Adaptive Detection Research Articles

An evaluation of methods for detecting false data injection attacks in the smart grid

With the introduction of new Information and Communication Technology (ICT) to Electrical Power Systems (EPSs) there is an increased potential and impact of cyber-attacks. Phasor Measurement Units (PMUs) enable very fine granular measurements to allow situational awareness in smart grids. But false data injection attacks, which manipulate measurement data, can trigger wrong decisions and cause critical situations in the grid. In this paper, we analyze four different false data injection attacks on PMU measurements and investigate different methods to detect such attacks. Classical bad data detection methods are not sufficient to detect stealthy attacks. We therefore propose to complement detection by additional methods. For this we analyze the detection performance of four very different detection methods: (a) the classical adaptive bad data detection approach based on the residuals of linear Kalman Filters, (b) a simple threshold based on the Median Average Deviation (MAD), (c) a distribution-based approach using the Kullback-Leibler Divergence (KLD), and (d) the cumulative sum (CUSUM) as a representative of a change point detection method. We show that each method has advantages and disadvantages and that multiple methods should be used together to prevent that attackers can circumvent detection.

DE-RGBD SLAM: Enhancing Static Feature Point Selection in RGB-D Visual SLAM Using Depth Information

Abstract Feature point extraction plays a key role in visual simultaneous localization and mapping (SLAM) systems. And it remains a major challenge to accurately select static feature points in a complex dynamic environment. To address this issue, this paper proposes an RGB-D SLAM method, referred to as DE-RGBD SLAM, which optimizes feature selection by integrating depth information and effectively utilizes depth data and multi-view geometric information to achieve localization and navigation for mobile robots in dynamic environments. Firstly, the method analyzes prominent feature regions in the image based on colour and depth information captured by an RGB-D camera. It sets adaptive FAST corner detection thresholds according to the grayscale information of these regions while masking other areas. Next, the method obtains in-depth information on the detected feature points in the current frame. It combines their pixel coordinates in the image coordinate system to determine the presence of redundant feature points. Notably, the method can detect some dynamic feature points between consecutive frames. Subsequently, in the camera coordinate system, the method compares the depth information of feature points in the depth image with the epipolar depth estimates derived from the essential matrix to determine whether the features are static and eliminate dynamic feature points. This approach significantly enhances the reliability of static feature points. Finally, the accuracy and robustness of the proposed method are validated through experiments conducted on the public TUM dataset and real-world scenarios compared to state-of-the-art visual SLAM systems.

Development of algorithms for early detection of cyberattacks on networks using machine learning

Critical infrastructure protection and national security are enhanced by the security and reliability of networks. Various types of information circulate on these networks, ranging in classification from open to closed. The consequences of cyberattacks on these networks can be severe, including reputational damage, financial loss, operational disruption and data leakage. Traditional security methods, such as firewalls and anti-virus software, are becoming less effective against modern and ever-changing cyber threats. As a result, powerful network intrusion detection systems (IDS) have become indispensable for proactive detection and mitigation of cyber attacks. Machine learning has become a viable method for creating adaptive intrusion detection tools that can detect new and complex attack patterns. By learning from huge labelled network traffic datasets, ML models can understand the subtle patterns and differentiating features of normal and abnormal or malicious traffic flows. This allows them to detect possible cyber threats and intrusions that traditional signature-based IDSs cannot detect. Extracting discriminative features and training appropriate classification models from such data is a challenging task. In the presented study, we analyse the effectiveness of ML algorithms for detecting cyberattacks, in particular distributed denial of service (DDoS) attacks, in network traffic data. In the presented study, a network attack detection system is developed using ML and deep learning (DL) models and experimented on the CICIDS2017 dataset. The main objectives of the study are to develop a strategy for extracting valuable information from raw network streams; to study the impact of data preparation on the false positive rate; and to conduct a comparative analysis of ML models for cyberattack detection. The main goal of the study is to provide an understanding of the development of a reliable adaptive network intrusion detection system using ML approaches that increase cybersecurity capabilities and protect against future cyberattacks.

