Adaptive Detection Research Articles

Adaptive detection of distributed targets in heterogeneous Gaussian clutter without secondary data

This paper addresses the problem of detecting distributed targets in heterogeneous Gaussian clutter without assuming the presence of secondary data. Specifically, the clutter is modeled as a spherically invariant random process with unknown texture components and covariance matrix structure (CMS). In contrast to existing approaches that are based on the estimate-and-plug techniques, we introduce an approximation of the generalized likelihood ratio test that leverages an alternating estimation procedure to obtain at least a local likelihood maximum. We also prove that the proposed method achieves the constant false alarm rate with respect to clutter parameters when appropriately initialized. Finally, a comprehensive performance analysis is carried out by Monte Carlo simulation and in comparison with the non-scatterer density dependent generalized likelihood ratio test (NSDD-GLRT) in the cases of known and unknown CMS. The results show that the proposed solution is more robust and effective than NSDD-GLRT when the CMS is unknown while exhibiting only a modest performance degradation with respect to the benchmark when the CMS is known.

An Adaptive Radar Target Detection Method Based on Alternate Estimation in Power Heterogeneous Clutter

Multichannel radars generally need to utilize a certain amount of training samples to estimate the covariance matrix of clutter for target detection. Due to factors such as severe terrain fluctuations and complex electromagnetic environments, the training samples usually have different statistical characteristics from the data to be detected. One of the most common scenarios is that all data have the same clutter covariance matrix structure, while different data have different power mismatches, called power heterogeneous characteristics. For detection problems in the power heterogeneous clutter environments, we propose detectors based on alternate estimation, using the generalized likelihood ratio test (GLRT) criterion, Rao criterion, Wald criterion, Gradient criterion, and Durbin criterion. Monte Carlo simulation experiments and real data indicate that the detector based on the Rao criterion has the highest probability of detection (PD). Furthermore, when signal mismatch occurs, the detector based on the GLRT criterion has the best selectivity, while the detector based on the Durbin criterion has the most robust detection performance.

ASOD: an adaptive stream outlier detection method using online strategy

In the current era of information technology, blockchain is widely used in various fields, and the monitoring of the security and status of the blockchain system is of great concern. Online anomaly detection for the real-time stream data plays vital role in monitoring strategy to find abnormal events and status of blockchain system. However, as the high requirements of real-time and online scenario, online anomaly detection faces many problems such as limited training data, distribution drift, and limited update frequency. In this paper, we propose an adaptive stream outlier detection method (ASOD) to overcome the limitations. It first designs a K-nearest neighbor Gaussian mixture model (KNN-GMM) and utilizes online learning strategy. So, it is suitable for online scenarios and does not rely on large training data. The K-nearest neighbor optimization limits the influence of new data locally rather than globally, thus improving the stability. Then, ASOD applies the mechanism of dynamic maintenance of Gaussian components and the strategy of dynamic context control to achieve self-adaptation to the distribution drift. And finally, ASOD adopts a dimensionless distance metric based on Mahalanobis distance and proposes an automatic threshold method to accomplish anomaly detection. In addition, the KNN-GMM provides the life cycle and the anomaly index for continuous tracking and analysis, which facilities the cause analysis and further interpretation and traceability. From the experimental results, it can be seen that ASOD achieves near-optimal F1 and recall on the NAB dataset with an improvement of 6% and 20.3% over the average, compared to baselines with sufficient training data. ASOD has the lowest F1 variance among the five best methods, indicating that it is effective and stable for online anomaly detection on stream data.

Airborne magnetic anomaly detection based on Bi-stable stochastic resonance system

In order to address the problem with low signal-to-noise ratio (SNR) detection for airborne magnetic anomaly detection (MAD), an adaptive detection method based on bi-stable stochastic resonance (SR) system is proposed. The method adopts a series structure, S-SR, effectively improving detection speed. Allowing for the difficulty in parameter selection for the SR system, this method can achieve the adaptive selection of system parameters. On this basis, the effect of different sampling frequencies in the Runge–Kutta method on the numerical model of the SR system is investigated. Finally, the simulation and experiment analysis of MAD methods are performed to draw a comparison between the S-SR, orthogonal basis functions (OBF) decomposition, and high-order crossing (HOC) methods. According to the simulation and experiment results, the S-SR method outperforms the OBF and HOC methods in the outcome and range of detection under the same conditions.

