Low False Alarm Rate Research Articles

Early neurological deterioration detection with a transformer convolutional auto-encoder model

This paper proposes an improved transformer convolutional auto-encoder model combined with the exponentially weighted moving average (EWMA) control chart to detect early neurological deterioration (END) of ischemic stroke patients after endovascular therapy in advance. In the proposed method, the transformer convolutional auto-encoder is used to extract crucial features of multivariate clinical monitoring time series data and obtain the reconfiguration loss while EWMA control chart is utilized to monitor the derived reconfiguration loss and identify anomalies. To verify the feasibility and effectiveness of the proposed END detection approach, multivariate clinical monitoring time series data of ischemic stroke patients in the neurocritical care unit from Beijing Tiantan hospital are collected. Meanwhile, the proposed approach is benchmarked with seven state-of-the-art models. The computation results show that the proposed approach achieves the best performance with the lowest false alarm rate and the highest detection rate. Therefore, the proposed END detection model is practical to guide doctors in conducting clinical interventions in advance to prevent deterioration in patients with ischemic stroke.

Synergizing Ensemble Algorithms for Optimal Intrusion Detection in Asymmetric Data: A Comprehensive Analysis with the NSL-KDD Framework

Ensemble learning has emerged as a powerful method for enhancing the precision of intrusion detection systems (IDSs). In our study, we introduce two novel ensemble learning approaches for IDS: one based on a voting mechanism and the other on stacking techniques. These models were rigorously tested using the NSL-KDD dataset, demonstrating substantial accuracy improvements compared to traditional single classifier systems. Single classifiers often face challenges such as sensitivity to anomalies and noise, along with difficulties in adapting to new, unseen data. Ensemble learning effectively mitigates these issues by integrating the outputs of several classifiers, leading to more stable and accurate predictions. Our research findings reveal that our ensemble learning models can achieve up to 99% accuracy on the NSL-KDD dataset, a notable increase from the approximately 90% accuracy rates observed with single classifiers. Moreover, our models have demonstrated an impressively low false alarm rate (FAR) of under 1%. This indicates their exceptional capability in intrusion detection with minimal false positives. The outcomes of our study strongly indicate the potential of ensemble learning in refining the accuracy of IDSs. We are optimistic that our models will significantly bolster network security, and we are committed to furthering research in this promising field.

Multisensor fusion-based digital twin for localized quality prediction in robotic laser-directed energy deposition

Early detection of defects, such as keyhole pores and cracks is crucial in laser-directed energy deposition (L-DED) additive manufacturing (AM) to prevent build failures. However, the complex melt pool behaviour cannot be adequately captured by conventional single-modal process monitoring approaches. This study introduces a multisensor fusion-based digital twin (MFDT) for localized quality prediction in the robotic L-DED process. The data used in multisensor fusion includes features extracted from a coaxial melt pool vision camera, a microphone, and an off-axis short wavelength infrared thermal camera. The key novelty of this work is a spatiotemporal data fusion method that synchronizes multisensor features with the real-time robot motion data to achieve localized quality prediction. Optical microscope (OM) images of the printed part are used to locate defect-free and defective regions (i.e., cracks and keyhole pores), which serve as ground truth labels for training supervised machine learning (ML) models for quality prediction. The trained ML model is then used to generate a virtual quality map that registers quality prediction outcomes within the 3D volume of the printed part, thus eliminating the need of physical inspections by destructive methods. Experiments show that the virtual quality map closely matches the actual quality observed by OM. Compared to traditional single-sensor-based quality prediction, the MFDT has achieved a significantly higher quality prediction accuracy (96%), a higher ROC-AUC score (99%), and a lower false alarm rate (4.4%). As a result, the MFDT is a more reliable method for defect prediction. The proposed MFDT also lays the groundwork for our future development of a self-adaptive hybrid processing strategy that combines machining with AM for defect removal and quality improvement.

