High Detection Accuracy Rate Research Articles

LSTM-Enabled Big Data Security Framework Integrating Kerberos Authentication on AWS for Robust Cloud Protection

In today’s digital era, safeguarding cloud environments has become paramount due to the increasing complexity and volume of cyber threats. This research presents an innovative LSTM-enabled Big Data Security Framework that integrates the Kerberos Authentication Protocol on the Amazon Web Services (AWS) cloud platform. The proposed framework leverages the scalability and processing capabilities of Hadoop to manage and analyze large-scale data while ensuring robust security through Kerberos. The Long Short-Term Memory (LSTM) model is deployed to predict and detect anomalies in authentication requests, enhancing the framework's ability to mitigate unauthorized access. By combining LSTM’s predictive analytics with Kerberos’s ticket-based authentication, the framework ensures a multi-layered security architecture capable of identifying threats in real-time. Hadoop’s distributed computing environment further enables efficient processing of security logs and user behavior data, making it ideal for large-scale enterprise applications. A comprehensive evaluation of the framework demonstrates its efficacy in reducing false positives and achieving a high detection accuracy rate for potential threats. Key metrics, including precision, recall, and F1 score, validate the robustness of the approach. Additionally, the framework showcases superior scalability and adaptability to dynamic workloads in the AWS environment, ensuring consistent performance under varying data loads. This research contributes to the advancement of secure cloud computing by bridging the gap between traditional authentication methods and machine learning-driven threat detection. The integration of Kerberos with Big Data analytics and LSTM-based models establishes a foundation for future work in securing large-scale cloud ecosystems.

ANÁLISE DE UM PROTÓTIPO DE UMA ESTEIRA TRANSPORTADORA COM AUTOMAÇÃO INDUSTRIAL PARA SEPARAÇÃO DE PEÇAS METÁLICAS E NÃO METÁLICAS UTILIZANDO ARDUINO UNO

Introduction: The use of Arduino Uno and the LS PLC allows for an innovative approach to process automation, offering a practical and effective solution for industrial applications. This automated system is designed to increase productivity and accuracy in material separation, minimizing manual intervention and associated errors. Objective: to analyze a prototype of a conveyor belt with industrial automation for separating metal and non-metal parts using Arduino. Methodology: This study is a qualitative case study of the development of a prototype conveyor belt with industrial automation using the arduino system. Results: The data showed that the NPN inductive and ultrasonic sensors had high detection accuracy rates, with 95% and 97% success, respectively. Communication between the Arduino and the PLC was efficient, allowing the system to respond quickly to sensor readings and trigger the actuators synchronously. The implementation of safety devices, such as pushbuttons and circuit breakers, ensured system protection, while the visual monitoring provided by the 16×2 LCD display facilitated real-time monitoring of operations. Conclusion: The automation of the conveyor belt proved to be a viable and effective solution for sorting materials in an industrial context. The integration of the components and the efficient communication between them were fundamental to the success of the project. This project serves as a solid foundation for future innovations in industrial automation, contributing to the modernization and efficiency of production processes.

ANÁLISE DE UM PROTÓTIPO DE UMA ESTEIRA TRANSPORTADORA COM AUTOMAÇÃO INDUSTRIAL PARA SEPARAÇÃO DE PEÇAS METÁLICAS E NÃO METÁLICAS UTILIZANDO ARDUINO UNO

Introduction: The use of Arduino Uno and the LS PLC allows for an innovative approach to process automation, offering a practical and effective solution for industrial applications. This automated system is designed to increase productivity and accuracy in material separation, minimizing manual intervention and associated errors. Objective: to analyze a prototype of a conveyor belt with industrial automation for separating metal and non-metal parts using Arduino. Methodology: This study is a qualitative case study of the development of a prototype conveyor belt with industrial automation using the arduino system. Results: The data showed that the NPN inductive and ultrasonic sensors had high detection accuracy rates, with 95% and 97% success, respectively. Communication between the Arduino and the PLC was efficient, allowing the system to respond quickly to sensor readings and trigger the actuators synchronously. The implementation of safety devices, such as pushbuttons and circuit breakers, ensured system protection, while the visual monitoring provided by the 16×2 LCD display facilitated real-time monitoring of operations. Conclusion: The automation of the conveyor belt proved to be a viable and effective solution for sorting materials in an industrial context. The integration of the components and the efficient communication between them were fundamental to the success of the project. This project serves as a solid foundation for future innovations in industrial automation, contributing to the modernization and efficiency of production processes.

