Efficient Real-time Detection Research Articles

Detecting Brute Force Attacks Using Machine Learning

The importance of identifying network traffic abnormalities in cybersecurity cannot be emphasized enough, particularly considering the growing frequency and complexity of computer network assaults. With the emergence of new Internet-related technology, there is a corresponding increase in complex assaults. A significant difficulty is dictionary-based brute-force assaults (BFA), which need efficient real-time detection and mitigation techniques. This study explores the detection of SSH and FTP brute-force attacks via the use of the primary objective of our study is to use the machine learning methodology for the identification and detection of SSH and FTP brute-force assaults. Employing many classifiers enables a pretty comprehensive examination of the effectiveness of machine learners in spotting brute force attacks on SSH and FTP. Brute-force attacks are a widely used and perilous technique for acquiring usernames and passwords. Utilizing ethical hacking is a commendable method to assess the impact of a brute-force attack. This article explores several defense tactics and methodologies for using brute-force attacks. The advantages and disadvantages of many defense techniques are listed, along with details on the kind of attack that is most straightforward to recognize. we made use of machine learning (ML) classifiers: Naive Bayes (NB), decision Tree (DT), random forest (RF) Logistic Regression (LG), Quadratic Discriminant Analysis (QDA), Stochastic Gradient Descent (SGD), Linear Discriminant Analysis (LDA), Multi-Layer Perceptron (MLP), we determined that the Random Forest (RF) algorithm achieved the highest level with an accuracy of 99.9%.

LDA-ID: An LDA-based framework for real-time network intrusion detection

Network intrusion poses a severe threat to the Internet. However, existing intrusion detection models cannot effectively distinguish different intrusions with high-degree feature overlap. In addition, efficient real-time detection is an urgent problem. To address the two above problems, we propose a Latent Dirichlet Allocation topic model-based framework for real-time network Intrusion Detection (LDA-ID), consisting of static and online LDA-ID. The problem of feature overlap is transformed into static LDA-ID topic number optimization and topic selection. Thus, the detection is based on the latent topic features. To achieve efficient real-time detection, we design an online computing mode for static LDA-ID, in which a parameter iteration method based on momentum is proposed to balance the contribution of prior knowledge and new information. Furthermore, we design two matching mechanisms to accommodate the static and online LDA-ID, respectively. Experimental results on the public NSL-KDD and UNSW-NB15 datasets show that our framework gets higher accuracy than the others.

Forest Fire Object Detection Analysis Based on Knowledge Distillation

This paper investigates the application of the YOLOv7 object detection model combined with knowledge distillation techniques in forest fire detection. As an advanced object detection model, YOLOv7 boasts efficient real-time detection capabilities. However, its performance may be constrained in resource-limited environments. To address this challenge, this research proposes a novel approach: considering that deep neural networks undergo multi-layer mapping from the input to the output space, we define the knowledge propagation between layers by evaluating the dot product of features extracted from two different layers. To this end, we utilize the Flow of Solution Procedure (FSP) matrix based on the Gram matrix and redesign the distillation loss using the Pearson correlation coefficient, presenting a new knowledge distillation method termed ILKDG (Intermediate Layer Knowledge Distillation with Gram Matrix-based Feature Flow). Compared with the classical knowledge distillation algorithm, KD, ILKDG achieved a significant performance improvement on a self-created forest fire detection dataset. Specifically, without altering the student network’s parameters or network layers, mAP@0.5 improved by 2.9%, and mAP@0.5:0.95 increased by 2.7%. These results indicate that the proposed ILKDG method effectively enhances the accuracy and performance of forest fire detection without introducing additional parameters. The ILKDG method, based on the Gram matrix and Pearson correlation coefficient, presents a novel knowledge distillation approach, providing a fresh avenue for future research. Researchers can further optimize and refine this method to achieve superior results in fire detection.