Adaptive detectors for mismatched subspace target in clutter with lognormal texture

Due to factors such as array misalignment and waveform distortion, the target echo may not be precisely located within the nominal subspace in practical applications, resulting in mismatch issues. In response to this challenge, this paper investigates adaptive detection methods for detecting mismatched subspace targets amidst lognormal clutter background. To enhance the suppression of mismatched signal, we introduce a fictitious signal into the null hypothesis, which is situated within a subspace orthogonal to the nominal subspace in the whitened observation space. Following convention, we allow for the existence of a training dataset that shares the same covariance matrix (CM) structure as the main dataset. Subsequently, we propose two adaptive subspace detectors based on a two-step Generalized Likelihood Ratio Test (GLRT) and a two-step maximum a posteriori (MAP) GLRT. Both novel detectors have been validated to have constant false alarm rate (CFAR) properties for speckle CM. Numerical experiments are carried out using simulation data and measured sea clutter data, which demonstrate that our proposed methods exhibit robustness against non-mismatched signal and effective suppression of mismatches.

Intermediate Domain-Based Meta Learning Framework for Adaptive Object Detection

Intermediate Domain-Based Meta Learning Framework for Adaptive Object Detection

Robust speckle covariance matrix estimation of sea clutter based on spectral symmetry

Speckle covariance matrix estimation plays an important role in adaptive target detection of maritime radars. Spatial heterogeneity of sea clutter incurs insufficient secondary data in estimation, which degrades detection performance. It is an effective way of estimation improvement to exploit the Doppler spectrum knowledge of sea clutter for structural dimension reduction of the speckle covariance matrix. This paper proposes a speckle covariance matrix estimation method based on the spectral symmetry knowledge of sea clutter, indicating that the spectrum of sea clutter is symmetric with respect to the Doppler offset. First of all, the spectral symmetry of sea clutter is analyzed by using the measured data from several opened databases and the results show that most of the data excluding sea spikes have symmetric Doppler spectra. Secondly, the spectral symmetry constrains the speckle covariance matrix into the Hadamard product of a special rank-one Toeplitz matrix and a real positive definite symmetric matrix. The degrees of freedom are reduced to almost half of that of a Hermitian matrix and thus fewer secondary data are required. Thirdly, a robust iterative maximum likelihood estimator is proposed to estimate the speckle covariance matrix with structural constraint and is free of the clutter texture distribution. The performance of the estimator is verified by the measured data and the results show that it is superior to traditional estimators, particularly in fewer secondary data.

On Explainable and Adaptable Detection of Distributed Denial-of-Service Traffic

On Explainable and Adaptable Detection of Distributed Denial-of-Service Traffic

Intrusion Detection System to Secure a Network using ACNN Model and Machine Learning

As cyber threats continue to evolve in sophistication and diversity, the need for robust Intrusion Detection Systems (IDS) becomes paramount to safeguarding network integrity. This research explores the application of an innovative approach by integrating an Attention-based Convolutional Neural Network (ACNN) model with machine learning techniques to enhance the accuracy and efficiency of intrusion detection. The proposed system leverages the ACNN's ability to capture contextual dependencies in network traffic data, enabling the extraction of intricate patterns indicative of potential intrusions. The ACNN's attention mechanism focuses on relevant features within the data, improving the model's discriminative power and adaptability to dynamic cyber threats. To achieve optimal performance, the ACNN is complemented with a machine learning framework that includes feature engineering, dimensionality reduction, and classification algorithms. This integrated approach allows the system to adapt and learn from evolving attack vectors, providing a proactive defense mechanism against both known and unknown threats. The research evaluates the proposed ACNN-based IDS using benchmark datasets and real-world network traffic scenarios. Comparative analysis against traditional IDS models showcases the superiority of the ACNN in terms of detection accuracy, false positive rates, and computational efficiency. Furthermore, the system's adaptability to emerging threats is demonstrated through continuous learning and retraining mechanisms. Results indicate that the ACNN-based IDS not only exhibits superior performance but also demonstrates resilience against evasion techniques employed by malicious actors. The research findings contribute to the advancement of network security by presenting a cuttingedge solution that combines deep learning and machine learning for effective and adaptive intrusion detection.