Deep Learning for Smart Grid Intrusion Detection: A Hybrid CNN-LSTM-Based Model

As digital technology becomes more deeply embedded in power systems, protecting the communication networks of Smart Grids (SG) has emerged as a critical concern. Distributed Network Protocol 3 (DNP3) represents a multi-tiered application layer protocol extensively utilized in Supervisory Control and Data Acquisition (SCADA)-based smart grids to facilitate real-time data gathering and control functionalities. Robust Intrusion Detection Systems (IDS) are necessary for early threat detection and mitigation because of the interconnection of these networks, which makes them vulnerable to a variety of cyberattacks. To solve this issue, this paper develops a hybrid Deep Learning (DL) model specifically designed for intrusion detection in smart grids. The proposed approach is a combination of the Convolutional Neural Network (CNN) and the Long-Short-Term Memory algorithms (LSTM). We employed a recent intrusion detection dataset (DNP3), which focuses on unauthorized commands and Denial of Service (DoS) cyberattacks, to train and test our model. The results of our experiments show that our CNN-LSTM method is much better at finding smart grid intrusions than other deep learning algorithms used for classification. In addition, our proposed approach improves accuracy, precision, recall, and F1 score, achieving a high detection accuracy rate of 99.50%.

Deepfake Face Detection Using Machine Learning with LSTM

Deep fake videos, which employ artificial intelligence to manipulate and generate highly convincing fake content, have emerged as a significant threat to society, potentially undermining trust in visual media. Detecting these deceptive videos is outmost importance to combat the spread of misinformation and protect the integrity of digital media. In this study, we propose a novel approach for deep fake face video detection utilizing Long Short-Term Memory (LSTM) networks, a type of Recurrent Neural Network (RNN). Our approach capitalizes on the temporal patterns and context within video sequences, harnessing the unique strengths of LSTM in capturing sequential information. We demonstrate the effectiveness of our methodology by training the LSTM network on a diverse dataset comprising both real and deep fake videos. The network’s ability to learn temporal dependencies and identify inconsistencies in facial expressions, eye movements, and other subtle cues allows it to distinguish between genuine and manipulated content. To further enhance the accuracy and robustness of our deep fake face detection system, we integrate pre-processing techniques for frame- level analysis, such as optical flow computation and facial landmarks extraction. Additionally, we employ a comprehensive ensemble of LSTM models and other machine learning algorithms to improve the overall detection performance. In our experiments, we evaluate the LSTM-based deep fake detection system on a large-scale dataset of both known and unseen deep fake videos, achieving high detection accuracy and low false positive rates. We also compare our approach with existing methods, demonstrating its superiority in terms of robustness and generalization. The results of this study signify the potential of LSTM-based models for mitigating the adverse effects of deep fake content on society. As deep fake technology continues to evolve, our approach showcases a promising step towards combating the dissemination of deceptive multimedia, promoting media integrity, and upholding trust in visual information. Keywords: LSTM Networks, Recurrent Neural Network, Optical flow computation, Facial landmarks extraction, False positive rates.

Validation of Machine Learning-Aided and Power Line Communication-Based Cable Monitoring Using Measurement Data.

The implementation of power line communications (PLC) in smart electricity grids provides us with exciting opportunities for real-time cable monitoring. In particular, effective fault classification and estimation methods employing machine learning (ML) models have been proposed in the recent past. Often, the research works presenting PLC for ML-aided cable diagnostics are based on the study of synthetically generated channel data. In this work, we validate ML-aided diagnostics by integrating measured channels. Specifically, we consider the concatenation of clustering as a data pre-processing procedure and principal component analysis (PCA)-based dimension reduction for cable anomaly detection. Clustering and PCA are trained with measurement data when the PLC network is working under healthy conditions. A possible cable anomaly is then identified from the analysis of the PCA reconstruction error for a test sample. For the numerical evaluation of our scheme, we apply an experimental setup in which we introduce degradations to power cables. Our results show that the proposed anomaly detector is able to identify a cable degradation with high detection accuracy and low false alarm rate.

