For in-vehicle network communication, the controller area network (CAN) broadcasts to all connected nodes without address validation. Therefore, it is highly vulnerable to all sorts of attack scenarios. This research proposes a novel intrusion detection system (IDS) for CAN to identify in-vehicle network anomalies. The statistical characteristics of attacks provide valuable information about the inherent intrusion patterns and behaviors. We employed two real-world attack scenarios from publicly available datasets to record a real-time response against intrusions with increased precision for in-vehicle network environments. Our proposed IDS can exploit malicious patterns by calculating thresholds and using the statistical properties of attacks, making attack detection more efficient. The optimized threshold value is calculated using brute-force optimization for various window sizes to minimize the total error. The reference values of normality require a few legitimate data frames for effective intrusion detection. The experimental findings validate that our suggested method can efficiently detect fuzzy, merge, and denial-of-service (DoS) attacks with low false-positive rates. It is also demonstrated that the total error decreases with an increasing attack rate for varying window sizes. The results indicate that our proposed IDS minimizes the misclassification rate and is hence better suited for in-vehicle networks.

In this paper, we propose an intrusion detection system based on the estimation of the Rényi entropy with multiple orders. The Rényi entropy is a generalized notion of entropy that includes the Shannon entropy and the min-entropy as special cases. In 2018, Kim proposed an efficient estimation method for the Rényi entropy with an arbitrary real order $\alpha$ . In this work, we utilize this method to construct a multiple order, Rényi entropy based intrusion detection system (IDS) for vehicular systems with various network connections. The proposed method estimates the Rényi entropies simultaneously with three distinct orders, two, three, and four, based on the controller area network (CAN)-IDs of consecutively generated frames. The collected frames are split into blocks with a fixed number of frames, and the entropies are evaluated based on these blocks. For a more accurate estimation against each type of attack, we also propose a retrospective sliding window method for decision of attacks based on the estimated entropies. For fair comparison, we utilized the CAN-ID attack data set generated by a research team from Korea University. Our results show that the proposed method can show the false negative and positive errors of less than 1% simultaneously.

In the tone reservation (TR) scheme, it is known that finding the optimal peak reduction tone (PRT) set is equivalent to solving the secondary peak minimization problem. However, this problem cannot be solved for the practical number of tones because it is nondeterministic polynomial-time (NP)-hard. In this paper, two efficient methods for selecting a near optimal PRT set are proposed. The first method is a random search algorithm with reduced computational complexity based on the observation that the secondary peak value of the time domain kernel, which is obtained by inverse fast Fourier transforming the characteristic sequence of the PRT set, statistically tends to decrease as the variance of the time domain kernel decreases. The second method is a deterministic selection algorithm using the cyclic difference set. The near optimality of these methods is confirmed through the numerical analysis.

In the tone reservation (TR) scheme of the orthogonal frequency division multiplexing (OFDM) systems, there exists a trade-off between the peak to average power ratio (PAPR) reduction performance and the peak reduction tone (PRT) set size. In this paper, we propose a multi-stage TR scheme for PAPR reduction, which adaptively selects one of several PRT sets according to the PAPR of OFDM signal while the PRT set is fixed for the conventional TR scheme. It is shown that the PAPR reduction performance of the proposed scheme is better than that of the conventional TR scheme when the tone reservation rate (TRR) is the same.

Driver behavior features extracted from the controller area network (CAN) have potential applications in improving vehicle safety. However, the development of a classifier-based IDS for in-vehicle networks remains an open research problem. To address this challenge, we incorporate novel n -fold cross-validation windowing techniques on two publicly available driving behavior datasets. A driver classification-based IDS is proposed using the LSTM-FCN model that utilizes the strengths of both fully convolutional network (FCN) and long short-term memory (LSTM) networks. These modules allow the model to learn spatial and temporal features and utilize contextual information. In addition, we combine three squeeze and excite (SnE) layers following FCN layers to incorporate adjacent spatial locations and augment a scaled dot product attention mechanism into the LSTM to improve its feature selection and extraction capabilities. Our proposed IDS uses hacking and countermeasure research lab (HCRL) and test datasets, which achieve an improvement in accuracy of 4.18% and 13.99% respectively, from the baseline LSTM-FCN model. The experimental results of our method exhibited an overall accuracy of 99.36% and 96.36% for both datasets and outperformed various state-of-the-art methods.

In this paper, we propose a method to determine the optimal cyclic delay value of cyclic delay diversity (CDD) in orthogonal frequency division multiplexing (OFDM) systems. As the cyclic delay value increases, we can get signal to interference and noise ratio (SINR) gain by the diversity effect, while SINR loss increases because of channel estimation errors. If the optimal cyclic delay value obtained by the proposed method is applied to a CDD scheme, we can minimize the required SINR for a given frame error rate (FER) under the above-mentioned trade-off.