Due to the rising number of sophisticated customer functionalities, electronic control units (ECUs) are increasingly integrated into modern automotive systems. However, the high connectivity between the in-vehicle and the external networks paves the way for hackers who could exploit in-vehicle network protocols' vulnerabilities. Among these protocols, the Controller Area Network (CAN), known as the most widely used in-vehicle networking technology, lacks encryption and authentication mechanisms, making the communications delivered by distributed ECUs insecure. Inspired by the outstanding performance of bidirectional encoder representations from transformers (BERT) for improving many natural language processing tasks, we propose in this paper “CAN-BERT”, a deep learning based network intrusion detection system, to detect cyber attacks on CAN bus protocol. We show that the BERT model can learn the sequence of arbitration identifiers (IDs) in the CAN bus for anomaly detection using the “masked language model” unsupervised training objective. The experimental results on the “Car Hacking: Attack & Defense Challenge 2020” dataset show that “CAN-BERT” outperforms state-of-the-art approaches. In addition to being able to identify in-vehicle intrusions in real-time within 0.8 ms to 3 ms w.r.t CAN ID sequence length, it can also detect a wide variety of cyberattacks with an F1-score of between 0.81 and 0.99.

Network Intrusion Detection Systems (NIDSs) are widely regarded as efficient tools for securing in-vehicle networks against diverse cyberattacks. However, since cyberattacks are always evolving, signature-based intrusion detection systems are no longer adopted. An alternative solution can be the deployment of deep learning based intrusion detection system which play an important role in detecting unknown attack patterns in network traffic. Hence, in this paper, we compare the performance of different unsupervised deep and machine learning based anomaly detection algorithms, for real-time detection of anomalies on the Audio Video Transport Protocol (AVTP), an application layer protocol implemented in the recent Automotive Ethernet based in-vehicle network. The numerical results, conducted on the recently published “Automotive Ethernet Intrusion Dataset show that deep learning models significantly outperfom other state-of-the art traditional anomaly detection models in machine learning under different experimental settings.

This paper proposes a new approach for Machine Learning (ML)-based beam alignment, for a single radio-frequency chain millimeter-wave (mmW) MIMO transmitter (Tx) and receiver (Rx), with massive antennas. Assuming (massive) codebooks of possible beams at Tx and Rx, we propose to sound a very small subset of beams from the Tx/Rx codebooks. We then use the SNR of the (subset of) sounded beams, to learn two ML models: Matrix Factorization (MF), and Nonnegative MF. Furthermore, we derive the update eqts for two optimization methods to solve the MF/Nonnegative MF optimization problems. While the first optimization method is shown to converge (and exhibits medium complexity), the second optimization method has negligible complexity (but lacks a convergence guarantee). Our extensive numerical results suggest that by sounding just 10% of the beams from the (large) Tx and Rx codebooks, MF and Nonnegative MF are able to predict the SNR of the remaining beams, with extremely high accuracy. This observation holds as the Tx/Rx codebook sizes vary from 64×64 to 1024 × 1024.

The growing number of Internet of Thing (IoT) and Ultra-Reliable Low Latency Communications (URLCC) use cases in next generation communication networks calls for the development of efficient Forward Error Correction (FEC) mechanisms. These use cases usually imply using short to mid-sized information blocks and requires low-complexity and/or fast decoding procedures. This paper investigates the joint learning of short to mid block-length coding schemes and associated Belief-Propagation (BP) like decoders using Machine Learning (ML) techniques. An interpretable auto-encoder (AE) architecture is proposed, ensuring scalability to block sizes currently challenging for ML-based linear block code design approaches. By optimizing a coding scheme w.r.t. the targeted decoder, the proposed system offers a good complexity/performance trade-off compared to various codes from literature with length up to 128 bits.