Optimizing anomaly-based attack detection using classification machine learning

AbstractOne of the significant aspects of our digital world is that data are literally everywhere, and it is increasing. On the other hand, the number of cyberattacks aiming to seize this data and use it illegally is increasing at an exponential rate, and this is the challenge. Therefore, intrusion detection systems (IDS) have attracted considerable interest from researchers and industries. In this regard, machine learning (ML) techniques are playing a pivotal role as they put the responsibility of analyzing enormous amounts of data, finding patterns, classifying intrusions, and solving issues on computers instead of humans. This paper implements two separate classification layers of ML-based algorithms with the recently published NF-UQ-NIDS-v2 dataset, preprocessing two volumes of sample records (100 k and 10 million), utilizing MinMaxScaler, LabelEncoder, selecting superlative features by recursive feature elimination, normalizing the data, and optimizing hyper-parameters for classical algorithms and neural networks. With a small dataset volume, the results of the classical algorithms layer show high detection accuracy rates for support vector (98.26%), decision tree (98.78%), random forest (99.07%), K-nearest neighbors (98.16%), CatBoost (99.04%), and gradient boosting (98.80%). In addition, the layer of neural network algorithms has proven to be a very powerful technology when using deep learning, particularly due to its unique ability to effectively handle enormous amounts of data and detect hidden correlations and patterns; it showed high detection results, which were (98.87%) for long short-term memory and (98.56%) for convolutional neural networks.

The individual color pattern on the back of Bufotes viridis balearicus (Boettger, 1880) allows individual photo-identification recognition for population studies

This study explores the potential of photo-identification methods (PIMs) as a viable, non-invasive, and ethical tool for wildlife studies, with a specific focus on anuran species such as Bufotes viridis balearicus (Boettger, 1880). Although the automatic photo-identification suite (APHIS) software was initially designed for lizard identification, our research shows its adaptability for anuran species, achieving a high detection accuracy rate of 95.28%, thus obtaining outstanding and higher values in compared to previous studies on this species. Crucially, our findings indicate that the success of PIM and the efficacy of image identification software like APHIS is dependent on the quality and standardization of the images collected. The study also underscores the importance of practical experience and continuous learning for the optimal utilization of software like APHIS. Despite occasional false rejected matches, the overall strong performance metrics with low false rejection rate demonstrate that these instances do not significantly impact the reliability of the technique. Thus, this research highlights the importance of careful implementation, continuous learning, and image quality control in leveraging the full potential of image identification software in wildlife studies.

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.

Improved Cyber Attack Detection Using MLB-FQS: A Novel Modified Lagrange Butterfly-based Fuzzy Quasi-Linear SVM Algorithm

With the rapid enhancement of communication and computer networks, internet-based technologies provide numerous convenient and accessible services to the public all over the world. Meanwhile, the types and number of cyber attacks have been increasing day by day and create a serious threat to secure most confidential data. Hence, securing data is considered a crucial task and nowadays intrusion detection system is one of the most effective and promising techniques to provide secure data by detecting malicious data. However, it becomes extremely hard in preventing the network system from attack due to imbalanced data that results in low detection and poor accuracy rates. Therefore, a novel intrusion detection mechanism is proposed to accurately detect cyber intrusion with high detection accuracy rate. The Intrusion detection mechanism comprises three significant phases namely the data pre-processing phase, the feature selection phase as well as attack detection phase. Various pre-processing techniques namely data normalization, data conversion as well as one-hot encoding process are utilized to provide an appropriate format that is suitable for analysis. In the feature selection phase, a Modified Lagrange Butterfly (MLB) optimization is employed to select optimal features for effective detection. Finally, a Fuzzy Quasi-Linear Support Vector Machine (FQL-SVM) classifier is employed to determine the network as two different types of classes namely the abnormal or malicious classes. After examining on malware dataset, the experimental analysis demonstrates a high classification accuracy rate of 98.41% which is better than other comparative techniques.

