Report available! A novel graph analytics theory model to mitigate IoT botnets attacks for big data

The project led by Raihana Syahirah Abdullah, from the Faculty of Information Technology and Communication at the Universiti Teknikal Malaysia Melaka (UTeM) finalized their project and submitted their technical report.

The research focused on getting the parameter from raw infection codes using a reverse engineering approach as well as addressing the behaviours of IoT botnets. The main objective of the research was to develop a new model in detecting IoT botnets using graph analytics theory model with analysing the selection of influence feature factor. The output of this research is a scheme that is able to remove and quarantine the suspicious codes as well as able to detect the behaviour changes in the IoT devices. The model also can be used as a security tool to discover the real behaviors of IoT botnets from the raw infection codes that exists in particular IoT devices and machines.

The report is publicly available.

Report available! A peering strategy for the Pacific Islands

Many telecommunications networks in the Pacific interconnect not directly but via international carriers in the United States or Australia. This has a profound impact on both the cost and the performance of regional traffic. While web traffic is slowed, real-time collaborations are rendered unusable, creating barriers for inter-island collaboration.

Governments, competitive carriers, Internet societies, and activists argue that direct interconnection, or peering, is the answer to these performance problems. They believe that if competitive networks are allowed to exchange traffic free-of-charge with incumbent networks, the cost of Internet will go down, and performance will go up.

Incumbent networks throughout the Pacific steadfastly refuse to openly peer with other carriers, education networks, and government networks – and a change in this behaviour is not in sight. Not only do they refuse to peer, they sometimes charge their competitors more for direct access to their networks than competitors pay for global Internet connectivity. Competitors, activists, and even governments say this is a clear violation of network neutrality. This project investigating carrier interconnections in the Pacific has shown the situation to be far more nuanced.

This project’s objective was to share research collected during an earlier iteration of the project via the web in a dynamic way. This included information on physical and routed topologies, telecommunications market data, and information on the relationships Pacific Island nations have with the rest of the world.

In support of these objectives, the project has produced a website that reviews the telecommunications environment of the Pacific Islands. The site looks at each market’s connectivity to the world: telecommunications, sea freight, air routes, and trade. It provides real-time statistics on carrier market share. Finally, it considers the complexity of island telecommunications through a composite case study on peering.

The report is publicly available.

Report available! RPKI Monitor and Visualizer for Detecting and Alerting for RPKI Errors

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.

Report available! Scalable Traffic Classification in Internet of Things (IoT) for Network Anomaly Detection

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.

Report available! Software Defined Networks based Security Architecture for IoT Infrastructures

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: