Collaborating Authors


Machine Learning based Anomaly Detection for 5G Networks Machine Learning

Protecting the networks of tomorrow is set to be a challenging domain due to increasing cyber security threats and widening attack surfaces created by the Internet of Things (IoT), increased network heterogeneity, increased use of virtualisation technologies and distributed architectures. This paper proposes SDS (Software Defined Security) as a means to provide an automated, flexible and scalable network defence system. SDS will harness current advances in machine learning to design a CNN (Convolutional Neural Network) using NAS (Neural Architecture Search) to detect anomalous network traffic. SDS can be applied to an intrusion detection system to create a more proactive and end-to-end defence for a 5G network. To test this assumption, normal and anomalous network flows from a simulated environment have been collected and analyzed with a CNN. The results from this method are promising as the model has identified benign traffic with a 100% accuracy rate and anomalous traffic with a 96.4% detection rate. This demonstrates the effectiveness of network flow analysis for a variety of common malicious attacks and also provides a viable option for detection of encrypted malicious network traffic.

How MIT researchers use machine learning to detect IP hijackings before they occur


The internet uses routing tables to determine how and where data is sent and received. Without accurate and reliable tables, the internet would be like a highway system with no signs or signals to direct the traffic to the right places. Of course, cybercriminals find a way to corrupt just about everything that makes the internet work, and routing is no exception. IP hijacking, or BGP (Border Gateway Protocol) hijacking, is a process in which hackers and cybercriminals take over groups of IP addresses by corrupting the routing tables that use BGP. The purpose is to redirect traffic on the public internet or on private business networks to the hijackers' own networks where they can intercept, view, and even modify the packets of data.

The 2018 Survey: AI and the Future of Humans


"Please think forward to the year 2030. Analysts expect that people will become even more dependent on networked artificial intelligence (AI) in complex digital systems. Some say we will continue on the historic arc of augmenting our lives with mostly positive results as we widely implement these networked tools. Some say our increasing dependence on these AI and related systems is likely to lead to widespread difficulties. Our question: By 2030, do you think it is most likely that advancing AI and related technology systems will enhance human capacities and empower them? That is, most of the time, will most people be better off than they are today? Or is it most likely that advancing AI and related technology systems will lessen human autonomy and agency to such an extent that most people will not be better off than the way things are today? Please explain why you chose the answer you did and sketch out a vision of how the human-machine/AI collaboration will function in 2030.

MIT CSAIL's AI detects possible IP address hijacking


Border gateway protocol (BGP), a routing protocol used to transfer data and information between different host gateways, is fundamental to the internet's design. Unfortunately, it's flawed in two respects: It lacks route authentication and basic origin validation. That makes BGP liable to cause connectivity issues in the event of misconfigurations, and worrisomely opens the door to malicious spammers, traffic interceptors, and cryptocurrency thieves. That's why researchers at MIT's Computer Science and Artificial Intelligence Lab recently conducted a study of BGP activity over the course of five years, with the goal of identifying the dominant characteristics of hijackers and how they differ from legitimate systems. The work informed a set of metrics to which the team applied an AI algorithm to evaluate their accuracy in identifying hijackers' patterns.

Visual Analytics of Anomalous User Behaviors: A Survey Machine Learning

The increasing accessibility of data provides substantial opportunities for understanding user behaviors. Unearthing anomalies in user behaviors is of particular importance as it helps signal harmful incidents such as network intrusions, terrorist activities, and financial frauds. Many visual analytics methods have been proposed to help understand user behavior-related data in various application domains. In this work, we survey the state of art in visual analytics of anomalous user behaviors and classify them into four categories including social interaction, travel, network communication, and transaction. We further examine the research works in each category in terms of data types, anomaly detection techniques, and visualization techniques, and interaction methods. Finally, we discuss the findings and potential research directions.

NEC chosen for NSW Police radio network upgrade


NEC Australia has announced being chosen by New South Wales Police Force to upgrade its microwave radio communications network using its iPasolink VR platform and supplying 110 iPasolink terminals to the New England region. According to NEC, the platform "enables the seamless upgrade of the radio network's capacity, effectively future-proofing communications, on demand", with the IT provider already having supplied 180 terminals to NSW Police. "The robust iPasolink Outdoor Units (ODUs) ... are designed to operate in the harshest environmental conditions," NEC said. "Crucial to the iPasolink selection is its small size which reduces tower load and eliminates the high cost of strengthening towers to accommodate larger, heavier equipment." The radio comms network is used by 20,000 NSW Police staffers, NEC said, and in 500 police stations across the state.