Security Projects
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CASPER AI: Context-Aware Security Policy Enforcement and Response
CASPER AI is a context-aware security platform developed at UTS through the Connectivity Innovation Network, with support from NSW Telco Authority. It connects fragmented organisational information so teams can make faster, safer and more policy-aligned security decisions. Why it matters In many organisations, important security information sits across different systems. One alert may look dangerous,…
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SMODS
The Secure Multi-Org Data-Sharing (SMODS) Project, is a game-changing data sharing platform which will enable secure and efficient data sharing between organisations. This project has a strong focus on real time multi media data sharing between Emergency Service Organisations (ESOs) for improved situational awareness during emergency events.
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Reverse Engineering Approaches to Vehicular Networks
This project aims at developing new highly efficient cyber schemes to reverse engineer vehicular network communications. We start with contemporary vehicle networks by reviewing and demonstrating existing methods to identify & understand black-box vehicular network communications and existing cyber defence schemes. Further research and development will expand this to future network designs, incorporate V2X connectivity…
Security Publications
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[MWC] Accountability and Reliability in 6G O-RAN: Who is Responsible When it Fails?
Future sixth-generation (6G) networks aim to enable new services with vastly different data rates, latencies, and scalability requirements. Open radio access network (O-RAN), a key architecture for 6G, provides flexibility, openness, and interoperability. However, the open architecture of O-RAN poses challenges for network accountability and reliability. …
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[TrustCom24] FedNIFW: Non-Interfering Fragmented Watermarking for Federated Deep Neural Network
During the deployment and utilization of federated models, they are susceptible to unauthorized theft or misuse. To address this issue, researchers have proposed the use of watermarking techniques to protect the Intellectual Property (IP) of the federated models. Nevertheless, traditional watermarking methods in federated learning have certain limitations. It is highly likely that different clients…
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[ProvSec24] Enabling Efficient Cross-Shard Smart Contract Calling via Overlapping
As blockchain networks grow, sharding offers a promising solution to scalability challenges by dividing the network into smaller segments. However, managing cross-shard transactions, especially those involving smart contract calling, introduces significant complexities due to the extensive coordination required between shards. This paper introduces a novel framework for blockchain architectures with overlapping shards to address these…
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[ELECTRONICS] GNN-Based Network Traffic Analysis for the Detection of Sequential Attacks in IoT
This research introduces a novel framework utilizing a sequential gated graph convolutional neural network (GGCN) designed specifically for botnet detection within Internet of Things (IoT) network environments. By capitalizing on the strengths of graph neural networks (GNNs) to represent network traffic as complex graph structures, our approach adeptly handles the temporal dynamics inherent to botnet…
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[Acm Comput Surv] Blockchained federated learning for internet of things: A comprehensive surveyfiltering-and-voting framework with adaptive aggregation in federated learning
The demand for intelligent industries and smart services based on big data is rising rapidly with the increasing digitization and intelligence of the modern world. This survey comprehensively reviews Blockchained Federated Learning (BlockFL) that joins the benefits of both Blockchain and Federated Learning to provide a secure and efficient solution for the demand. We compare…
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[ComNet] TbDd: A new trust-based, DRL-driven framework for blockchain sharding in IoT
Integrating sharded blockchain with IoT presents a solution for trust issues and optimized data flow. Sharding boosts blockchain scalability by dividing its nodes into parallel shards, yet it is vulnerable to the 1% attacks where dishonest nodes target a shard to corrupt the entire blockchain. Balancing security with scalability is pivotal for such systems. Deep…
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[ISCIT23] A Generative Adversarial Networks-Based Integer Overflow Detection Model for Smart Contracts
Due to the rapid development of blockchain technology in recent years, smart contracts have been widely applied in critical fields such as finance, insurance, healthcare, and the Internet of Things. However, smart contracts face increasingly serious security issues due to their unique operating environment and programming characteristics. We focus on Ethereum-based smart contracts and propose…
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[ISCIT23] A Novel Weights-less Watermark Embedding Method for Neural Network Models
Deep learning-based Artificial Intelligence (AI) technology has been extensively used recently. AI model theft is a regular occurrence. As a result, many academics focus their efforts on safeguarding the Intellectual Property (IP) of trained Neural Network (NN) models. The majority of the most recent white-box setting watermark embedding methods rely on modifying model weights. Weights…
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[TIST] Obfuscating the Dataset: Impacts and Applications
Obfuscating a dataset by adding random noises to protect the privacy of sensitive samples in the training dataset is crucial to prevent data leakage to untrusted parties when dataset sharing is essential. We conduct comprehensive experiments to investigate how the dataset obfuscation can affect the resultant model weights.
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[Chin. Phys. B.] Robustness of community networks against cascading failures with heterogeneous redistribution strategies
Network robustness is one of the core contents of complex network security research. This paper focuses on the robustness of community networks with respect to cascading failures, considering the nodes influence and community heterogeneity. A novel node influence ranking method, community-based Clustering–LeaderRank (CCL) algorithm, is first proposed to identify influential nodes in community networks.