Enhanced Network Intrusion Detection System Using Machine Learning

Abstract: In order to forecast anomalies more correctly, a moderately excellent network detection system for intrusions requires a high rate of detection and a relatively low false alarm rate. Because older datasets cannot capture the design of a set of recent attacks, modeling on the basis of these datasets lacks generalizability. We discuss numerous models before concluding with the one that performs best utilizing various types of evaluation measures. Along with modeling, a detailed data analysis on the properties of the set of data itself is performed for a more complete picture employing our comprehension of a correlation variance, and similar aspects. Furthermore, hypothetical considerations for potential network intrusion detection systems are presented, including advice on prospective modeling and dataset production.

Construction and selection of deformation monitoring model for high arch dam using separate modeling technique and composite decision criterion

Deformation prediction is important to ensure the safe and stable operation of arch dams. Statistical models are extensively applied in arch dam deformation monitoring models, which generally include hydrostatic pressure component, temperature component, and aging (irrecoverable) component. In traditional statistical models, aging component is misset, which will cause unreasonable mutual compensation of each component, resulting in overfitting of the overall model. In this paper, the deformation model based on separate modeling technology is, therefore, proposed mitigating the overfitting problem caused by misspecification of the expression of the aging component in traditional statistical models. Dam deformation components related to different effects are extracted from the deformation monitoring sequence with improved complete ensemble empirical mode decomposition with adaptive noise algorithm and equal water level condition. The correct components of the monitoring model are constructed separately. On the one hand, the fitting accuracy of the model is reflected by the coefficient of determination (R2); on the other hand, the overfitting degree of the model is quantitatively evaluated by the overfitting coefficient (OC), so that the model with high fitting accuracy and prediction accuracy is determined, that is, the optimal model is selected by using the R2-OC criterion. In this paper, displacement monitoring data from measurement points are used for analysis. The results show that the deformation monitoring model based on the separated modeling technique exhibits higher prediction accuracy and lower false alarm rate. The R2-OC criterion better reflects the degree of overfitting of the monitoring model and the real situation of arch dam monitoring and warning, which improves the accuracy of model selection.

Application of Deep Neural Network with Frequency Domain Filtering in the Field of Intrusion Detection

In the field of intrusion detection, existing deep learning algorithms have limited capability to effectively represent network data features, making it challenging to model the complex mapping relationship between network data and attack behavior. This limitation, in turn, impacts the detection accuracy of intrusion detection systems. To address this issue and further enhance detection accuracy, this paper proposes an algorithm called the Fourier Neural Network (FNN). The core of FNN consists of a Deep Fourier Neural Network Block (DFNNB), which is composed of a Hadamard Neural Network (HNN) and a Fourier Neural Network Layer (FNNL). In a DFNNB, the HNN is responsible for sampling the network intrusion data samples in different time domain spaces. The FNNL, on the other hand, performs a Fourier transform on the samples outputted by the HNN and maps them to the frequency domain space, followed by a filtering process. Finally, the data processed by filtering are transformed back to the time domain space for subsequent feature extraction work by the DFNNB. Additionally, to enhance the algorithm’s detection accuracy and filter out noise signals, this paper also introduces a High-energy Filtering Process (HFP), which eliminates noise signals from the data signal and reduces interference on the final detection result. Due to the ability of FNN to process network data in both the time domain space and the frequency domain space, it possesses a stronger capability in expressing data features. Finally, this paper conducts performance evaluations on the KDD Cup99, NSL-KDD, UNSW-NB15, and CICIDS2017 datasets. The results demonstrate that the proposed FNN-based IDS model achieves higher detection rates, lower false alarm rates, and better detection performance than classical deep learning and machine learning methods.

Infrared Moving Small Target Detection Based on Space–Time Combination in Complex Scenes

In the infrared small target images with complex backgrounds, there exist various interferences that share similar characteristics with the target (such as building edges). The accurate detection of small targets is crucial in applications involving infrared search and tracking. However, traditional detection methods based on small target feature detection in a single frame image may result in higher error rates due to insufficient features. Therefore, in this paper, we propose an infrared moving object detection method that integrates spatio-temporal information. To address the limitations of single-frame detection, we introduce a temporal sequence of images to suppress false alarms caused by single-frame detection through analyzing motion features within the sequence. Firstly, based on spatial feature detection, we propose a multi-scale layered contrast feature (MLCF) filtering for preliminary target extraction. Secondly, we utilize the spatio-temporal context (STC) as a feature to track the image sequence point by point, obtaining global motion features. Statistical characteristics are calculated to obtain motion vector data that correspond to abnormal motion, enabling the accurate localization of moving targets. Finally, by combining spatial and temporal features, we determine the precise positions of the targets. The effectiveness of our method is evaluated using a real infrared dataset. Through analysis of the experimental results, our approach demonstrates stronger background suppression capabilities and lower false alarm rates compared to other existing methods. Moreover, our detection rate is similar or even superior to these algorithms, providing further evidence of the efficacy of our algorithm.