Efficient real-time detection of electrical equipment images using a lightweight detector model

Infrared technology holds significant importance in the detection of electrical equipment, as it has the capability to swiftly and securely identify electrical apparatus. To simplify the implementation of proficient detection frameworks for electrical equipment within constrained settings (like embedded apparatus), this study presents an enhanced, lightweight model of the single-shot multibox detector (SSD). This model specifically addresses the detection of multiple equipment objects within infrared imagery. The model realized the lightweight of the model by using the network structure characteristics of squeezenet to modify the backbone network of SSD, and compensated for the impact of the lightweight model on the detection accuracy by adding multiple convolutional layers and connecting branches to enhance the propagation ability and extraction ability of features. To ensure a comprehensive evaluation of the model’s detection capabilities, all the models discussed in this study employed the technique of random weight initialization. This approach was utilized to validate the optimal structure of the model and its performance. The experimentation was conducted on both the PASCAL VOC 2007 benchmark dataset and an infrared image dataset encompassing five distinct categories of electrical equipment found within substations. The experimental outcomes indicate that this model offers an efficient approach for achieving lightweight, real-time detection of electrical apparatus.

A Real-Time Gait Phase Detection Method Based on BiLSTM-Attention Model.

Real-time gait phase detection is essential to achieve accurate and stable walking assistance in intelligent rehabilitation training for patients with motor disorders. This study proposed an efficient real-time detection method to detect three gait phases (loading response, stance, and swing) based on a bidirectional long short-term memory network with an attention layer (BiLSTM-Attention). We validated our method on a public dataset where eight healthy subjects' data during treadmill walking were employed. A single inertial measurement unit (IMU) was attached to the shank to measure the sagittal plane acceleration of the lower leg and the angular velocity around the central lateral axis. These data were transposed and segmented into data sequences based on labels using a sliding window method. The data from 8 participants were divided into the training, validation, and test sets (5:1:2). Results showed the average recognition accuracy of the proposed model on new subjects was 97.40% with an average time delay of 15.7±10.1ms, showing the method's potential to be applied for practice use.

RSIn-Dataset: An UAV-Based Insulator Detection Aerial Images Dataset and Benchmark

Power line inspection is an important part of the smart grid. Efficient real-time detection of power devices on the power line is a challenging problem for power line inspection. In recent years, deep learning methods have achieved remarkable results in image classification and object detection. However, in the power line inspection based on computer vision, datasets have a significant impact on deep learning. The lack of public high-quality power scene data hinders the application of deep learning. To address this problem, we built a dataset for power line inspection scenes, named RSIn-Dataset. RSIn-Dataset contains 4 categories and 1887 images, with abundant backgrounds. Then, we used mainstream object detection methods to build a benchmark, providing reference for insulator detection. In addition, to address the problem of detection inefficiency caused by large model parameters, an improved YoloV4 is proposed, named YoloV4++. It uses a lightweight network, i.e., MobileNetv1, as the backbone, and employs the depthwise separable convolution to replace the standard convolution. Meanwhile, the focal loss is implemented in the loss function to solve the impact of sample imbalance. The experimental results show the effectiveness of YoloV4++. The mAP and FPS can reach 94.24% and 53.82 FPS, respectively.

A Lightweight YOLOv5 Optimization of Coordinate Attention

As Machine Learning technologies evolve, there is a desire to add vision capabilities to all devices within the IoT in order to enable a wider range of artificial intelligence. However, for most mobile devices, their computing power and storage space are affected by factors such as cost and the tight supply of relevant chips, making it impossible to effectively deploy complex network models to small processors with limited resources and to perform efficient real-time detection. In this paper, YOLOv5 is studied to achieve the goal of lightweight devices by reducing the number of original network channels. Then detection accuracy is guaranteed by adding a detection head and CA attention mechanism. The YOLOv5-RC model proposed in this paper is 30% smaller and lighter than YOLOv5s, but still maintains good detection accuracy. YOLOv5-RC network models can achieve a good balance between detection accuracy and detection speed, with potential for its widespread use in industry.

A real-time object detection model for orchard pests based on improved YOLOv4 algorithm