Global attention network with multiscale feature fusion for infrared small target detection

A global attention network (GANet) with multiscale feature fusion is proposed to detect infrared small target by introducing a transformer attention module and an adaptive asymmetric fusion module. The transformer attention module is designed to learn the long-range relationship between small targets and background. The adaptive asymmetric fusion module is employed to aggregate the multiscale contextual information from high-level and low-level features. In addition, a target duplicating data augmentation strategy by copy-pasting small targets many times is proposed to increase the positive samples during training for suppressing the class-imbalance problem. Extensive experiments on infrared small target datasets demonstrate that our method can achieve high detection accuracy and low false alarm rate compared with some state-of-the-art model-driven and data-driven methods.

Improving deep learning acoustic classifiers with contextual information for wildlife monitoring

Bioacoustics, the exploration of animal vocalizations and natural soundscapes, has emerged as a valuable tool for studying species within their habitats, particularly those that are challenging to observe. This approach has broadened the horizons of biodiversity assessment and ecological research. However, monitoring wildlife with acoustic recorders produces large volumes of data that can be labor-intensive to analyze. Deep learning has recently transformed many computational disciplines by enabling the automated processing of large and complex datasets and has gained attention within the bioacoustics community. Despite the revolutionary impact of deep learning on acoustic detection and classification, attaining both high detection accuracy and low false positive rates in bioacoustics remains a significant challenge. An intriguing yet unexplored avenue for enhancing deep learning in bioacoustics involves the utilization of contextual information, such as time and location, to discern animal vocalizations within acoustic recordings. As a first case study, a multi-branch Convolutional Neural Network (CNN) was developed to classify 22 different bird songs using spectrograms as a first input, and spatial metadata as a secondary input. A comparison was made to a baseline model with only spectrogram input. A geographical prior neural network was trained, separately, to estimate the probability of a species occurring at a given location. The output of this network was combined with the baseline CNN. As a second case study, temporal data and spectrograms were used as input to a multi-branch CNN for the detection of Hainan gibbon (Nomascus hainanus) calls, the world’s rarest primate. Our findings demonstrate that adding metadata to the bird song classifier significantly improves classification performance, with the highest improvement achieved using the geographical prior model (F1-score of 87.78% compared to 61.02% for the baseline model). The multi-branch CNNs also proved efficient (F1-scores of 76.87% and 78.77%) and simpler to use than the geographical prior. In the second case study, our findings revealed a decrease in false positives by 63% (94% of the calls were detected) when the metadata was used by the multi-branch CNN, and an increase of 19% in gibbon detection. This study has uncovered an exciting new avenue for improving classifier performance in bioacoustics. The methodology described in this study can assist ecologists, wildlife management teams, and researchers in reducing the amount of time spent analyzing large acoustic datasets obtained from passive acoustic monitoring studies. Our approach can be adapted and applied to other calling species, and thus tailored to other use cases.

Machine Learning Based Diagnosis for Diabetic Retinopathy for SKPD-PSC

The study aimed to apply to Machine Learning (ML) researchers working in image processing and biomedical analysis who play an extensive role in comprehending and performing on complex medical data, eventually improving patient care. Developing a novel ML algorithm specific to Diabetic Retinopathy (DR) is a challenge and need of the hour. Biomedical images include several challenges, including relevant feature selection, class variations, and robust classification. Although the current research in DR has yielded favourable results, several research issues need to be explored. There is a requirement to look at novel pre-processing methods to discard irrelevant features, balance the obtained relevant features, and obtain a robust classification. This is performed using the Steerable Kernalized Partial Derivative and Platt Scale Classifier (SKPD-PSC) method. The novelty of this method relies on the appropriate non-linear classification of exclusive image processing models in harmony with the Platt Scale Classifier (PSC) to improve the accuracy of DR detection. First, a Steerable Filter Kernel Pre-processing (SFKP) model is applied to the Retinal Images (RI) to remove irrelevant and redundant features and extract more meaningful pathological features through Directional Derivatives of Gaussians (DDG). Next, the Partial Derivative Image Localization (PDIL) model is applied to the extracted features to localize candidate features and suppress the background noise. Finally, a Platt Scale Classifier (PSC) is applied to the localized features for robust classification. For the experiments, we used the publicly available DR detection database provided by Standard Diabetic Retinopathy (SDR), called DIARETDB0. A database of 130 image samples has been collected to train and test the ML-based classifiers. Experimental results show that the proposed method that combines the image processing and ML models can attain good detection performance with a high DR detection accuracy rate with minimum time and complexity compared to the state-of-the-art methods. The accuracy and speed of DR detection for numerous types of images will be tested through experimental evaluation. Compared to state-of-the-art methods, the method increases DR detection accuracy by 24% and DR detection time by 37.