Detection of false data injection attacks on power systems based on measurement-eigenvalue residual similarity test

Existing False data injection attack (FDIA) detection methods based on measurement similarity testing have difficulty in distinguishing between actual power grid accidents and FDIAs. Therefore, this paper proposes a detection method called the measurement-eigenvalue residual similarity (MERS) test, which can accurately detect FDIAs in AC state estimationof power system and effectively distinguish them from actual power grid accidents. Simulation results on the IEEE 39-bus system demonstrate that the proposed method achieves higher detection rates and lower false alarm rates than traditional methods under various operation conditions.

Infrared Ship Target Detection Based on Dual Channel Segmentation Combined with Multiple Features

In infrared images of the sea surface, apart from the complex background of the sea surface, there are often sky and island backgrounds. The disturbances caused by sea wind and the reflection of intense sunlight on the sea surface increase the complexity of the background, which seriously hinders the detection of targets. To achieve the detection of dark-polarity ship targets in such environments, a dual-channel threshold segmentation method based on local low-gray region detection and geometric features judgment is proposed in this paper. In one channel, adaptive threshold segmentation is performed on the low-gray regions of the acquired image and combined with geometric features to obtain a finer segmentation result. In the other channel, adaptive segmentation is performed on the preprocessed image, and potential backgrounds that may be finely segmented as targets are filtered out based on an area threshold. Finally, the results of the two channels are multiplied and fused to obtain an accurate segmentation result. Experimental results demonstrate that the proposed algorithm outperforms the comparison algorithm in subjective and objective evaluations. The proposed algorithm in this paper not only achieves a low false alarm rate but also exhibits a higher detection rate, and the average detection rate in the test sequence surpasses 95%.

A deep learning technique to detect distributed denial of service attacks in software-defined networks

Software-Defined Network (SDN) is an established networking paradigm that separates the control plane from the data plane. It has central network control, and programmability facilities, therefore SDN can improve network flexibility, management, performance, and scalability. The programmability and control centralization of SDN have improved network functions but also exposed it to security challenges such as Distributed Denial of Service (DDoS) attacks that target both control and data planes. This paper proposes an effective detection technique against DDoS attack in SDN control plane and data plane. For the control plane, the technique detects DDoS attacks through a Deep Learning (DL) model using new features extracted from traffic statistics. A DL method (AE-BGRU) for DDoS detection uses Autoencoder (AE) with Bidirectional Gated Recurrent Unit (BGRU). The proposed features for the control plane include unknown IP destination address, packets inter-arrival time, Transport layer protocol (TLP) header, and Type of service (ToS) header. For the data plane, the technique tracks the switch's average arrival bit rate with an unknown destination address in the data plane. Then, the technique detects DDoS attacks through a DL-based model which also uses AE with BGRU. The proposed features in the data plane include the switch's stored capacity, the average rate of packets with unknown destination addresses, the IP Options header, and the average number of flows. The dataset is generated from feature extraction and computations from normal and attack packets and used with the classifier. Also, additional Machine Learning (ML) methods are used to enhance the detection process. If the model detects an attack, the technique mitigates DDoS effects by updating the user's trust value and blocking suspicious senders based on the trust value. The experimental results proved that compared to related techniques, the suggested method had a higher accuracy and lower false alarm rate.

Early Fire Detection Using Long Short-Term Memory-Based Instance Segmentation and Internet of Things for Disaster Management.