A Comprehensive Review on an Advanced Machine Learning Approach for Enhancing Phishing Website Detection

Abstract: Phishing attacks continue to pose a significant threat to online security, exploiting user trust to steal sensitive information. This paper presents a comprehensive review of advanced machine learning techniques for enhancing phishing website detection. We analyze recent developments in feature engineering, focusing on content-based, URL-based, and networkbased attributes. Various machine learning algorithms, including supervised and unsupervised learning, are examined, highlighting their strengths and limitations in this domain. Furthermore, we investigate the potential of ensemble methods and deep learning models to improve detection accuracy. Additionally, we address challenges such as concept drift and adversarial attacks, discussing potential mitigation strategies. Finally, we outline future research directions, emphasizing the need for adaptable and real-time detection systems to counter evolving phishing tactics. This review serves as a valuable resource for researchers and practitioners seeking to develop more robust and effective phishing website detection solutions.

Adaptive Robust Subspace Detection Based on GLRT, Rao, Wald, Gradient, and Durbin Tests

Adaptive Robust Subspace Detection Based on GLRT, Rao, Wald, Gradient, and Durbin Tests

Confused and disentangled distribution alignment for unsupervised universal adaptive object detection

Universal domain adaptive object detection (UniDAOD) is a more challenging and realistic problem than traditional domain adaptive object detection (DAOD), aiming to transfer the knowledge from the well-labeled source domain to the unlabeled target domain without any prior knowledge of label sets. Intuitively, the main challenge of UniDAOD is to eliminate the domain shift and suppress the interference caused by the category shift induced by private classes (i.e., classes only existed in one domain). In the current study, we propose a simple but effective CODE framework, namely Confused and Disentangled Extraction, for alleviating this issue. Specifically, we propose the virtual adversarial adaptation module, characterized by incorporating virtual domain labels within the domain classifier for unaligned samples. This confuses the domain classifier, effectively addressing the issue of converging to local optima resulting from equilibrium challenges and consequently narrowing the domain shift. Simultaneously, we introduce the entropy margin separation module, which utilizes the distinctiveness of category predictions as a disentangled factor. This enables the automatic discovery of private classes in each domain, suppressing interference during the adaptation process. Experiments on four universal scenarios (i.e., closed-set, partial-set, open-partial-set, and open-set) show that CODE obtains a significant performance gain over original DAOD detectors.

Laser-based drone vision disruption with a real-time tracking system for privacy preservation

The capabilities of drones are increasing every day, as is the ease with which civilians can buy and fly them. Most drones are equipped with a camera that is used by a point-of-view operator and, at the same time, can be used for image capture. The use of drones creates a threat to privacy whereby anyone who can fly a drone can take pictures without permission. This study aims to create a 2-axis tracker system that can recognize a drone and locate the position of the drone camera so that a laser beam can track and dazzle the drone camera. The depth-sensing camera is used to localize the part of the target corresponding to the drone’s camera and is created using the YOLOv5 algorithm as a deep-learning detector model. The drone’s camera range and position are challenging to detect due to its small size. Our adaptive detection method combines drone detection and drone camera detection. The depth-sensing camera provides input in the form of a three-coordinate axis from the target. If only the drone is detected, a predictive algorithm can determine the camera’s position for illumination with the laser. Alternatively, if the drone camera is detected, the laser can follow the target’s movement more quickly. In this study, a green (520 nm) laser module with adjustable power is used to investigate factors that affect the dazzling range. The computer vision detection algorithm can detect and localize the position of the drone camera up to 500 cm with a confidence level of more than 65%. If the target is in the center of the field of view, the accuracy of the target position can reach 98%. The tracker can follow the drone’s movement from 2 m/s to 4 m/s with a maximum error of 1.9 cm from the center point of the drone camera for close range. For long range, the maximum error is 6.2 cm. A laser power of 23.5 mW at 500 cm distance is found to be sufficient to dazzle and track drone cameras.

ARDOD: adaptive radius density-based outlier detection

ARDOD: adaptive radius density-based outlier detection

Adaptive Detection and Classification of Brain Tumour Images Based on Photoacoustic Imaging

A new imaging technique called photoacoustic imaging (PAI) combines the advantages of ultrasound imaging and optical absorption to provide structural and functional details of tissues. It has broad application prospects in the accurate diagnosis and treatment monitoring of brain tumours. However, the existing photoacoustic image classification algorithms cannot effectively distinguish benign tumours from malignant tumours. To address this problem, the YoLov8-MedSAM model is proposed in this research to provide precise and adaptable brain tumour identification and detection segmentation. Additionally, it employs convolutional neural networks (CNNs) to classify and identify tumours in order to distinguish between benign and malignant variations in PAI. The experimental results show that the method proposed in this study not only effectively detects and segments brain tumours of various shapes and sizes but also increases the accuracy of brain tumour classification to 97.02%. The method provides richer and more valuable diagnostic information to the clinic and effectively optimizes the diagnosis and treatment strategy of brain tumours.

