Performance Of Detection System Research Articles

Detecting Hate Speech on Twitter Network using Ensemble Machine Learning

Twitter is habitually exploited now-a-days to propagate torrents of hate speeches, misogynistic, and misandry tweets that are written in slang. Machine learning methods have been explored in manifold studies to address the inherent challenges of hate speech detection in online spaces. Nevertheless, language has subtleties that can make it stiff for machines to adequately comprehend and disambiguate the semantics of words that are heavily dependent on the usage context. Deep learning methods have demonstrated promising results for automatic hate speech detection, but they require a significant volume of training data. Classical machine learning methods suffer from the innate problem of high variance that in turn affects the performance of hate speech detection systems. This study presents a voting ensemble machine learning method that harnesses the strengths of logistic regression, decision trees, and support vector machines for the automatic detection of hate speech in tweets. The method was evaluated against ten widely used machine learning methods on two standard tweet data sets using the famous performance evaluation metrics to achieve an improved average F1-score of 94.2%.

Design of Chinese Grammar Recognition and Error Correction Model Based on the Deep Neural Network.

In order to further improve the performance of the automatic grammar error detection system, a new Chinese grammar recognition and correction model is proposed in this paper. Based on the transformer attention mechanism, the bias matrix of Gaussian distribution is added to improve the attention of the model to local text and strengthen the information extraction of wrong words and surrounding words in the wrong text. In addition, the ON_LSTM model is used to extract grammatical information from the special grammatical structure of error text. The experimental results show that the two methods can effectively improve the accuracy and recall rate, and the fused model achieves the highest F1 value. Finally, the Chinese text error correction system is designed to expand the application scope of the model, which helps to reduce the human cost in language learning.

Aircraft Trajectory Prediction With Enriched Intent Using Encoder-Decoder Architecture

Aircraft trajectory prediction is a challenging problem in air traffic control, especially for conflict detection. Traditional trajectory predictors require a variety of inputs such as flight-plans, aircraft performance models, meteorological forecasts, etc. Many of these data are subjected to environmental uncertainties. Further, limited information about such inputs, especially the lack of aircraft tactical intent, makes trajectory prediction a challenging task. In this work, we propose a deep learning model that performs trajectory prediction by modeling and incorporating aircraft tactical intent. The proposed model adopts the encoder-decoder architecture and makes use of the convolutional layer as well as Gated Recurrent Units (GRUs). The proposed model does not require explicit information about aircraft performance and wind data. Results demonstrate that the provision of enriched aircraft intent, together with appropriate model design, could improve the prediction error up to 30% at a prediction horizon of 10 minutes (from 4.9 nautical miles to 3.4 nautical miles). The model also guarantees the mean error growth rate with increasing look-ahead time to be lower than 0.2 nautical miles per minute. In addition, the model offers a very low variance in the prediction, which satisfies the variance-standard specified by EUROCONTROL (EU Organization for Safety and Navigation of Air Traffic) for trajectory predictors. The proposed model also outperforms the state-of-the-art trajectory prediction model, where the Root Mean Square Error (RMSE) is reduced from 0.0203 to 0.0018 for latitude prediction, and from 0.0482 to 0.0021 for longitude prediction in a single prediction step of 15 seconds look-ahead. We showed that the pre-trained model on ADS-B data maintains its high performance, in terms of cross-track and along-track errors, when being validated in the Bluesky Air Traffic Simulator. The proposed model would significantly improve the performance of conflict detection systems where such trajectory prediction models are needed.

Scintillation light detection in the 6-m drift length ProtoDUNE Dual Phase liquid argon TPC

The Deep Underground Neutrino Experiment (DUNE) is a leading-edge experiment for long-baseline neutrino oscillation studies, neutrino astrophysics and nucleon decay searches. ProtoDUNE-Dual Phase (DP) is a 6 × 6 × 6 m3 liquid argon time-projection-chamber (LArTPC) operated at the CERN Neutrino Platform in 2019–2020 as a prototype of the DUNE far detector. In ProtoDUNE-DP, the scintillation and electroluminescence light produced by cosmic muons in the LArTPC is collected by photomultiplier tubes placed up to 7 m away from the ionizing track. In this paper, we present the performance of the ProtoDUNE-DP photon detection system, comparing different wavelength-shifting techniques and the use of xenon-doped LAr as a promising option for future large LArTPCs. The scintillation light production and propagation processes are analyzed and compared to simulations, improving understanding of the liquid argon properties.