Fire outbreaks continue to cause damage despite the improvements in fire-detection tools and algorithms. As the human population and global warming continue to rise, fires have emerged as a significant worldwide issue. These factors may contribute to the greenhouse effect and climatic changes, among other detrimental consequences. It is still challenging to implement a well-performing and optimized approach, which is sufficiently accurate, and has tractable complexity and a low false alarm rate. A small fire and the identification of a fire from a long distance are also challenges in previously proposed techniques. In this study, we propose a novel hybrid model, called IS-CNN-LSTM, based on convolutional neural networks (CNN) to detect and analyze fire intensity. A total of 21 convolutional layers, 24 rectified linear unit (ReLU) layers, 6 pooling layers, 3 fully connected layers, 2 dropout layers, and a softmax layer are included in the proposed 57-layer CNN model. Our proposed model performs instance segmentation to distinguish between fire and non-fire events. To reduce the intricacy of the proposed model, we also propose a key-frame extraction algorithm. The proposed model uses Internet of Things (IoT) devices to alert the relevant person by calculating the severity of the fire. Our proposed model is tested on a publicly available dataset having fire and normal videos. The achievement of 95.25% classification accuracy, 0.09% false positive rate (FPR), 0.65% false negative rate (FNR), and a prediction time of 0.08 s validates the proposed system.

Semi-supervised, Neural Network based approaches to face mask and anomaly detection in surveillance networks

In the post-pandemic world, surveillance cameras play a key aspect when it comes to detecting various kinds of security risks. These can range from burglars entering a premises to an individual wearing or not wearing a mask where convention dictates one way versus the other. We are proposing a system that would allow the autonomous detection of these security risks with minimal human intervention. We propose using Multi-task Cascaded Convolutional Neural Networks as the face detector, followed by a Gabor image feature extractor, and the Kernel-based Online Anomaly Detection algorithm for detecting and identifying potential risks in real-time. We have tested our proposed framework on five different datasets comprising two datasets from public online repositories, one publicly available dataset from the Georgia Institute of Technology, and two from real-life settings. We compared our results to the Viola Jones legacy face detector, and with two other anomaly detection algorithms based on Principal Component Analysis (PCA) and the Autoencoder scheme. Our proposed framework has yielded high detection rates with low false alarm rates, in addition to being adaptive, portable, and requiring minimal infrastructure.

Collaborative Federated Learning-Based Model for Alert Correlation and Attack Scenario Recognition

Planned and targeted attacks, such as the advanced persistent threat (APT), are highly sophisticated forms of attack. They involve numerous steps and are intended to remain within a system for an extended length of period before progressing to the next stage of action. Anticipating the next behaviors of attackers is a challenging and crucial task due to the stealthy nature of advanced attack scenarios, in addition to the possible high volumes of false positive alerts generated by different security tools such as intrusion detection systems (IDSs). Intelligent models that are capable of establishing a correlation individual between individual security alerts in order to reconstruct attack scenarios and to extract a holistic view of intrusion activities are required to exploit hidden links between different attack stages. Federated learning models performed in distributed settings have achieved successful and reliable implementations. Alerts from distributed security devices can be utilized in a collaborative manner based on several learning models to construct a federated model. Therefore, we propose an intelligent detection system that employs federated learning models to identify advanced attack scenarios such as APT. Features extracted from alerts are preprocessed and engineered to produce a model with high accuracy and fewer false positives. We conducted training on four machine learning models in a centralized learning; these models are XGBoost, Random Forest, CatBoost, and an ensemble learning model. To maintain privacy and ensure the integrity of the global model, the proposed model has been implemented using conventional neural network federated learning (CNN_FL) across several clients during the process of updating weights. The experimental findings indicate that ensemble learning achieved the highest accuracy of 88.15% in the context of centralized learning. CNN_FL has demonstrated an accuracy of 90.18% in detecting various attacks of APTs while maintaining a low false alarm rate.