A Steering-Vector-Based Matrix Information Geometry Method for Space–Time Adaptive Detection in Heterogeneous Environments

Plagued by heterogeneous clutter, it is a serious challenge for airborne radars to detect low-altitude, weak targets. To overcome this problem, a novel matrix information geometry detector for airborne multi-channel radar is proposed in this paper. The proposed detector applies the given steering vector and array structure information to the matrix information geometry detection method so that it can be used for space–time adaptive detection. While improving the detection performance, the matrix information geometry detector’s original anti-clutter advantage is enhanced as well. The simulation experiment results indicate that the proposed detector has advantages in several of the properties related to space–time adaptive detection, while its computational complexity does not increase significantly. Moreover, experiment results based on the measured data verify the superior performance of the proposed method. Sea-detecting data-sharing-program data, mountaintop data, and phased-array radar data are employed to verify the performance advantage of the proposed method in heterogeneous clutter and the ability for weak target detection.

Intelligent Advanced Attack Detection Technology based on Multi-modal Data Fusion

This paper proposes an adaptive Wedman intrusion detection algorithm (AID-DFS) for data fusion. Firstly, feature extraction of abnormal text detection is carried out using a BI-gated loop (Bi-GRU). Multi-branch convolutional recurrent neural network (CNN-RNN) extracts hierarchical features from abnormal images. The multi-mode dynamic fusion uses the intermodal and intramodal attention mechanisms. In this way, a joint representation of multiple modes is obtained. The visual perception mechanism is used to realize multichannel integration and strengthen the function of original information in multichannel. The experimental results show that the proposed method has 99.6% accuracy and 94.9% accuracy. Compared with other algorithms, the proposed method can improve the performance of the intrusion system by about 10.2%.

Multi-Environment Adaptive Fast Constant False Alarm Detection Algorithm Optimization Strategy

Multi-Environment Adaptive Fast Constant False Alarm Detection Algorithm Optimization Strategy

An adaptive detection approach for multi-scale defects on wind turbine blade surface

Multi-scale defects will inevitably appear in wind turbine blade defect detection within practical detection applications. An adaptive multi-scale detection approach is proposed to accurately classify and locate defects on the wind turbine blade surface. The proposed approach includes two main detection procedures: rough detection and precise detection. In the rough detection, the multi-level features extraction module with an adaptive bounding box proposal module is used to depict multi-scale defect regions and train a binary classifier to distinguish defects from non-defects. At the precise detection stage, three defect categories and four coordinates representing defect locations are obtained based on a multi-class defect classifier and regression of rough location boxes. The proposed method is evaluated on a real wind turbine blade surface defect dataset collected in a commercial wind farm and annotated manually. Results show that (1) the proposed model can detect the class of the blade multi-scale defects and outperforms other schemes with 96.89% mAP in the same model training epochs, (2) the positioning performance analysis of the model for multi-scale defects is conducted to validate the accuracy of the proposed model for multi-scale defect location.

Environmentally adaptive fast object detection in UAV images

Detecting objects in aerial images poses a challenging task due to the presence of numerous small objects and complex environmental information. To address these problems, we propose an efficient detector specifically designed for aerial images, named EAF-YOLOv8, based on YOLOv8-S. In this paper, we introduce a novel backbone network called EAFNet, specifically designed for small object detection. EAFNet consists of the Rapidly Merging Receptive Fields Aggregation Module (RMRFAM) and Multi-Scale Channel Attention (MSCA). The RMRFAM utilizes dilated convolution (DConv) and partial convolution (PConv) to acquire richer receptive fields, capturing more extensive contextual information at higher levels while reducing redundancy in channel information, thereby accelerating inference speed. Furthermore, inspired by denoising tasks, we focus on the feature information surrounding the target background and propose MSCA. MSCA integrates channel attention with an embedded self-attention feature pyramid, extending the feature learning scope to the surrounding environment of the target, beyond the target itself. This approach utilizes enhanced background features to elicit a higher response for small targets, reducing false positives. Experimental results demonstrate that in UAVDT and VisDrone2019, the proposed EAF-YOLOv8 achieves mAP50 scores of 34.3% and 49.7%, respectively. Additionally, EAF-YOLOv8 exhibits high real-time inference speeds of 77.60 FPS and 55.56 FPS, showcasing competitive detection performance.