Adaptive Dynamic Thresholding Method for Fault Detection in Diesel Engine Lubrication Systems

Fault detection in marine diesel engine lubrication systems is crucial for ensuring the long-term stable operation of diesel engines and the safety of maritime navigation. Traditional fixed-parameter alarm threshold methods lack flexibility and are prone to missing faults. Data-driven approaches like machine learning require high-quality data for fault samples. This study leverages the relative advantages of data mining methods and threshold techniques, proposing an adaptive threshold construction method based on dynamic parameter relationship inference. Employing an algorithm for inferring dynamic relationships among multiple parameters of the lubrication system builds an adaptive threshold detection model. Extensive diesel engine tests and actual fault data demonstrate that the proposed method can address the issues of missed faults encountered by static threshold methods and the low detection accuracy of machine learning approaches without the need for fault samples. This significantly enhances fault detection accuracy in marine diesel engine lubrication systems, offering considerable industrial practical value.

Self - adaptive canny edge detection with reinforcement learning and dominant texture color Patterns for melanoma segmentation and classification in dermoscopic images

Self - adaptive canny edge detection with reinforcement learning and dominant texture color Patterns for melanoma segmentation and classification in dermoscopic images

A Robust Malaria Cell Detection Framework Using Adaptive and Atrous Convolution-Based Recurrent Mobilenetv2 with Trans-MobileUNet + + -Based Abnormality Segmentation.

The highly contagious malaria disease is spread by the female Anopheles mosquito. This disease results in a patient's death or incapacity to move their muscles, if it is not appropriately identified in the early stages. A Rapid Diagnostic Test (RDT) is a frequently used approach to find malaria cells in red blood cells. However, it might not be able to identify infections with small amounts of samples. In the microscopic detection model, blood stains are placed under a microscope for diagnosing malaria. But accurate diagnosis is hard in this method, particularly in developing nations where the disease is most common. The microscopic detection processes are expensive and time-consuming due to the usage of microscopes. The quality of the blood smears and the availability of a qualified specialist, who is skilled in recognizing the disease, impact the accuracy of malaria detection results. The traditional deep learning-based malaria identification models need more processing power. Therefore, a deep learning-based adaptive method is designed to detect malaria cells through the medical image. Hence, the images are gathered from the standard sites and then fed to the segmentation process. Here, the abnormality segmentation is carried out with the help of a developed Trans-MobileUNet + + (T-MUnet + +) network. Trans-MobileUNet + + captures global context, so it is well-suited for segmentation tasks. The segmented image is applied to the adaptive detection phase where the Adaptive and Atrous Convolution-based Recurrent MobilenetV2 (AA-CRMV2) model is designed for the effective recognition of malaria cells. The efficiency of the designed approach is elevated by optimizing the parameters from the AA-CRMV2 network with the help of the Updated Random Parameter-based Fennec Fox Optimization (URP-FFO) algorithm. Several experimental analyses are evaluated in the implemented model over classical techniques to display their effectualness rate.

Agent-based dynamic thresholding for adaptive anomaly detection using reinforcement learning

Agent-based dynamic thresholding for adaptive anomaly detection using reinforcement learning

Enhancing intrusion detection in next-generation networks based on a multi-agent game-theoretic framework

<span lang="EN-US">With cyber threats becoming increasingly sophisticated, existing intrusion detection systems (IDS) in next generation networks (NGNs) are subjected to more false-positives and struggles to offer robust security feature, highlighting a critical need for more adaptive and reliable threat detection mechanisms. This research introduces a novel IDS that leverages a dueling deep Q-network (DQN) a reinforcement learning algorithm within game-theoretic framework simulating a multi-agent adversarial learning scenario to address these challenges. By employing a customized OpenAI Gym environment for realistic threat simulation and advanced dueling DQN mechanisms for reduced overestimation bias, the proposed scheme significantly enhances the adaptability and accuracy of intrusion detection. Comparative analysis against current state-of-the-art methods reveals that the proposed system achieves superior performance, with accuracy and F1-score improvements to 95.02% and 94.68%, respectively. These results highlight the potential scope of the proposed adaptive IDS to provide a robust defense against the dynamic threat landscape in NGNs.</span>

Network Intrusion Detection Using Transformer Models and Natural Language Processing for Enhanced Web Application Attack Detection