Impersonation Attack Detection in Mobile Edge Computing by Levering SARSA Technique in Physical Layer Security

Smart health systems typically integrate sensor technology with the Internet of Things, enabling healthcare systems to monitor patients. These biomedical applications collect healthcare data through remote sensors and transfer the data to a centralized system for analysis. However, the communication between the edge node and the mobile user is susceptible to impersonation attacks in mobile edge computing (MEC) for the biomedical application. For this purpose, we propose a detection mechanism for medical and healthcare services, i.e., reinforcement learning for impersonation attacks. We construct a system model of MEC, a key generation model (KGM), and an impersonation attack model (IAM). In addition, we also design an impersonation attack detection algorithm based on the SARSA technique under the IAM. In our proposed work, the SARSA-based method outplays the detection of impersonation attacks in the dynamic environment compared to the traditional Q-learning technique. Finally, we evaluate the false alarm rate (FAR), miss detection rate (MDR), and average error rate (AER) in the hypothesis tests to compare the performance of our proposed method with the traditional Q-learning. In comparison to the classic Q-learning based technique, simulation experiments show that the suggested approach can avoid impersonation attacks in a dynamic environment for medical and healthcare services. The results also indicate that the SARSA technique has a high detection accuracy and low average error rate compared to the conventional Q-learning based approach.

Review of Detection Denial of Service Attacks using Machine Learning through Ensemble Learning

Today's network hacking is more resource-intensive because the goal is to prohibit the user from using the network's resources when the target is either offensive or for financial gain, especially in businesses and organizations. That relies on the Internet like Amazon Due to this, several techniques, such as artificial intelligence algorithms like machine learning (ML) and deep learning (DL), have been developed to identify intrusion and network infiltration and discriminate between legitimate and unauthorized users. Application of machine learning and ensemble learning algorithms to various datasets, consideration of homogeneous ensembles using a single algorithm type or heterogeneous ensembles using several algorithm types, and evaluation of the discovery outcomes in terms of accuracy or discovery error for detecting attacks. The survey literature provides an overview of the many approaches and approaches of one or more machine-learning algorithms used in various datasets to identify denial of service attacks. It has also been shown that employing the hybrid approach is the most common and produces better attack detection outcomes than using the sole approaches. Numerous machine learning techniques, including support vector machines (SVM), K-Nearest Neighbors (KNN), and ensemble learning like random forest (RF), bagging, and boosting, are illustrated in this work (DT). That is employed in several articles to identify different denial of service (DoS) assaults, including the trojan horse, teardrop, land, smurf, flooding, and worm. That attacks network traffic and resources to deny users access to the resources or to steal confidential information from the company without damaging the system and employs several algorithms to obtain high attack detection accuracy and low false alarm rates.

Automatic Building of a Powerful IDS for The Cloud Based on Deep Neural Network by Using a Novel Combination of Simulated Annealing Algorithm and Improved Self- Adaptive Genetic Algorithm

Cloud computing (CC) is the fastest-growing data hosting and computational technology that stands today as a satisfactory answer to the problem of data storage and computing. Thereby, most organizations are now migratingtheir services into the cloud due to its appealing features and its tangible advantages. Nevertheless, providing privacy and security to protect cloud assets and resources still a very challenging issue. To address the aboveissues, we propose a smart approach to construct automatically an efficient and effective anomaly network IDS based on Deep Neural Network, by using a novel hybrid optimization framework “ISAGASAA”. ISAGASAA framework combines our new self-adaptive heuristic search algorithm called “Improved Self-Adaptive Genetic Algorithm” (ISAGA) and Simulated Annealing Algorithm (SAA). Our approach consists of using ISAGASAA with the aim of seeking the optimal or near optimal combination of most pertinent values of the parametersincluded in building of DNN based IDS or impacting its performance, which guarantee high detection rate, high accuracy and low false alarm rate. The experimental results turn out the capability of our IDS to uncover intrusionswith high detection accuracy and low false alarm rate, and demonstrate its superiority in comparison with stateof-the-art methods.

