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Researcher profile

Robert Abbas

Robert Abbas contributes to research discovery and scholarly infrastructure.

ResearcherAffiliation not importedOpen to collaborate
Cryptography and SecurityNetworking and Internet Architectureeess.SPMachine LearningInformation Theorymath.IT

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Published work

6 published item(s)

preprint2026arXiv

A Comparative Analysis of Machine Learning Models for Intrusion Detection in Intelligent Transport Systems

AI-powered edge computing security is moving Intelligent Transportation Systems (ITS) from passive, rule-based protections to proactive, smart, zero-touch, self-sufficient safeguards that neutralize threats in milliseconds. As transportation becomes more connected with edge computing, massive IoT, and advanced 5G for vehicle-to-everything (V2X) connectivity, AI at the edge computing nodes plays a crucial role in protecting against sophisticated threats, enabling URLLC (ultra-low-latency communications) for smart transport, and enhancing infrastructure capabilities and safety. This research applies edge computing to improve latency, bandwidth efficiency, and service responsiveness by moving processing closer to devices, gateways, and users. However, this shift also expands the cyberattack surface because edge nodes are distributed, heterogeneous, and often resource-constrained. The paper proposes a trust-aware federated hybrid intrusion detection framework in which a random forest, a decision tree, and a linear SVM network learn complementary traffic representations at each edge site, while a server performs trust-aware aggregation of local model updates.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

5G Coverage, Prediction, and Trial Measurements

When planning a 5G network in the sub-6GHz bands, similar cell planning techniques to LTE can be applied. Looking at the Australian environment, the n78 band (3.3-3.8GHz TDD) is approximately 1GHz higher than the 2.6GHz band used in existing LTE networks. As a result, the coverage footprint can be similar, and therefore co-locating 5G NR (New Radio) on existing LTE base stations is a common strategy for initial network rollout. Any difference in coverage can be compensated by beamforming gain, less downtilting, or increasing the gNodeB's transmit power. This paper presents an initial link budget for data services, provides a coverage prediction, and measurements for a 5G NR NSA (Non Stand Alone) trial radiating at 3.5GHz with 60 MHz bandwidth. The coverage prediction is generated using RF planning tool Atoll, which is then compared to coverage measurements from the trial. These findings can be used to help plan a future 5G network in the Sydney metro area or similar environment.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

6G White Paper on Machine Learning in Wireless Communication Networks

The focus of this white paper is on machine learning (ML) in wireless communications. 6G wireless communication networks will be the backbone of the digital transformation of societies by providing ubiquitous, reliable, and near-instant wireless connectivity for humans and machines. Recent advances in ML research has led enable a wide range of novel technologies such as self-driving vehicles and voice assistants. Such innovation is possible as a result of the availability of advanced ML models, large datasets, and high computational power. On the other hand, the ever-increasing demand for connectivity will require a lot of innovation in 6G wireless networks, and ML tools will play a major role in solving problems in the wireless domain. In this paper, we provide an overview of the vision of how ML will impact the wireless communication systems. We first give an overview of the ML methods that have the highest potential to be used in wireless networks. Then, we discuss the problems that can be solved by using ML in various layers of the network such as the physical layer, medium access layer, and application layer. Zero-touch optimization of wireless networks using ML is another interesting aspect that is discussed in this paper. Finally, at the end of each section, important research questions that the section aims to answer are presented.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

6G White paper: Research challenges for Trust, Security and Privacy

The roles of trust, security and privacy are somewhat interconnected, but different facets of next generation networks. The challenges in creating a trustworthy 6G are multidisciplinary spanning technology, regulation, techno-economics, politics and ethics. This white paper addresses their fundamental research challenges in three key areas. Trust: Under the current "open internet" regulation, the telco cloud can be used for trust services only equally for all users. 6G network must support embedded trust for increased level of information security in 6G. Trust modeling, trust policies and trust mechanisms need to be defined. 6G interlinks physical and digital worlds making safety dependent on information security. Therefore, we need trustworthy 6G. Security: In 6G era, the dependence of the economy and societies on IT and the networks will deepen. The role of IT and the networks in national security keeps rising - a continuation of what we see in 5G. The development towards cloud and edge native infrastructures is expected to continue in 6G networks, and we need holistic 6G network security architecture planning. Security automation opens new questions: machine learning can be used to make safer systems, but also more dangerous attacks. Physical layer security techniques can also represent efficient solutions for securing less investigated network segments as first line of defense. Privacy: There is currently no way to unambiguously determine when linked, deidentified datasets cross the threshold to become personally identifiable. Courts in different parts of the world are making decisions about whether privacy is being infringed, while companies are seeking new ways to exploit private data to create new business revenues. As solution alternatives, we may consider blockchain, distributed ledger technologies and differential privacy approaches.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Campus Wi-Fi Coverage Mapping and Analysis

Wireless Local Area Networks (WLANs), known as Wi-Fi, have become an essential service in university environments that helps staff, students and guests to access connectivity to the Internet from their mobile devices. Apart from the Internet being a learning resource, students also submit their assignments online using web portals. Most campuses will have poor coverage areas for mobile networks and, as a result, the ability of the wireless network to supplement Internet access for mobile devices in these areas becomes more important. Acquiring clear understanding of WLAN traffic patterns, network handover between access points and inter-network handover between the Wi-Fi and mobile networks, the optimal placement of networking equipment will help deliver a better wireless service. This paper presents data analyses and Wi-Fi signal coverage maps obtained by performing wireless radio surveys, coverage predictions and statistical analysis of data from the existing access points to show the current Wi-Fi performance in several locations of a large university campus. It them makes recommendations that should improve performance. These recommendations are derived from AP performance testing and made in the context of cabling length limitations and physical and aesthetic placement restrictions that are present at each location.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Machine Learning based Anomaly Detection for 5G Networks

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.

0 citations0 reviewsNo code linkedOpen access