The increasing frequency and complexity of web application attacks necessitate more advanced detection methods. This research explores integrating Transformer models and Natural Language Processing (NLP) techniques to enhance network intrusion detection systems (NIDS). Traditional NIDS often rely on predefined signatures and rules, limiting their effectiveness against new attacks. By leveraging the Transformer's ability to capture long-term dependencies and the contextual richness of NLP, this study aims to develop a more adaptive and intelligent intrusion detection framework. Utilizing the CSIC 2010 dataset, comprehensive preprocessing steps such as tokenization, stemming, lemmatization, and normalization were applied. Techniques like Word2Vec, BERT, and TF-IDF were used for text representation, followed by the application of the Transformer architecture. Performance evaluation using accuracy, precision, recall, F1 score, and AUC demonstrated the superiority of the Transformer-NLP model over traditional machine learning methods. Statistical validation through Friedman and T-tests confirmed the model's robustness and practical significance. Despite promising results, limitations include the dataset's scope, computational complexity, and the need for further research to generalize the model to other types of network attacks. This study indicates significant improvements in detecting complex web application attacks, reducing false positives, and enhancing overall security, making it a viable solution for addressing increasingly sophisticated cybersecurity threats

A Highly-Scalable Poisson-Coded Retinal Optogenetic Stimulator With Fully-Analog ED-Based Adaptive Spike Detection and Closed-Loop Calibration

A Highly-Scalable Poisson-Coded Retinal Optogenetic Stimulator With Fully-Analog ED-Based Adaptive Spike Detection and Closed-Loop Calibration

AWARDistill: Adaptive and robust 3D object detection in adverse conditions through knowledge distillation

AWARDistill: Adaptive and robust 3D object detection in adverse conditions through knowledge distillation

Robust Domain Adaptive Object Detection With Unified Multi-Granularity Alignment.

Domain adaptive detection aims to improve the generalization of detectors on target domain. To reduce discrepancy in feature distributions between two domains, recent approaches achieve domain adaption through feature alignment in different granularities via adversarial learning. However, they neglect the relationship between multiple granularities and different features in alignment, degrading detection. Addressing this, we introduce a unified multi-granularity alignment (MGA)-based detection framework for domain-invariant feature learning. The key is to encode the dependencies across different granularities including pixel-, instance-, and category-levels simultaneously to align two domains. Specifically, based on pixel-level features, we first develop an omni-scale gated fusion (OSGF) module to aggregate discriminative representations of instances with scale-aware convolutions, leading to robust multi-scale detection. Besides, we introduce multi-granularity discriminators to identify where, either source or target domains, different granularities of samples come from. Note that, MGA not only leverages instance discriminability in different categories but also exploits category consistency between two domains for detection. Furthermore, we present an adaptive exponential moving average (AEMA) strategy that explores model assessments for model update to improve pseudo labels and alleviate local misalignment problem, boosting detection robustness. Extensive experiments on multiple domain adaption scenarios validate the superiority of MGA over other approaches on FCOS and Faster R-CNN detectors.

Phishing detection using clustering and machine learning

<span lang="EN-US">Phishing is a prevalent and evolving cyber threat that continues to exploit human vulnerability to deceive individuals and organizations into revealing sensitive information. Phishing attacks encompass a range of tactics, from deceptive emails and fraudulent websites to social engineering techniques. Traditional methods of detection, such as signature-based approaches and rule-based filtering, have proven to be limited in their effectiveness, as attackers frequently adapt and create new, previously unseen phishing campaigns. Consequently, there is a growing need for more sophisticated and adaptable detection methods. In recent years, Machine Learning (ML) and Artificial Intelligence (AI) have played a significant role in enhancing phishing detection. These technologies leverage large datasets to train models capable of recognizing subtle patterns and anomalies in both email content and website behavior. This research proposes a hybrid algorithm to detect phishing attack based on clustering and classification ML methods (CMLM): Deep Learning (DL) and Decision Tree (DT). Simulation results show that the proposed technique achieves high percentage of accuracy in detecting phishing</span>

Two-Step Adaptive Target Detection Scheme Using Beam-Steering FMCW Radar Sensors

This letter proposes a two-stage adaptive target detection approach using a beam-steered frequency-modulated continuous wave (FMCW) radar sensor. In the first step, coarse estimation was performed using multiple sharped beams, where the presence of a target was determined using only a portion of the sampled beat signals. In the second step, a high-resolution range-Doppler (RD) map was generated using the entire information about the beat signal pertaining to the transmitted beams estimated to contain the target. Angle estimation was accomplished through digital beamforming based on the enhanced RD map. Furthermore, to verify the effectiveness of the proposed algorithm, high-resolution target detection experiments were conducted using a 24-GHz FMCW radar, while complexity reduction was evaluated in terms of the required memory size.

