The growing significance of reliability and security in industrial control systems (ICS) has rendered it essential to investigate the integration of safety and security. However, safety and security were studied and discussed separately in previous studies. Meanwhile, there are some conflicts between safety and security in ICS, making the integration challenging to balance. In this paper, we put forward a scheme to incorporate safety and security in ICS utilizing fault trees and attack defense trees. We present a fault-attack defense tree model to evaluate safety and security. To solve the quantitative analysis issue, we adopted the fuzzy set theory and applied least squares to strengthen the confidence in the assessment outcomes. When conducting an integrated security and assurance assessment, our solution provides effective security countermeasures. In this paper, we prove that the suggested scheme can balance safety and security and properly assess the effectiveness of security countermeasures through experiments and analysis of the Tennessee Eastman system.

With the continuous advancement of automotive intelligence and ubiquitous connection, In-Vehicle Networks (IVN) are confronted with heightened security challenges. Attackers can inject false messages to manipulate or disrupt critical functional modules on the vehicle, leading to significant safety issues. Given the real-time requirements and resource constraints of the in-vehicle network, a lightweight Intrusion Detection System (IDS) is needed to deal with the security threats caused by attacks. This paper proposes an effective intrusion detection method to address these requirements. The method combines refined CAN traffic features with a lightweight Deep Learning (DL) network. The time interval series, ID and the CAN message payload are extracted by a T-shaped window, vectorized to form a one-dimensional data frame, and the detection is completed by a lightweight 1D deep learning model using efficient convolution calculation. In binary and multiclass experiments on two publicly available datasets, the feature refinement strategy is discussed. Compared to existing deep learning-based intrusion detection methods, our approach demonstrates advantages in both intrusion detection performance and computational complexity, highlighting its deployment potential in embedded IVN environments.

Random stepped-frequency chirp waveform (RSFCW) is capable of achieving high-resolution synthetic aperture radar (SAR) imagery as well as certain electronic countermeasure capabilities. The Doppler-dependent phase incoherence due to the platform motion leads to degradation of the frequency synthesis of RSFCW. In this letter, a coherence processing algorithm for RSFCWSAR is developed with full cell Doppler correction (FCDC). The waveform structure of RSFCW is exploited to decompose the signal into different Doppler cells, which paves a way to the FCDC in phase alignment for frequency synthesis. Extensive simulation has confirmed the superiority of our method’s performance in comparison to those of traditional techniques.

Conventional sparsity-driven synthetic aperture radar (SAR) imagery proceeds via ℓ1regularization, or named by least absolute shrinkage and selection operator (LASSO). However, followed by the enhanced sparse feature, structures of the scenes or targets of interests in weak scattering are easily lost. Therefore, it becomes difficult to make use of the high-resolution SAR data, even high costs have been paid for the resolution. In this paper, a novel structure-guaranteed SAR (SG-SAR) imaging algorithm is proposed by utilizing the morphology metric for the cluster feature of the scatterers of the scenes/targets of interests. By introducing structural prior in terms of morphology norm, the intended structure features can be highlighted via convex regularization. More specifically, to accommodate to complicated scenarios or targets, the structure element in the morphology regularizer is designed to be spatially variant under structure tensor representation. Different from conventional convex optimizations, the proposed SG-SAR algorithm is solved under alternating direction method of multipliers (ADMM), which is flexible to incorporate with the super-resolution imagery. In such cases, both sparse and structural features can be simultaneously enhanced, even with limited measurements and in low signal-to-noise ratio (SNR). Superior convergence and robustness can be guaranteed. Moreover, a grouping mask scheme is used to accommodate to the complex-valued SAR data. Finally, both simulated and measured SAR data are applied for the validation. Comparisons with the conventions are performed in terms of phase transition analysis, so as to verify the superiority of the proposed algorithm both qualitatively and quantitatively.