Improve Performance of Deep Learning Polyp Detection System in Clinical Colonoscopy

Improve Performance of Deep Learning Polyp Detection System in Clinical Colonoscopy

Sustainable Intrusion Detection with New Attack Classification in Private Clouds

Traditional data-driven intrusion detection systems (IDSs) are typically based on the recognition of some specific features, regulations or patterns belonging to the well-defined known attacks, so they cannot separate new or unknown attacks from abnormities and may even confuse new attacks and legitimate behaviors. With the development of artificial intelligence (AI) technology, it becomes the mainstream technology to improve the detection performance of intrusion detection system. However, the available AI-driven IDSs can hardly classify different types of new attacks separated from abnormities, and they are usually not dedicated to the private cloud, edge or fog computing environments, where the update of the new attack recognition can be very different from that of the public environments. In this article, we present a novel sustainable and AI-driven intrusion detection scheme to support the classification of new attacks in the private clouds. We first adopt the convolutional neural network algorithm to recognize the known attacks, and then propose a new model of recognition and classification for unknown attacks based on network behaviors. We further propose a new approach to update the attacks recognition model for the private clouds. Finally, we provide extensive experiment results to demonstrate that our proposed scheme outperforms the previous IDSs in terms of attack detection accuracy, attack classification accuracy and updating efficiency.

Data Curation and Quality Evaluation for Machine Learning-Based Cyber Intrusion Detection

Intrusion detection is an essential task for protecting the cyber environment from attacks. Many studies have proposed sophisticated models to detect intrusions from a large amount of data, yet they ignore the fact that poor data quality has a direct impact on the performance of the intrusion detection systems. This article first summarizes existing machine learning-based intrusion detection systems and the datasets used for building these systems. Then the data preparation workflow and data quality requirements for intrusion detection are discussed. In order to investigate how data quality affects model performance, we conducted experiments on 11 HIDS datasets using eight machine learning (ML) models and two pre-trained language models (BERT and GPT-2). The experimental results show: 1. BERT and GPT outperform the other models on all of the datasets. 2. The pre-trained models and the classic ML models behave differently when duplicate data and overlapped data are removed from a dataset. The pre-trained models are more capable of learning from duplicate and overlapped data compared to the classic ML models. 3. Removing overlaps and duplicates can improve the performances of the pre-trained models and the traditional ML models on most datasets used in this study. However, doing this can sometimes cause model performance to be decreased. 4. The reliability of model performance is affected when a testing data contain duplicates. 5. The overlapped rate between the normal and intrusion classes seems to have an inverse relationship to the pre-trained models’ performances on the intrusion detection task. Given the results, we discuss model selection in HIDS, and quality assurance in training data and testing data based on nine data quality dimensions.

Improve Performance of Deep Learning Polyp Detection System in Clinical Colonoscopy

Improve Performance of Deep Learning Polyp Detection System in Clinical Colonoscopy

Adversarial Training Against Adversarial Attacks for Machine Learning-Based Intrusion Detection Systems

Intrusion detection system plays an important role in defending networks from security breaches. End-to-end machine learning-based intrusion detection systems are being used to achieve high detection accuracy. However, in case of adversarial attacks, that cause misclassification by introducing imperceptible perturbation on input samples, performance of machine learning-based intrusion detection systems is greatly affected. Though such problems have widely been discussed in image processing domain, very few studies have investigated network intrusion detection systems and proposed corresponding defence. In this paper, we attempt to fill this gap by using adversarial attacks on standard intrusion detection datasets and then using adversarial samples to train various machine learning algorithms (adversarial training) to test their defence performance. This is achieved by first creating adversarial sample based on Jacobian-based Saliency Map Attack (JSMA) and Fast Gradient Sign Attack (FGSM) using NSLKDD, UNSW-NB15 and CICIDS17 datasets. The study then trains and tests JSMA and FGSM based adversarial examples in seen (where model has been trained on adversarial samples) and unseen (where model is unaware of adversarial packets) attacks. The experiments includes multiple machine learning classifiers to evaluate their performance against adversarial attacks. The performance parameters include Accuracy, F1-Score and Area under the receiver operating characteristic curve (AUC) Score.

Intrusion Detection Systems Using Blockchain Technology: A Review, Issues and Challenges

Intrusion detection systems that have emerged in recent decades can identify a variety of malicious attacks that target networks by employing several detection approaches. However, the current approaches have challenges in detecting intrusions, which may affect the performance of the overall detection system as well as network performance. For the time being, one of the most important creative technological advancements that plays a significant role in the professional world today is blockchain technology. Blockchain technology moves in the direction of persistent revolution and change. It is a chain of blocks that covers information and maintains trust between individuals no matter how far apart they are. Recently, blockchain was integrated into intrusion detection systems to enhance their overall performance. Blockchain has also been adopted in healthcare, supply chain management, and the Internet of Things. Blockchain uses robust cryptography with private and public keys, and it has numerous properties that have leveraged security’s performance over peer-to-peer networks without the need for a third party. To explore and highlight the importance of integrating blockchain with intrusion detection systems, this paper provides a comprehensive background of intrusion detection systems and blockchain technology. Furthermore, a comprehensive review of emerging intrusion detection systems based on blockchain technology is presented. Finally, this paper suggests important future research directions and trending topics in intrusion detection systems based on blockchain technology.

