Accountability and Reliability in 6G O-RAN: Who is Responsible When it Fails?

Ying He, Guangsheng Yu, Xu Wang, Qin Wang, Zijian Niu, Wei Ni, Ren Ping Liu

IEEE Wireless Communications

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. This article analyzes the use cases and risks of multi-party collaboration in O-RAN and discusses its potential enabling technologies, including blockchain and machine learning, to address the accountability and reliability concerns around O-RAN. A feasibility study of a blockchain and large language model (LLM)-powered O-RAN is conducted to demonstrate the effectiveness of the concept. Performance evaluations show that these technologies can enhance the accountability and reliability of O-RAN, with certification processes of blockchain and customization of fine-tuning LLMs.

CAN-Trace Attack: Exploit CAN Messages to Uncover Driving Trajectories

Xiaojie Lin, Baihe Ma, Xu Wang, Guangsheng Yu, Ying He, Wei Ni, Ren Ping Liu

IEEE Transactions on Intelligent Transportation Systems

Driving trajectory data remains vulnerable to privacy breaches despite existing mitigation measures. Traditional methods for detecting driving trajectories typically rely on map-matching the path using Global Positioning System (GPS) data, which is susceptible to GPS data outage. This paper introduces CAN-Trace, a novel privacy attack mechanism that leverages Controller Area Network (CAN) messages to uncover driving trajectories, posing a significant risk to drivers’ long-term privacy. A new trajectory reconstruction algorithm is proposed to transform the CAN messages, specifically vehicle speed and accelerator pedal position, into weighted graphs accommodating various driving statuses. CAN-Trace identifies driving trajectories using graph-matching algorithms applied to the created graphs in comparison to road networks. We also design a new metric to evaluate matched candidates, which allows for potential data gaps and matching inaccuracies. Empirical validation under various real-world conditions, encompassing different vehicles and driving regions, demonstrates the efficacy of CAN-Trace: it achieves an attack success rate of up to 90.59% in the urban region, and 99.41% in the suburban region

FedNIFW: Non-Interfering Fragmented Watermarking for Federated Deep Neural Network

Haiyu Deng, Xiaocui Dang, Yanna Jiang, Xu Wang, Guangsheng Yu, Wei Ni, Ren Ping Liu

2024 IEEE 23rd International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom)

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 may embed watermarks in the same region of the model. During the aggregation of the watermarked weights, the watermarks from various clients may overlap, resulting in conflicts between the embedded watermarks. To overcome these challenges, we propose a novel method called Non-Interfering Fragmented Watermarking for Federated Models (FedNIFW). In the proposed scheme, each client node is assigned a specific segment of the neural network layer where watermarking can be applied. During training, each client is allowed to embed watermarks only within their designated segments, while other segments intended for watermarking by different clients are frozen. Experimental results demonstrate that this segmented watermarking scheme effectively prevents conflicts between client watermarks and does not significantly impact the accuracy of the federated models. These findings underscore the feasibility of the proposed watermarking scheme.

SMAKAP: Secure Mutual Authentication and Key Agreement Protocol for RFID Systems

Shayesta Naziri; Xu Wang; Guangsheng Yu; Jian Xu; Sudhir Shrestha; Christy Jie Liang

2024 17th International Conference on Security of Information and Networks (SIN)

Radio Frequency Identification (RFID) is a crucial technology in the Internet of Things (IoT), enabling seamless wireless communication and data exchange. However, these technologies can pose significant security chal-lenges if not implemented with proper attention to security protocols-especially in communication, where pre-shared keys are not used between active tags and readers for device authentication. Some recent authentication protocols rely solely on a hash function, nonce, and single public kay agreement, which can lead to failure to implement robust security and proper authentication or ineffective for high security application environments. To effectively address these challenges this paper proposes a secure Elliptic Curve Cryptography (ECC) based lightweight mutual authentication protocol utilizing a hybrid key agreement protocol between active tag and reader for secure communication in RFID-enabled devices in the IoT environments. The informal analysis demonstrates a secure communication environment for data privacy and flexibility through effective key management. This protocol is adaptable to various applications by addressing specific requirements and limitations.

Preventing harm to the rare in combating the malicious: A filtering-and-voting framework with adaptive aggregation in federated learning

Yanna Jiang, Baihe Ma, Xu Wang, Guangsheng Yu, Caijun Sun, Wei Ni, and Ren Ping Liu

Neurocomputing

The distributed nature of Federated Learning (FL) introduces security vulnerabilities and issues related to the heterogeneous distribution of data. Traditional FL aggregation algorithms often mitigate security risks by excluding outliers, which compromises the diversity of shared information. In this paper, we introduce a novel filtering-and-voting framework that adeptly navigates the challenges posed by non-iid training data and malicious attacks on FL. The proposed framework integrates a filtering layer for defensive measures against the intrusion of malicious models and a voting layer to harness valuable contributions from diverse participants. Moreover, by employing Deep Reinforcement Learning (DRL) for dynamic aggregation weight adjustment, we ensure the optimized aggregation of participant data, enhancing the diversity of information used for aggregation and improving the performance of the global model. Experimental results demonstrate that the proposed framework presents superior accuracy over traditional and contemporary FL aggregation methods as diverse models are utilized. It also shows robust resistance against malicious poisoning attacks.

Fishers Harvest Parallel Unlearning in Inherited Model Networks

Xiao Liu, Mingyuan Li, Xu Wang, Guangsheng Yu, Wei Ni, Lixiang Li, Haipeng Peng, Renping Liu

arxiv

Unlearning in various learning frameworks remains challenging, with the continuous growth and updates of models exhibiting complex inheritance relationships. This paper presents a novel unlearning framework, which enables fully parallel unlearning among models exhibiting inheritance. A key enabler is the new Unified Model Inheritance Graph (UMIG), which captures the inheritance using a Directed Acyclic Graph (DAG).Central to our framework is the new Fisher Inheritance Unlearning (FIUn) algorithm, which utilizes the Fisher Information Matrix (FIM) from initial unlearning models to pinpoint impacted parameters in inherited models. By employing FIM, the FIUn method breaks the sequential dependencies among the models, facilitating simultaneous unlearning and reducing computational overhead. We further design to merge disparate FIMs into a single matrix, synchronizing updates across inherited models. Experiments confirm the effectiveness of our unlearning framework. For single-class tasks, it achieves complete unlearning with 0% accuracy for unlearned labels while maintaining 94.53% accuracy for retained labels on average. For multi-class tasks, the accuracy is 1.07% for unlearned labels and 84.77% for retained labels on average. Our framework accelerates unlearning by 99% compared to alternative methods.

ByCAN: Reverse Engineering Controller Area Network (CAN) Messages from Bit to Byte Level

Xiaojie Lin, Baihe Ma, Xu Wang, Guangsheng Yu, Ying He, Ren Ping Liu, Wei Ni

IEEE Internet of Things Journal

As the primary standard protocol for modern cars, the controller area network (CAN) is a critical research target for automotive cybersecurity threats and autonomous applications. As the decoding specification of CAN is a proprietary black-box maintained by original equipment manufacturers (OEMs), conducting related research and industry developments can be challenging without a comprehensive understanding of the meaning of CAN messages. In this article, we propose a fully automated reverse-engineering system, named ByCAN, to reverse engineer CAN messages. ByCAN outperforms the existing research by introducing byte-level clusters and integrating multiple features at both the byte and bit levels. ByCAN employs the clustering and template matching algorithms to automatically decode the specifications of CAN frames without the need for prior knowledge. Experimental results demonstrate that ByCAN achieves high accuracy in slicing and labeling performance, i.e., the identification of CAN signal boundaries and labels. In the experiments, ByCAN achieves slicing accuracy of 80.21%, slicing coverage of 95.21%, and labeling accuracy of 68.72% for the general labels when analysing the real-world CAN frames.

GNN-Based Network Traffic Analysis for the Detection of Sequential Attacks in IoT

Tanzeela Altaf, Xu Wang, Wei Ni, Guangsheng Yu, Ren Ping Liu, Robin Braun

Electronics

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 attacks. Key to our approach is the development of a time-stamped multi-edge graph structure that uncovers subtle temporal patterns and hidden relationships in network flows, critical for recognizing botnet behaviors. Moreover, our sequential graph learning framework incorporates time-sequenced edges and multi-edged structures into a two-layered gated graph model, which is optimized with specialized message-passing layers and aggregation functions to address the challenges of time-series traffic data effectively. Our comparative analysis with the state of the art reveals that our sequential gated graph convolutional neural network achieves substantial improvements in detecting IoT botnets. The proposed GGCN model consistently outperforms the conventional model, achieving improvements in accuracy ranging from marginal to substantial—0.01% for BoT IoT and up to 25% for Mirai. Moreover, our empirical analysis underscores the GGCN’s enhanced capabilities, particularly in binary classification tasks, on imbalanced datasets. These findings highlight the model’s ability to effectively navigate and manage the varying complexity and characteristics of IoT security threats across different datasets