Accurate and efficient real-time detection of orchard pests was essential and could improve the economic benefits of the fruit industry. The orchard pest dataset, PestImgData, was built through a series of methods such as web crawler, specimen image collection and data augmentation. PestImgData was composed of two parts, PestImgData-1 and PestImgData-2. It contained 24,796 color images and covered 7 types of orchard pests. Based on the PestImgData and YOLOv4 algorithm, this paper conducted a preliminary study on the real-time object detection of orchard pests from 4 perspectives: transfer learning, activation function, anchor box, and batch normalization. In addition, this paper also visualized the feature learning ability of the detection models. On the basis of the above research, three improvement measures were adopted: the post-processing NMS algorithm was upgraded to DIoU-NMS, the training method was upgraded to 2-time finetuning training and the training data was enhanced. The performance of the improved model, F-D-YOLOv4-PEST, had been effectively improved. The mean average precision of F-D-YOLOv4-PEST was 92.86%, and the detection time of a single picture was 12.22 ms, which could meet the real-time detection requirements. In addition, in the case of high overlap area or high density, F-D-YOLOv4-PEST still maintained good performance. In the testing process of the laboratory and the greenhouse, including the wired network and the wireless network, F-D-YOLOv4-PEST could locate and classify pests as expected. This research could provide technical reference for the intelligent identification of agricultural pests based on deep learning.

Target Electromagnetic Detection Method in Underground Environment: A Review

Underground target detection plays an important role in maintaining underground space security and relies on efficient real-time detection methods. Electromagnetic detection methods including ground penetrating radar and electromagnetic induction have been implemented in detecting underground targets and have been shown to have bright prospects for the future. This paper presents a comprehensive review of current electromagnetic detection methods and recent advances in underground target detection. There are two main problems in electromagnetic underground target detection, namely inversion and classification. We summarize solutions to these problems based on electromagnetic methods as well as deep learning. In addition, this paper proposes a technical framework based on data acquisition and data processing to describe the application of electromagnetic methods. Finally, this paper concludes that although various methods have been proposed, there still exist several future research hotspots in the fields of soil water content, tree root system research, and underground unexploded ordnance. This paper can be a reference for researchers in underground target detection and related fields. Therefore, the research of underground target electromagnetic detection method is expected to increase significantly.

Detecting Epileptic Seizures via Non-Uniform Multivariate Embedding of EEG Signals.

Efficient real-time detection of epileptic seizures remains a challenging task in clinical practice. In this study, we introduce a new thresholding method to monitor brain activities via a non-uniform multivariate (NUM) embedding of multi-channel electroencephalogram (EEG) signals. Specifically, we present a NUM embedding optimization problem to identify the best embedding parameters. We originate one feature, named non-uniform multivariate multiscale entropy (NUMME), which is extracted from the NUM embedded EEG data. Finally, the extracted feature, compared to an individualized threshold, is used for monitoring and detecting seizure onsets. Experimental results on the real CHB-MIT Scalp EEG database show that our approach achieves a comparable performance to the state-of-art methods. Moreover, it is important to note that we accomplish this without using any sophisticated machine learning algorithms.

Real-time fluorescence sensing of single photoactive proteins using silver nanowires

We demonstrate that single functionalized silver nanowires form a geometric platform suitable for efficient real-time detection of single photoactive proteins. By collecting series of images using wide-field fluorescence microscopy, events of single protein attachment can be distinguished with the signal to noise ratio further improved by fluorescence enhancement due to plasmon excitations in the nanowires. The enhancement is evidenced by strong shortening of the fluorescence decay of single photoactive proteins conjugated to the silver nanowires.

MobileNet‐SSD MicroScope using adaptive error correction algorithm: real‐time detection of license plates on mobile devices

At present, many deep learning methods have been applied widely in the field of vehicle and license plate detection. These methods are quite effective in detecting large objects (like vehicles, pedestrians etc.). However, detection for small objects, such as license plates, is not ideal, especially in complex scenes such as license plate occlusion, so that it is difficult to meet the real-time requirements of the industry. To realise efficient real-time detection of small license plates on mobile devices, this study proposes a lightweight model for small object detection, named MobileNet-SSD MicroScope (MSSD MS). This model improves the accuracy of license plate detection, enhances the anti-interference capability and can be implemented in real time on the mobile device RK3399. Besides, for false detection objects, an adaptive error correction algorithm is proposed to reduce the false detection rate, which improves the precision rate of license plate detection and is adaptive to various scenes. The experimental results show that compared with MobileNet-SSD, MSSD MS using the adaptive error correction algorithm has stronger robustness, which can significantly improve the effect of small license plate detection and meet the requirements of real-time detection on mobile devices.