In-Vehicle Stereo Vision Systems with Improved Ant Colony Optimization Based Lane Detection: A Solution to Accidents Involving Large Goods Vehicles Due to Blind Spots

This paper presents an in-vehicle stereo vision system as a solution to accidents involving large good vehicle due to blind spots using Nigeria as a case study. In this paper, a stereo-vision system was attached to the front of Large Good Vehicles (LGVs) with a view to presenting live feeds of vehicles close to the LGV vehicles and their distance away. The captured road images using the stereo vision system were optimized for effectiveness and optimal vehicle maneuvering using a modified metaheuristics algorithm called the simulated annealing Ant Colony Optimization (saACO) algorithm. The concept of simulated annealing is strategies used to automatically select the control parameters of the ACO algorithm. This helps to stabilize the performance of the ACO algorithm irrespective of the quality of the lane images captured in the in-vehicle vision system. The system is capable of notifying drivers through lane detection techniques of blind spots. This technique enables the driver to be more aware of what surrounds the vehicle and make decisions early. In order to test the system, the stereo-vision device was mounted on a Large good vehicle, driven in Zaria (a city in Kaduna state in Nigeria), and data were in the record. Out of 180 events, 42.22% of potential accident events were caused by Passenger Cars, while 27.22%, 18.33% and 12.22% were caused by two-wheelers, Large Good Vehicles and road users, respectively. In the same vein, the in-vehicle lane detection system shows a good performance of the saACO-based lane detection system and gives a better performance in comparison with the standard ACO method.

Intrusion Detection Method Based on Active Incremental Learning in Industrial Internet of Things Environment

Intrusion detection is a hot field in the direction of network security. Classical intrusion detection systems are usually based on supervised machine learning models. These offline-trained models usually have better performance in the initial stages of system construction. However, due to the diversity and rapid development of intrusion techniques, the trained models are often difficult to detect new attacks. In addition, very little noisy data in the training process often has a considerable impact on the performance of the intrusion detection system. This paper proposes an intrusion detection system based on active incremental learning with the adaptive capability to solve these problems. IDS consists of two modules, namely the improved incremental stacking ensemble learning detection method called Multi-Stacking model and the active learning query module. The stacking model can cope well with concept drift due to the diversity and generalization selection of its base classifiers, but the accuracy does not meet the requirements. The Multi-Stacking model improves the accuracy of the model by adding a voting layer on the basis of the original stacking. The active learning query module improves the detection of known attacks through the committee algorithm, and the improved KNN algorithm can better help detect unknown attacks. We have tested the latest industrial IoT dataset with satisfactory results.

A New Method Based on Copula Theory for Evaluating Detection Performance of Distributed-Processing Multistatic Radar System

A New Method Based on Copula Theory for Evaluating Detection Performance of Distributed-Processing Multistatic Radar System

Joint Transmitting Subarray Partition and Beamforming for Active Jamming Suppression in Phased‐MIMO Radar

AbstractAs the tradeoff between conventional phased‐array and multiple‐input multiple‐output (MIMO) radars, phased‐MIMO radar has been studied in recent years, which can simultaneously obtain coherent processing gain and waveform diversity gain. In this paper, in order to effectively suppress the jamming signal and improve the target detection performance of radar system, a joint transmitting subarray partition and beamforming method in phased‐MIMO radar is proposed. To be specific, at the transmitter, the transmitting array of radar system is partitioned into a certain number of non‐overlapped subarrays, and the subarray structure of transmitting array and the corresponding beamformer weight vectors are simultaneously optimized by constructing the optimization model of maximizing the signal‐to‐noise ratio of the whole received echoes under certain constraint conditions, so as to effectively maximize the power utilization of radar transmitter, reduce the peak sidelobe level of transmit beampattern and improve the radar anti‐jamming performance. Moreover, the adaptive beamforming technique is applied to suppress the jamming signal entering the receiver, so that the transmitting and receiving adaptive degrees of freedom are jointly utilized to enhance the anti‐jamming capability of radar system. Simulation results demonstrate the validity of the proposed method.

MIMO Multifunction RF Systems: Detection Performance and Waveform Design

This paper studies the detection performance of a multiple-input-multiple-output (MIMO) multifunction radio frequency (MFRF) system, which simultaneously supports radar, communication, and jamming. We show that the detection performance of the MIMO MFRF system improves as the transmit signal-to-interference-plus-noise-ratio (SINR) increases. To analyze the achievable SINR of the system, we formulate an SINR maximization problem under the communication and jamming functionality constraint as well as a transmit energy constraint. We derive a closed-form solution of this optimization problem for energy-constrained waveforms and present a detailed analysis of the achievable SINR. Moreover, we analyze the SINR for systems transmitting constant-modulus waveforms, which are often used in practice. We propose an efficient constant-modulus waveform design algorithm to maximize the SINR. Numerical results demonstrate the capability of a MIMO array to provide multiple functions, and also show the tradeoff between radar detection and the communication/jamming functionality.