TFF-CNN: Distributed optical fiber sensing intrusion detection framework based on two-dimensional multi-features

Distributed fiber optic vibration sensing system has been widely used in safety monitoring with distinct advantages, and the feature extraction and classification methods of fiber optic signals directly determine the real-time performance and reliability of the monitoring system. The existing research feature extraction methods are unidimensional and time consuming, which cannot balance the goals of high accuracy and low time consumption for safety monitoring systems. In this paper, we propose an efficient recognition framework for fusing signal time–frequency features, called TFF-CNN, based on Gramian Angular Difference Fields(GADF) and FFT co-generation matrix(FFTT) to manually extract two-dimensional time–frequency image features of fiber optic signals, taking the significant advantages of CNN in image processing and combining a two-channel model and a fusion module that simulates human decision-making behavior. The accuracy of TFF-CNN is 99.30 % and the detection response time is only 0.6 s. Compared with other methods in this field, TFF-CNN has the advantages of low false alarm rate and short time consumption, which is more suitable for deployment in security monitoring field with distributed fiber optic sensing system.Index Terms: Distributed optical fiber vibration sensing system(DVS), Two-dimensional multi-feature, CNN, intrusion detection, real-time monitoring.

Detecting Abrupt Change in Channel Covariance Matrix for MIMO Communication

The acquisition of the channel covariance matrix is of paramount importance to many strategies in multiple-input-multiple-output (MIMO) communications, such as the minimum mean-square error (MMSE) channel estimation. Therefore, plenty of efficient channel covariance matrix estimation schemes have been proposed in the literature. However, an abrupt change in the channel covariance matrix may happen occasionally in practice due to the change in the scattering environment and the user location. Our paper aims to adopt the classic change detection theory to detect the change in the channel covariance matrix as accurately and quickly as possible such that the new covariance matrix can be re-estimated in time. Specifically, this paper first considers the technique of on-line change detection (also known as quickest/sequential change detection), where we need to detect whether a change in the channel covariance matrix occurs at each channel coherence time interval. Next, because the complexity of detecting the change in a high-dimension covariance matrix at each coherence time interval is too high, we devise a low-complexity off-line strategy in massive MIMO systems, where change detection is merely performed at the last channel coherence time interval of a given time period. Numerical results show that our proposed on-line and off-line schemes can detect the channel covariance change with a small delay and a low false alarm rate. Therefore, our paper theoretically and numerically verifies the feasibility of detecting the channel covariance change accurately and quickly in practice.

Design of intrusion detection system for intelligent mobile network teaching

Because of the openness and complexity of the mobile network environment, the problem of network security is becoming more and more serious, and the mobile network teaching system is also facing the risk of intrusion and attack. Therefore, this paper designs an intrusion detection system for mobile network teaching system. The system uses machine learning and data mining technology to analyze and process the data flow of the mobile network teaching system, extract the characteristics of the data flow, build the intrusion detection model, and realize the intrusion detection of the mobile network teaching system. The research results of this paper show that the intrusion detection system of mobile network teaching system based on intelligent algorithm can effectively detect the intrusion behavior of mobile network teaching system, with high detection accuracy and low false alarm rate, which provides a guarantee for the security and stability of mobile network teaching system.

FOG-Empowered Augmented-Intelligence-Based Proactive Defensive Mechanism for IoT-Enabled Smart Industries

The recent innovations in the network communication domain have entirely revolutionized the conventional industrial sector by introducing a new era of automatic communication. The Industrial Internet of Things (IIoT) is acknowledged as an exclusive space where IoT seems to divulge expressive manifestations. However, the expanded connectivity, more openness, and the widespread use of low-power communication devices of IIoT makes them vulnerable to malicious attacks, and criminal activities. Moreover, the heterogeneous and prevalent nature of the IIoT devices makes it very difficult to come up with a centralized threat detection mechanism, thus its security remains a major concern. Motivated by this, we propose a fog-empowered Augmented Intelligence (IA)-based defensive mechanism to ensure secure communication in IoT-enabled smart industries. The designed framework incorporates the aggregated potentials of two highly acclaimed Deep Learning (DL) classifiers: Gated Recurrent Unit (GRU) and Bidirectional Long Short-Term Memory (BiLSTM). Where the DL-based scheme detects anomalies in such industrial networks and the FOG-based scenario promotes the routing flexibility and interoperability of the heterogeneous devices of the IoT-enabled smart industrial network. The validity of the proposed mechanism is analytically investigated by conducting a comparison with the benchmark threat detection techniques. The proposed solution is also generically examined parallel to some state-of-the-art approaches. The Cu-GRU-BiLSTM framework achieved up to 99.91% accuracy with a low false alarm rate and outperforms the baseline and recent threat detection approaches. The simulation and comparison results validate the effectiveness of the proposed mechanism and advocate it as a phenomenal choice to ensure efficacious and secure communication in IoT-based smart industries.