A Directional Enhanced Adaptive Detection Framework for Small Targets

Due to the challenges posed by limited size and features, positional and noise issues, and dataset imbalance and simplicity, small object detection is one of the most challenging tasks in the field of object detection. Consequently, an increasing number of researchers are focusing on this area. In this paper, we propose a Directional Enhanced Adaptive (DEA) detection framework for small targets. This framework effectively combines the detection accuracy advantages of two-stage methods with the detection speed advantages of one-stage methods. Additionally, we introduce a Multi-Scale Object Adaptive Slicing (MASA) module and an improved IoU-based aggregation module that integrate with this framework to enhance detection performance. For better comparison, we use the F1 score as one of the evaluation metrics. The experimental results demonstrate that our DEA framework improves the performance of various backbone detection networks and achieves better comprehensive detection performance than other proposed methods, even though our network has not been trained on the test dataset while others have.

Adaptive polarimetric persymmetric detection for distributed subspace targets in lognormal texture clutter

In this paper, the adaptive polarimetric persymmetric detection for distributed subspace targets under the background of compound Gaussian clutter is investigated, where the compound Gaussian clutter exhibits texture that follows a lognormal distribution. Based on the two-step Generalized Likelihood Ratio Test (2S GLRT), two-step maximum a posteriori Generalized Likelihood Ratio Test (2S MAP GLRT), two-step Rao (2S Rao) test and two-step Wald (2S Wald) test, we have proposed four polarimetric persymmetric detectors. Initially, we model the target echo as a distributed subspace signal, assuming known clutter texture and polarization speckle covariance matrix (PSCM), and derive the corresponding test statistics. Then, the estimation of the lognormal texture is obtained through maximum a posteriori (MAP). Conventionally, a set of secondary data, which share the same PSCM as the cells under test (CUTs), is assumed to participate in the estimation of the PSCM, leveraging its inherent persymmetric property during the estimation process. Finally, the estimated values are substituted into the proposed test statistics to obtain fully adaptive polarimetric persymmetric detectors. Numerical experimental results using simulated data and measured sea clutter data demonstrate that the proposed four adaptive polarimetric persymmetric detectors exhibit a constant false alarm rate (CFAR) characteristic relative to the PSCM and satisfactory detection performance for distributed subspace targets.

Adaptive occlusion object detection algorithm based on OL-IoU

The continuous advancement of autonomous driving technology imposes higher demands on the accuracy of target detection in complex environments, particularly when traffic targets are occluded. Existing algorithms still face significant challenges in detection accuracy and real-time performance under such conditions. To address this issue, this paper proposes an improved YOLOX algorithm based on adaptive deformable convolution, named OCC-YOLOX. This algorithm enhances the feature extraction network’s ability to focus on occluded targets by incorporating a coordinate attention mechanism. Additionally, it introduces the Overlapping IoU (OL-IoU) loss function to optimize the overlap between predicted and ground truth bounding boxes, thereby improving detection accuracy. Furthermore, the adoption of Fast Spatial Pyramid Pooling (Fast SPP) reduces computational complexity while maintaining real-time performance. Experiments on fused public datasets demonstrate that OCC-YOLOX achieves improvements in accuracy, recall, and average precision by 2.76%, 1.25%, and 1.92%, respectively. In addition to testing on the KITTI, CityPersons, and BDD100K datasets, the effectiveness of the OCC-YOLOX algorithm is further validated through comparisons with self-collected occlusion scene data. The experimental results indicate that OCC-YOLOX outperforms existing mainstream detection algorithms, particularly in handling complex occlusion scenarios, significantly enhancing the accuracy and efficiency of object detection. This study provides new insights for addressing the challenges of occluded target detection in intelligent transportation systems.