Detecting anomalies in time series data via a deep learning algorithm combining wavelets, neural networks and Hilbert transform

Design of a transferable time series anomaly detection method.Novel deep neural network structure facilitates learning short and long-term pattern interdependencies.Detection of anomalies in the Seismic Electrical Signal for predicting earthquake activity.Detection of road anomalies using smartphone data, facilitating crowdsourcing applications. The quest for more efficient real-time detection of anomalies in time series data is critically important in numerous applications and systems ranging from intelligent transportation, structural health monitoring, heart disease, and earthquake prediction. Although the range of application is wide, anomaly detection algorithms are usually domain specific and build on experts knowledge. Here a new signal processing algorithm inspired by the deep learning paradigm is presented that combines wavelets, neural networks, and Hilbert transform. The algorithm performs robustly and is transferable. The proposed neural network structure facilitates learning short and long-term pattern interdependencies; a task usually hard to accomplish using standard neural network training algorithms. The paper provides guidelines for selecting the neural network's buffer size, training algorithm, and anomaly detection features. The algorithm learns the system's normal behavior and does not require the existence of anomalous data for assessing its statistical significance. This is an essential attribute in applications that require customization. Anomalies are detected by analysing hierarchically the instantaneous frequency and amplitude of the residual signal using probabilistic Receiver Operating Characteristics. The method is shown to be able to automatically detect anomalies in the Seismic Electrical Signal that could be used to predict earthquake activity. Furthermore, the method can be used in combination with crowdsourcing of smartphone data to locate road defects such as potholes and bumps for intervention and repair.

A real-time computer vision-based platform for fabric inspection part 1: algorithm

Developing an efficient real-time detection algorithm is quite important for an automated inspection system. This paper presents a practical method based on local singular value decomposition (SVD) and normalised cross-correlation (NCC) for real-time defect detection in woven fabrics. As fabric-textured images exhibit high periodicity among the repeated sub-patterns, non-defective or normal image samples (image patches) can be efficiently approximated as a linear combination of the basis vectors (BVs) obtained via SVD. Since these BVs are recovered from normal samples, they will only capture the key structural features of the non-defective images. When using the BVs to model new samples, we can expect defective or abnormal samples with structural features not found in normal cases will incur substantial approximation errors. Therefore, complex defect detection can be converted to a template matching problem, where the robust NCC is utilised to measure disparities between the original and its approximation for defect identification. Experimental results on various real-world fabrics exhibit accurate defect detection with low false alarm rate, and we also conduct a comparison with a feature extraction-based method to further confirm the effectiveness of our algorithm.

Efficient real-time detection of terahertz pulse radiation based on photoacoustic conversion by carbon nanotube nanocomposite

By integrating a photoacoustic transmitter based on a carbon nanotube nanocomposite and an optical microring resonator as an ultrasonic sensor, a low-noise terahertz pulse detection system is demonstrated at room temperature. The response time and the noise-equivalent detectability energy are on the order of 0.1 µs and 220 pJ, respectively.

플로우 분석을 이용한 분산 서비스 거부 공격 탐지 방법

Abstract Today, Distributed denial of service (DDoS) attack present a very serious threat to the stability of the internet. The DDoS attack, which is consuming all of the computing or communication resources necessary for the service, is known very difficult to protect. The DDoS attack usually transmits heavy traffic data to networks or servers and they cannot handle the normal service requests because of running out of resources. It is very hard to prevent the DDoS attack. Therefore, an intrusion detection system on large network is need to efficient real-time detection. In this paper, we propose the detection mechanism using analysis of flow information against DDoS attacks in order to guarantee the transmission of normal traffic and prevent the flood of abnormal traffic. The OPNET simulation results show that our ideas can provide enough services in DDoS attack. Key Words : DDoS Attack Detection, Quality-of-Service, Flow Information * 정회원, 경북대학교 컴퓨터학부접수일자 : 2014년 1월 8일, 수정완료 : 2014년 1월 29일 게재확정일자: 2014년 2월 7일Received: 8 January, 2014 / Revised: 29 January, 2014Accepted: 7 February, 2014

Auxiliary signal design for failure detection in differential-algebraic equations

Fault detection and identification (FDI) are important tasks in most modern industrial and mechanical systems and processes. Many of these systems are most naturally modeled by differential-algebraic equations (DAE). This paper addresses active fault detection in DAE. A technique is presented to calculate an auxiliary test signal guaranteeing detection, assuming bounded additive noise. An efficient real time detection algorithm is also provided as are example simulations. The extension to model uncertainty is discussed.