Research on digital pulse processing techniques for silicon drift detector

Silicon drift detector (SDD) is a kind of high performance X-ray detector, which is widely used. The ray detection system based on SDD is composed of SDD device, preamplifier and pulse processing system. The now available pulse processing system has the problems of poor pulse pile-up rejection performance and being vulnerable to the parameter fluctuations of front-end system, which degrades the performance of detection system. A digital pulse processing system is proposed. In this system, analog-to-digital converter (ADC) directly samples the output signal of preamplifier, and transmits the data to the digital pulse processing platform for processing. According to the signal characteristics of SDD device and preamplifier, the influence of ADC sampling bits and sampling frequency on system performance is analyzed. Two optimized ADC sampling circuits are proposed to reduce energy resolution degradation induced by insufficient ADC sampling bits. The pulse shaping algorithm in the digital pulse processing system is studied. The results show that the shaping signal will not be distorted due to the parameter fluctuations of the front-end system, which proves the robustness of the digital pulse processing system. The digital pulse processing system is implemented and tested, and the correctness of the system is verified.

Scintillation light detection in the 6-m drift-length ProtoDUNE Dual Phase liquid argon TPC

DUNE is a dual-site experiment for long-baseline neutrino oscillation studies, neutrino astrophysics and nucleon decay searches. ProtoDUNE Dual Phase (DP) is a 6 times 6 times 6 m^3 liquid argon time-projection-chamber (LArTPC) that recorded cosmic-muon data at the CERN Neutrino Platform in 2019–2020 as a prototype of the DUNE Far Detector. Charged particles propagating through the LArTPC produce ionization and scintillation light. The scintillation light signal in these detectors can provide the trigger for non-beam events. In addition, it adds precise timing capabilities and improves the calorimetry measurements. In ProtoDUNE-DP, scintillation and electroluminescence light produced by cosmic muons in the LArTPC is collected by photomultiplier tubes placed up to 7 m away from the ionizing track. In this paper, the ProtoDUNE-DP photon detection system performance is evaluated with a particular focus on the different wavelength shifters, such as PEN and TPB, and the use of Xe-doped LAr, considering its future use in giant LArTPCs. The scintillation light production and propagation processes are analyzed and a comparison of simulation to data is performed, improving understanding of the liquid argon properties.

Enhancing the Performance of an Intrusion Detection System Using Spider Monkey Optimization in IoT

Enhancing the Performance of an Intrusion Detection System Using Spider Monkey Optimization in IoT

Recognition of Image-Based Plant Leaf Diseases Using Deep Learning Classification Models

Plant diseases are spread by a variety of pests, weeds, and pathogens and may have a devastating effect on agriculture, if not handled in a timely manner. Farmers face umpteen challenges from a proper water supply, untimely rain, storage facilities, and several plant diseases. Crops disease is the primary threat and it causes enormous loss to farmers in terms of production and finance. Identifying the disease from several hectares of agricultural land is a very difficult practice even with the presence of modern technology. Accurate and rapid illness prediction for early illness treatment to crops minimizes economical loss to the individual and further proves to be productive for healthy crops. Many studies use modern deep learning approaches to improve the accuracy and performance of object detection and identification systems. The suggested method notifies farmers of different agricultural illnesses, prompting them to take further essential precautions before the disease spreads to the whole agricultural field. The primary objective of this study is to detect the illnesses as soon as they begin to spread on the leaves of the plants. Super-Resolution Convolutional Neural Network (SRCNN) and Bicubic models are employed in the system to identify healthy and diseased leaves with an accuracy of 99.175 % and 99.156 % respectively.

Artificial Intelligence for Creating Low Latency and Predictive Intrusion Detection with Security Enhancement in Power Systems

Advancement in network technology has vastly increased the usage of the Internet. Consequently, there has been a rise in traffic volume and data sharing. This has made securing a network from sophisticated intrusion attacks very important to preserve users’ information and privacy. Our research focuses on combating and detecting intrusion attacks and preserving the integrity of online systems. In our research we first create a benchmark model for detecting intrusions and then employ various combinations of feature selection techniques based upon ensemble machine learning algorithms to improve the performance of the intrusion detection system. The performance of our model was investigated using three evaluation metrics namely: elimination time, accuracy and F1-score. The results of the experiment indicated that the random forest feature selection technique had the minimum elimination time, whereas the support vector machine model had the best accuracy and F1-score. Therefore, conclusive evidence could be drawn that the combination of random forest and support vector machine is suitable for low latency and highly accurate intrusion detection systems.