Investigation of landslide dam life span using prediction models based on multiple machine learning algorithms

A rapid and accurate prediction of a landslide dam’s life span is of significant importance for emergency geological treatment. However, current prediction models for the state of a landslide dam are based solely on geomorphological indexes, and do not take into consideration attribute properties such as landslide types, trigger factors, and dam types. This study investigates the relationships between a landslide dam’s geometry and the capacity of the barrier lake and proposes fitting models, which supplement the current landslide dam database. Subsequently, six predictive models for landslide dam life span are established, utilizing machine learning algorithms such as logistic regression, k-nearest neighbors, support vector machine, Naïve Bayes, decision tree, and random forest, which consider five factors, including geometry parameters and attribute properties. The performances of these six models are analyzed and compared to a typical prediction model, the dimensionless blockage index (DBI). The results suggest that the models established in this study not only have a consistent absolute accuracy as the DBI model, but also overcome the disadvantage that a large number of cases cannot be judged by the DBI model. Among the formulated machine learning models, the random forest model exhibits the highest absolute accuracy (89%), lowest error rate (7%), lowest false alarm rate (15%), and no uncertainty rate. Additionally, three renowned landslide dams, namely the Costantino, Hsiaolin, and Baige landslide dams, are analyzed to illustrate the applicability of the established machine learning models. The study results provide essential guidance for the predictions and emergency geological treatments of landslide dam disasters.

A model for multi-attack classification to improve intrusion detection performance using deep learning approaches

This proposed model introduces novel deep learning methodologies. The objective here is to create a reliable intrusion detection mechanism to help identify malicious attacks. Deep learning based solution framework is developed consisting of three approaches. The first approach is Long-Short Term Memory Recurrent Neural Network (LSTM-RNN) with seven optimizer functions such as adamax, SGD, adagrad, adam, RMSprop, nadam and adadelta. The model is evaluated on NSL-KDD dataset and classified multi attack classification. The model has outperformed with adamax optimizer in terms of accuracy, detection rate and low false alarm rate. The results of LSTM-RNN with adamax optimizer is compared with existing shallow machine and deep learning models in terms of accuracy, detection rate and low false alarm rate. The multi model methodology consisting of Recurrent Neural Network (RNN), Long-Short Term Memory Recurrent Neural Network (LSTM-RNN), and Deep Neural Network (DNN). The multi models are evaluated on bench mark datasets such as KDD’99, NSL-KDD, and UNSWNB15 datasets. The models self-learnt the features and classifies the attack classes as multi-attack classification. The models RNN, and LSTM-RNN provide considerable performance compared to other existing methods on KDD’99 and NSL-KDD dataset.

Sonar echo detection based on two-dimensional matched filtering and convolutional neural network

In underwater acoustics, the performance of sonar echo detection is limited when the echo-to-noise ratio or echo-to-reverberation ratio is low. As an attempt to improve the echo detection rate and maintain a low false alarm rate, a method based on two-dimensional matched filtering (2-D-MF) and convolutional neural network (CNN) is proposed. The 2-D-MF divides the replica signal into multiple sub-replicas, each with different frequency components, and utilizes the sub-replicas to perform the matched filtering individually, obtaining 2-D-MF features that better represent the amplitude-frequency characteristics of the echo signal. The CNN is utilized as an echo detector to extract echo information from the 2-D-MF features and determine the presence of an echo. The proposed method is tested using data collected in the South China Sea, 2021. During the experiment, a transducer transmitted linear frequency modulation (LFM) signals, and a transponder, acting as an analog target, forwarded the LFM signals as echoes. The detection results demonstrate that this method can improve the echo detection rate by approximately 7% while maintaining a constant false alarm rate of 1‰.