This paper focuses on centralized and distributed state estimations with measurement outliers and inaccurate noise covariance matrices. To capture the heavy-tailed nature introduced by measurement outliers, the measurement noise is modeled using an elliptical distribution (ED). To guarantee the conjugate, the inaccurate scale matrix of ED and the covariance matrix of the Gaussian process noise are modeled as inverse Wishart distributions, respectively. In the centralized filtering algorithm, enabling robust multi-sensor fusion at a central node, the state and parameters are jointly estimated based on the variational Bayesian (VB) approach. In the distributed case, a distributed VB (DVB) algorithm is proposed in a fully distributed way, which includes the natural gradient ascent, consensus averaging, and VB updates. Simulation results demonstrate that the proposed algorithms substantially outperform existing state-of-the-art filters in terms of robustness and estimation accuracy.

The TLS encryption protocol plays a vital role in securing data transmission, but it also presents challenges for payload-based Network Intrusion Detection Systems (NIDS). Existing methods utilize statistical characteristics of side-channel features, such as the mean packet length, to identify encrypted traffic. However, packet lengths are constrained by the Maximum Segment Size (MSS) of the TCP protocol. This constraint causes the length-varied sequence to be encapsulated into segments of equal length, resulting in information loss. Moreover, flow-level statistical features are vulnerable to interference from noisy packets, making it challenging to detect malicious traffic injected with benign packets effectively. In this paper, we propose an encrypted traffic detection model based on Record length and frEquency Domain Analysis (REDA). First, we reconstruct the TLS Record Length Sequence (TRLS), which is a length-varied sequence, to capture differences in traffic content during transmission. Second, we employ the Discrete Fourier Transform (DFT) to extract frequency domain features of the TRLS, which are resistant to attacker interference. Finally, an improved One-Class Support Vector Machine (OCSVM) algorithm is devised for the unsupervised detection of malicious traffic, enabling the identification of unknown attacks. Experiments show that REDA is superior to other state-of-the-art methods in terms of accuracy by 2.44%.

The data-driven deep learning methods have brought significant progress and potential to intrusion detection. However, there are two thorny problems caused by the characteristics of intrusion data: "multi-type features" and "data imbalance". The former means that forcefully and improperly transforming intrusion features from distinct metric spaces can result in semantic loss and noise. The latter indicates that the intrusion data is imbalanced in quantity and quality due to its complex spatial distribution. We propose a Hybrid Framework for Multi-type and Imbalance Data (HF-Mid) to address the above two problems. Firstly, we divide the intrusion features into equivalent and non-equivalent groups, and then embed them sequentially using Supervised Paragraph Vector-Distributed Memory (SPV-DM), which excels at modeling co-occurrence relationships, and Deep Neural Network (DNN), which is suitable for modeling non-linear relationships, thereby solving the "multitype features" problem. Secondly, we adopt a low-noise collective matrix factorization (CMF) model to fuse the two obtained features for dimensionality reduction. Finally, we employ a multiple classifier to detect intrusion. During the classifier training stage, we design a genetic algorithm-based proportional sampling method to select high-quality samples in each training batch. thus addressing the "data imbalance" problem. The experimental results demonstrate the proposed framework exhibits an overall improvement of 5.9% and 1.5% in terms of accuracy and false positive rate on average, respectively.

Coprime array can augment the array aperture and enhance the interferences suppression capability for array processing. In this paper, we propose a coprime virtual iterative adaptive approach (CVIAA) method to achieve beamforming performance improvement. Existing coprime array adaptive beamformers (CAABs) usually select the maximum virtual uniform linear array to estimate parameters with the non-uniform virtual sensors deleted, which will suffer from performance loss because some information is ignored. The proposed CVIAA method can cope with the non-uniformity of virtual coarray where entire data is preserved. Even though the virtual array signal is a single snapshot, the proposed CVIAA method can precisely estimate the powers of source signals and noise variance in an iterative way. Moreover, the proposed CVIAA estimator outperforms the existing virtual array Capon estimator when the source signals are closely located. Unlike the traditional iterative adaptive approach (IAA), the proposed CVIAA method reduces the computational complexity by dividing the data into several low-dimension subsets and estimates the noise variance iteratively. Finally, we calculate the weight vector of the proposed CAAB from its definition. Computer simulations verify that the proposed CVIAA estimator is superior to existing estimators and the proposed CAAB can handle environmental uncertainties.

Coprime array exhibits many advantages over the uniform linear array (ULA) with the same number of physical sensors in resolution performance and interference suppression capability. In this study, the authors take the advantages of coprime array to improve the robustness of adaptive beamformer. In the coprime virtual ULA (CV‐ULA), they prove that a constructed Toeplitz matrix can be taken as the sample covariance matrix from the perspective of virtual signal characteristics. The CV‐ULA Capon spectrum estimator is modified to obtain the directions and powers of all impinging signals. Since the real directions of all impinging signals are located at different angular sectors, they form independent signal subspace for each impinging signal. They also assign independent steering vector mismatches for different impinging signals to obtain their real steering vectors. The steering vector mismatch of each impinging signal is independently obtained by solving its own convex optimisation problem. They reconstruct the interference‐plus‐noise covariance matrix (INCM) with precise steering vectors and powers of interference signals. The proposed weight vector is computed by combining the desired signal steering vector and the reconstructed INCM. Extensive simulations show that the proposed algorithm provides robustness against many types of model mismatches.