Detecting Fake News Using Data Analytics: A Systematic Literature Review And Machine Learning Approach

This study systematically reviews advancements in fake news detection techniques, examining methodologies and frameworks across machine learning, natural language processing, social network analysis, and multimodal approaches. By following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, a rigorous and transparent review process was conducted, resulting in the selection and evaluation of 254 research articles. The findings reveal that supervised learning models, such as Support Vector Machines, Decision Trees, and Naïve Bayes, have shown strong performance in text-based fake news classification, particularly when feature selection is optimized. Deep learning models, including CNNs, RNNs, and transformers, have further advanced detection accuracy by capturing complex linguistic patterns, though challenges with computational demands and model interpretability remain. In scenarios with limited labeled data, unsupervised learning models and semi-supervised approaches offer adaptability, with clustering, anomaly detection, and iterative self-labeling proving effective for evolving misinformation. Additionally, cross-disciplinary approaches, integrating insights from psychology, sociology, and network science, enhance detection models by accounting for user behavior, emotional appeal, and social conformity in the spread of fake news. Case studies from collaborative projects underscore the potential of interdisciplinary efforts to develop robust, adaptable detection frameworks. This review concludes that effective fake news detection requires a multifaceted approach, combining technical advancements with social science insights to address the complexity and adaptive nature of misinformation. The study emphasizes the need for continued research in hybrid models and adaptive, real-time detection solutions to strengthen defenses against fake news in diverse digital environments.

Adaptive Random Early Detection Using Deep Reinforcement Learning for Enhanced Congestion Control in Dynamic TCP/IP Networks

AQM is used to meet targets for congestion control and low delay, high throughput in TCP/IP networks. But methods developed for AQM like Random Early Detection (RED) work strictly on the control parameters pre-set on them and thus may not respond very well to changing network conditions. Thus, this paper presents a composite AQM model using DRL based on DQN, and the ability to learn independently regarding the AQM weight parameter of the queue. The fact that DRL is adaptive means the effectiveness of the proposed system is also assured. As it is observed in implementing traditional model-based approach, the stability and performance degrade when tested in different network conditions; however, DRL’s ability to learn independently enables ML to solve network congestion as it happens. Consequently, more stability is achieved, the delay is lesser, more bandwidth is used and the overall packet drop rate is low; the proposed DRL-RED model is ideal for dynamic network environments. The proposed DRL-RED model is compared with the regular RED algorithm for both low density and high-density networks. This proposed model has achieved sustained throughput of up to 49.9 Mbps with 0.949 % reduction on delay and very low PLR of 8.38043%. From the comparative analysis, it is clear that the employment of DRL with RED improves the stock of network throughput, the reduction on packet drop frequency, and the general delay in network situations. There are two main disadvantages of this approach: first, it cannot reach the heavy-traffic, the problem partly solved by model-based approach; second, the values of the congestion-control parameters cannot be reset, an issue eradicated by DRL as a result of its inherent adaptiveness. Consequently, new stability and increased performance can be achieved under various network conditions.

Distributed Passive Positioning and Sorting Method for Multi-Network Frequency-Hopping Time Division Multiple Access Signals.

When there are time division multiple access (TDMA) signals with large bandwidth, waveform aliasing, and fast frequency-hopping in space, current methods have difficulty achieving the accurate localization of radiation sources and signal-sorting from multiple network stations. To solve the above problems, a distributed passive positioning and network stations sorting method for broadband frequency-hopping signals based on two-level parameter estimation and joint clustering is proposed in this paper. Firstly, a two-stage filtering structure is designed to achieve control filtering for each frequency point. After narrowing down the parameter estimation range through adaptive threshold detection, the time difference of arrival (TDOA) and the velocity difference of arrival (VDOA) can be obtained via coherent accumulating based on the cross ambiguity function (CAF). Then, a multi-station positioning method based on the TDOA/VDOA is used to estimate the position of the target. Finally, the distributed joint eigenvectors of the multi-stations are constructed, and the signals belonging to different network stations are effectively classified using the improved K-means method. Numerical simulations indicate that the proposed method has a better positioning and sorting effect in low signal-to-noise (SNR) and low snapshot conditions compared with current methods.