Enhancing Front-Vehicle Detection in Large Vehicle Fleet Management

Transportation safety has been widely discussed for avoiding forward collisions. The broad concept of remote sensing can be applied to detect the front of vehicles without contact. The traditional Haar features use adjacent rectangular areas for many ordinary vehicle studies to detect the front vehicle images in practice. This paper focused on large vehicles using a front-installed digital video recorder (DVR) with a near-infrared (NIR) camera. The views of large and ordinary vehicles are different; thus, this study used a deep learning method to process progressive improvement in moving vehicle detection. This study proposed a You Only Look Once version 4 (YOLOv4) supplemented with the fence method, called YOLOv4(III), to enhance vehicle detection. This method had high detection accuracy and low false omission rates using the general DVR equipment, and it provided comparison results. There was no need to have a high specification front camera, and the proposed YOLOv4(III) was found to have competitive performance. YOLOv4(III) reduced false detection rates and had a more stable frame per second (FPS) performance than with Haar features. This improved detection method can give an alert for large vehicle drivers to avoid serious collisions, leading to a reduction in the waste of social resources.

Vibration fault detection of vehicle transmission gearbox based on time-frequency analysis of non-stationary signals

The traditional vibration fault detection method of vehicle transmission gearbox cannot realise feature extraction of non-stationary fault signal, which leads to large error of detection result and low fault detection rate. Therefore, this paper proposes a new method of vehicle transmission gearbox vibration fault detection based on non-stationary signal time-frequency analysis. The components of transmission gearbox are analysed, and the transmission path of vibration fault signal of transmission gearbox is obtained according to its structural characteristics. The time-frequency characteristics of non-stationary signals are extracted by empirical mode method to complete the vibration fault detection of vehicle transmission gearbox. Experimental results show that the proposed method has high detection accuracy and high detection rate under the premise of amplitude and phase modulation of fault signal.

Epileptic EEG Classification by Using Time-Frequency Images for Deep Learning.

Epilepsy is one of the most common brain disorders worldwide. The most frequently used clinical tool to detect epileptic events and monitor epilepsy patients is the EEG recordings. There have been proposed many computer-aided diagnosis systems using EEG signals for the detection and prediction of seizures. In this study, a novel method based on Fourier-based Synchrosqueezing Transform (SST), which is a high-resolution time-frequency (TF) representation, and Convolutional Neural Network (CNN) is proposed to detect and predict seizure segments. SST is based on the reassignment of signal components in the TF plane which provides highly localized TF energy distributions. Epileptic seizures cause sudden energy discharges which are well represented in the TF plane by using the SST method. The proposed SST-based CNN method is evaluated using the IKCU dataset we collected, and the publicly available CHB-MIT dataset. Experimental results demonstrate that the proposed approach yields high average segment-based seizure detection precision and accuracy rates for both datasets (IKCU: 98.99% PRE and 99.06% ACC; CHB-MIT: 99.81% PRE and 99.63% ACC). Additionally, SST-based CNN approach provides significantly higher segment-based seizure prediction performance with 98.54% PRE and 97.92% ACC than similar approaches presented in the literature using the CHB-MIT dataset.

Computer Vision based Early Electrical Fire-detection in Video Surveillance oriented for Building environment

This work presents autonomous electrical fire-detection and localization using computer vision based techniques. The proposed work uses YOLO v2 to extract the electrical fire features more effectively than other conventional and machine learning approaches. This working model is tested on commercial and residential building as well as indoor and outdoor environments. This framework has achieved high detection accuracy and low false alarm rate. Besides, the proposed frame work can be used for early real-time electrical fire detection in surveillance videos and we present experimental results for electrical fire localization in CCTV footage using the deep learning architecture proposed in this work.