber-physical systems that are an integral component of the IoT.
The report is publicly available.
Prof. Vijay Varadharajan from the Faculty of Engineering and Built Environment at The University of Newcastle has completed the report for one of the grants that was allocated for implementation in 2018, titled “Software Defined Networks based Security Architecture for IoT Infrastructures”.
The project developed fine granular security policies and a lightweight security protocol to authenticate IoT devices and authorise them to access services in network infrastructure in a secure manner. The project involved three stages:
- In the first stage, the project team conducted a detailed study of security attacks on IoT infrastructures and the different security solutions that currently exist to counteract the various types of attacks. Then, analysed the pros and cons of the existing solutions, and developed security requirements that need to be addressed in designing security architecture for IoT Applications.
- As part of second stage, the team developed a lightweight authentication protocol based on a novel public key encryption scheme. The proposed protocol achieved a balance between the efficiency and communication cost without sacrificing security.
- In the third stage, the team proposed a SDN based security architecture for IoT systems. Their security architecture allowed specification of fine granular access policy constraints on communications between end users, devices and services in a distributed environment. A novel feature of the proposed architecture is its ability to specify path based security policies, which is a distinct advantage in SDNs.
The report is publicly available here: