Performance Of Detection System Research Articles

Improved TLBO-JAYA Algorithm for Subset Feature Selection and Parameter Optimisation in Intrusion Detection System

Many optimisation-based intrusion detection algorithms have been developed and are widely used for intrusion identification. This condition is attributed to the increasing number of audit data features and the decreasing performance of human-based smart intrusion detection systems regarding classification accuracy, false alarm rate, and classification time. Feature selection and classifier parameter tuning are important factors that affect the performance of any intrusion detection system. In this paper, an improved intrusion detection algorithm for multiclass classification was presented and discussed in detail. The proposed method combined the improved teaching-learning-based optimisation (ITLBO) algorithm, improved parallel JAYA (IPJAYA) algorithm, and support vector machine. ITLBO with supervised machine learning (ML) technique was used for feature subset selection (FSS). The selection of the least number of features without causing an effect on the result accuracy in FSS is a multiobjective optimisation problem. This work proposes ITLBO as an FSS mechanism, and its algorithm-specific, parameterless concept (no parameter tuning is required during optimisation) was explored. IPJAYA in this study was used to update the C and gamma parameters of the support vector machine (SVM). Several experiments were performed on the prominent intrusion ML dataset, where significant enhancements were observed with the suggested ITLBO-IPJAYA-SVM algorithm compared with the classical TLBO and JAYA algorithms.

Improved PSO for optimizing the performance of intrusion detection systems

Improved PSO for optimizing the performance of intrusion detection systems

Tl concentration and its variation in a CsI(Tl) crystal for the CALIFA detector

One of the factors that can contribute to the resolution of long, doped inorganic scintillators used for nuclear spectroscopy is the variation of the dopant concentration over the length the detector crystal. In this work an investigation of such potential variations in one of the CsI(Tl) scintillators used in the calorimeter, CALIFA, of the R3B experiment at FAIR, has been performed using particle induced X-ray emission. No statistically significant gradient in doping level was found along the long axis of the investigated sample crystal and the mean value of the Tl concentration was measured to be 0.0839(38)% by weight. This corresponds to a light output of 97.3−1.7+1.3% relative to the maximum attainable light output according to previously published work. By taking the ±1σ bounds, the 3% statistical spread in the relative light output provides a good reference value of the minimum light-output non-uniformity observed for the CALIFA crystals. If the relative light output is estimated pointwise from a set of Tl concentration measurements a light-output non-uniformity of 4.6(2.4)% results. For a γ-ray energy of 662 keV the deduced variation in Tl concentration contributes with 0.48(6)% to the typical resolution of 7.74(6)% measured with a collimated source along the crystal main axis. The result is of interest for the characterization of the detector system performance and for realistic simulations of the light collection process in detector systems that are used for nuclear spectroscopy and calorimetry.

A wrapper‐based feature selection for improving performance of intrusion detection systems

SummaryAlong with expansion in using of Internet and computer networks, the privacy, integrity, and access to digital resources have been faced with permanent risks. Due to the unpredictable behavior of network, the nonlinear nature of intrusion attempts, and the vast number of features in the problem environment, intrusion detection system (IDS) is regarded as the main problem in the security of computer networks. A feature selection technique helps to reduce complexity in terms of both the executive load and the storage by selecting the optimal subset of features. The purpose of this study is to identify important and key features in building an IDS. To improve the performance of IDS, this paper proposes an IDS that its features are optimally selected using a new hybrid method based on fruit fly algorithm (FFA) and ant lion optimizer (ALO) algorithm. The simulation results on the dataset KDD Cup99, NSL‐KDD, and UNSW‐NB15 have shown that the FFA–ALO has an acceptable performance according to the evaluation criteria such as accuracy and sensitivity than previous approaches.

Proposed Hybrid Classifier to Improve Network Intrusion Detection System using Data Mining Techniques

Network intrusion detection system (NIDS) is a software system which plays an important role to protect network system and can be used to monitor network activities to detect different kinds of attacks from normal behavior in network traffics. A false alarm is one of the most identified problems in relation to the intrusion detection system which can be a limiting factor for the performance and accuracy of the intrusion detection system. The proposed system involves mining techniques at two sequential levels, which are: at the first level Naïve Bayes algorithm is used to detect abnormal activity from normal behavior. The second level is the multinomial logistic regression algorithm of which is used to classify abnormal activity into main four attack types in addition to a normal class. To evaluate the proposed system, the KDDCUP99 dataset of the intrusion detection system was used and K-fold cross-validation was performed. The experimental results show that the performance of the proposed system is improved with less false alarm rate.

Computer aided detection for laterally spreading tumors and sessile serrated adenomas during colonoscopy.

Evidence has shown that deep learning computer aided detection (CADe) system achieved high overall detection accuracy for polyp detection during colonoscopy. The detection performance of CADe system on non-polypoid laterally spreading tumors (LSTs) and sessile serrated adenomas/polyps (SSA/Ps), with higher risk for malignancy transformation and miss rate, has not been exclusively investigated. A previously validated deep learning CADe system for polyp detection was tested exclusively on LSTs and SSA/Ps. 1451 LST images from 184 patients were collected between July 2015 and January 2019, 82 SSA/Ps videos from 26 patients were collected between September 2018 and January 2019. The per-frame sensitivity and per-lesion sensitivity were calculated. (1) For LSTs image dataset, the system achieved an overall per-image sensitivity and per-lesion sensitivity of 94.07% (1365/1451) and 98.99% (197/199) respectively. The per-frame sensitivity for LST-G(H), LST-G(M), LST-NG(F), LST-NG(PD) was 93.97% (343/365), 98.72% (692/701), 85.71% (324/378) and 85.71% (6/7) respectively. The per-lesion sensitivity of each subgroup was 100.00% (71/71), 100.00% (64/64), 98.31% (58/59) and 80.00% (4/5). (2) For SSA/Ps video dataset, the system achieved an overall per-frame sensitivity and per-lesion sensitivity of 84.10% (15883/18885) and 100.00% (42/42), respectively. This study demonstrated that a local-feature-prioritized automatic CADe system could detect LSTs and SSA/Ps with high sensitivity. The per-frame sensitivity for non-granular LSTs and small SSA/Ps should be further improved.

Non-Classical Semiconductor Photon Sources Enhancing the Performance of Classical Target Detection Systems

We demonstrate and analyze how deploying non-classical intensity correlations obtained from a monolithic semiconductor quantum photon source can enhance classical target detection systems. This is demonstrated by examining the advantages offered by the utilization of the non-classical correlations in a correlation based target detection protocol. We experimentally demonstrate that under the same condition, the target contrast obtained from the protocol when non-classical correlations are utilized exhibits an improvement of up to 17.79dB over the best classical intensity correlation-based target detection protocol, under 29.69dB channel loss and excess noise 13.40dB stronger than the probe signal. We also assessed how the strong frequency correlations within the non-classical photon pairs can be used to further enhance this protocol.

Online Intrusion Scenario Discovery and Prediction Based on Hierarchical Temporal Memory (HTM)

With the development of intrusion detection, a number of the intelligence algorithms (e.g., artificial neural networks) are introduced to enhance the performance of the intrusion detection systems. However, many intelligence algorithms should be trained before being used, and retrained regularly, which is not applicable for continuous online learning and analyzing. In this paper, a new online intrusion scenario discovery framework is proposed and the intelligence algorithm HTM (Hierarchical Temporal Memory) is employed to improve the performance of the online learning ability of the system. The proposed framework can discover and model intrusion scenarios, and the constructed model keeps evolving with the variance of the data. Additionally, a series of data preprocessing methods are introduced to enhance its adaptability to the noisy and twisted data. The experimental results show that the framework is effective in intrusion scenario discovery, and the discovered scenario is more concise and accurate than our previous work.

The Research on autopilot system based on lightweight YOLO-V3 target detection algorithm

Computer vision is one of the most important branches in the field of deep learning, which includes image classification, target detection and so on. In this paper, the pruning algorithm is combined with the method of reducing the network depth, and the trained network model is applied to the field of autopilot, which solves the problems of high power consumption, single function and poor real-time performance of the vehicle target detection system. In this paper, through a large number of theoretical research and experiments, the appropriate activation function and regularization method are selected. Through the visualization method of neural network convolution layer, the working principle of the network is expounded in detail. Through theoretical analysis, the appropriate anchor box, is selected and the non-maximum suppression algorithm is introduced. Tensorboard data visualization system is used to monitor the distribution and law of network parameters. The accuracy of detection according to the experimental target is 98.7%.

A methodology for conducting efficient sanitization of HTTP training datasets

The performance of anomaly-based intrusion detection systems depends on the quality of the datasets used to form normal activity profiles. Suitable datasets should include high volumes of real-life data free from attack instances. On account of this requirement, obtaining quality datasets from collected data requires a process of data sanitization that may be prohibitive if done manually, or uncertain if fully automated.In this work, we propose a sanitization approach for obtaining datasets from HTTP traces suited for training, testing, or validating anomaly-based attack detectors. Our methodology has two sequential phases. In the first phase, we clean known attacks from data using a pattern-based approach that relies on tools that detect URI-based known attacks. In the second phase, we complement the result of the first phase by conducting assisted manual labeling systematically and efficiently, setting the focus of expert examination not on the raw data (which would be millions of URIs), but on the set of words that compose the URIs. This dramatically downsizes the volume of data that requires expert discernment, making manual sanitization of large datasets feasible. We have applied our method to sanitize a trace that includes 45 million requests received by the library web server of the University of Seville. We were able to generate clean datasets in less than 84 h with only 33 h of manual supervision. We have also applied our method to some public benchmark datasets, confirming that attacks unnoticed by signature-based detectors can be discovered in a reduced time span.

Event classification using improved salp swarm algorithm based probabilistic neural network in fiber-optic perimeter intrusion detection system

Abstract To remarkably upgrade the performance of the intrusion detection system based on single-mode—multimode—single-mode (SMS) fiber structure which can effectively discriminate man-made event and natural event in zone perimeter, a novel event classification approach is presented in this paper. Firstly, the original intrusion signal is decomposed by the filter bank into four frequency channels with an interval of 11745 Hz. Then, the singular value and kurtosis extracted from each frequency channel as combinations of eigenvectors, being transferred to the probabilistic neural network (PNN) for training and classification. Finally, the weight factor and adaptive mutation operator are embedded in the salp swarm algorithm (SSA), which is utilized to optimize and search for the optimal smoothing factor of the PNN classifier and compared with the other four metaheuristic (MH) algorithms under five algorithm performance evaluation metrics. Large practical experiments exhibit that the presented scheme can accurately recognize man-made events (knocking, rattling) and natural events (wind, rain) respectively, shedding light on the feasibility and applicability of SMS fiber structure in zone perimeter.

Theoretical and experimental investigation on the performance of heterodyne detection system affected by the beam mode of partially coherent beams in atmospheric turbulence

The mathematical model of the effect of beam mode (It contains fundamental mode and other higher orders mode) on the heterodyne efficiency of heterodyne detection system was established, and the effect of beam mode on the performance of heterodyne detection system was theoretically investigated for partially coherent GSM beam in slant atmospheric turbulence conditions. The results show that the inner scale of turbulence has a greater effect on the heterodyne efficiency and normalized M2. (It is used to evaluate beam quality.) The larger the mode orders are, the lower the proportion of the fundamental mode and heterodyne efficiency are. Finally, the changes of beam mode caused by turbulence was obtained by a heterodyne detection system in an experiment investigating optical communication at a distance of 1.3 km. The results of this experiment verified that the mode orders are increased by turbulence effect and the beam mode increases with an increase in the turbulence intensity, resulting in a decrease in the corresponding the peak-to-peak value of the IF signal.

Detection and recognition of a ground moving target under random dynamic conditions

AbstractThe infrared radiation characteristic of ground moving targets under random dynamic conditions is an important research hotspot to study for the detection performance of the airborne photoelectric detection system. This paper studies the detection mechanism of the airborne photoelectric detection system to a ground moving target and analyzes the relevant factors that affect the infrared radiation characteristics of ground moving targets. Based on spectral radiation theory, establishes the infrared radiation illumination function of a ground moving target under a complex background environment, combines the rotation relationship matrix between ground moving target and airborne photoelectric detection system, and derives the new calculation model of the signal‐to‐noise ratio under the Fraunhofer diffraction effect. The experimental results showed that the infrared radiation characteristics of the ground moving target are affected by detection time and photoelectric detector's wavelength, which verifies the spatial position relationship between the ground moving target and the airborne photoelectric detection system also determines the size of the output signal of the photoelectric detector. To effectively improve the infrared radiation characteristics of the ground moving target, the appropriate wavelength of the photoelectric detector changes the flight attitude and velocity of the carrier aircraft, which can improve the infrared radiation characteristics of ground the moving target in the detection process.

Convolutional neural network model based on terahertz imaging for integrated circuit defect detections.

Detection of integrated circuit (IC) defects is vital in IC manufacturing. Traditional defect detection methods have relied on scanning electron microscopy and X-ray imaging techniques that are time consuming and destructive. Hence, in this paper we considered terahertz imaging as a label-free and nondestructive alternative. This study aimed to use a convolutional neural network model (CNN) to improve the performance of the terahertz imaging IC detection system. First, we constructed a terahertz imaging IC dataset and analyzed it. Subsequently, a new CNN structure was proposed based on the VGG16 network. Finally, it was optimized based on its structure and dropout rate. The method proposed above can improve IC defects detection accuracy of THz imaging. Most significantly, this work will promote the application of terahertz imaging in practice and provide a foundation to further research in relevant fields.

Optical modulation in Ge-rich SiGe waveguides in the mid-infrared wavelength range up to 11 \xb5m

Waveguide integrated optical modulators in the mid-infrared wavelength range are of significant interest for molecular spectroscopy. This is because on-chip synchronous detection can improve the performance of detection systems and can also be used for free-space communications where optical modulators working in atmospheric transparency windows are needed. Here we report optical modulation in a mid-infrared photonic circuit, reaching wavelengths larger than 8 µm. Optical modulation in the wavelength range from 5.5 to 11 µm is shown, relying on a broadband Ge-rich graded-SiGe platform. This demonstration experimentally confirms the free-carrier absorption effect modeling. These results pave the way towards efficient high-performance electrically-driven integrated optical modulators in the mid-infrared wavelength range.

60 GHz Low-Noise Amplifier for Detection Systems

This paper presents a 60 GHz low-noise amplifier (LNA) to improve detection systems performance. The considered system is based on impulse radar technology. The LNA is designed in GaAs metamorphic High Electron Mobility Transistor (m-HEMT) technology. Three stages common source transistors are used with inductive degeneration for a good tradeoff between gain and noise. The post-layout simulation results show a noise factor of 2.06 dB and a gain of 14.5 dB at 60.2 GHz, for a power consumption of 13.5 mW. The simulated nonlinear characteristics show an IP 1dB (Input 1 dB compression Point) of -10 dBm and an IIP3 (Input third-order Intercept Point) of -4.4 dBm. The LNA occupies an area of 1.56 × 1.29 mm2.

Determination of structural and damage detection system influencing parameters on the value of information

A method to determine the influencing parameters of a structural and damage detection system is proposed based on the value of information analysis. The value of information analysis utilizes the Bayesian pre-posterior decision theory to quantify the value of damage detection system for the structural integrity management during service life. First, the influencing parameters of the structural system, such as deterioration type and rate are introduced for the performance of the prior probabilistic system model. Then the influencing parameters on the damage detection system performance, including number of sensors, sensor locations, measurement noise, and the Type-I error are investigated. The pre-posterior probabilistic model is computed utilizing the Bayes’ theorem to update the prior system model with the damage indication information. Finally, the value of damage detection system is quantified as the difference between the maximum utility obtained in pre-posterior and prior analysis based on the decision tree analysis, comprising structural probabilistic models, consequences, as well as benefit and costs analysis associated with and without monitoring. With the developed approach, a case study on a statically determinate Pratt truss bridge girder is carried out to validate the method. The analysis shows that the deterioration rate is the most sensitive parameter on the effect of relative value of information over the whole service life. Furthermore, it shows that more sensors do not necessarily lead to a higher relative value of information; only specific sensor locations near the highest utilized components lead to a high relative value of information; measurement noise and the Type-I error should be controlled and be as small as possible. An optimal sensor employment with highest relative value of information is found. Moreover, it is found that the proposed method can be a powerful tool to develop optimal service life maintenance strategies—before implementation—for similar bridges and to optimize the damage detection system settings and sensor configuration for minimum expected costs and risks.

An in-Process Inspection System to Detect Noise Originating from within the Interior Trim Panels of Car Doors.

Car body parts are sometimes responsible for irritating noise caused by assembly defects. Typically, various types of noise are known to originate from within the interior trim panels of car doors. This noise is considered to be an important factor that degrades the emotional satisfaction of the driver of the car. This research suggests an in-process inspection system consisting of an inspection workstation and a noise detection method. The inspection workstation presses down the car door trim panel by using a pneumatic pusher while microphones record the acoustic signals directly above the door trim panel and on the four sides of the workstation. The collected signals are analyzed by the proposed noise detection method after applying noise reduction. The noise detection method determines the presence of irritating noise by using noise source localization in combination with the time difference of arrival method and the relative signal strengths. The performance of the in-process noise detection system was evaluated by conducting experiments on faulty and healthy car door trim panels.

Highly Sensitive Detection of Amaranth Realized with Upconversion Nanoparticles-Based Solid Sensor

High performance of synthetic colorants detection system has great importance for food safety, yet its quantitative analysis with high sensitivity remains challenging. Herein, core–shell NaGdF4: Yb3+, Er3+@NaYF4 upconversion nanoparticles based solid sensor (UCNPs-SS) is designed and explored in this work, where the detection limit of 0.01 μg mL−1 can be achieved thanks to the effective energy transfer between amaranth and this fluorescence-based sensor. Besides, ratiometric detection technique is employed in this system to ensure the accuracy of the detection, hence, insignificant change of the upconversion luminescence intensity of this UCNPs-SS is observed when the potentially interfering substances are present. Moreover, this UCNPs-SS exhibits good stability for recycled detections. Hence, the precise amaranth detection of three commercial soft drinks and excellent recoveries are achieved via our core–shell UCNPs-SS in this work. These positive results indicate that this proposed fluorescence detect approach not only exhibits excellent sensitivity, but also provides feasibility for practical applications.

The detection of lead ion level in distilled water using 405 nm laser light and optical probe based on the Directional coupler

The development of lead ion detection systems is expected to have an advantage in terms of simplicity of the device and easy to trace the of level lead ion in the water. The level of lead ion in water that is allowed into the human body is 0.01 ppm. In this work, an alternative methode to detect the concentration of lead ion in water using 405 nm laser light and optical probe based on the directional coupler is proposed. The proposed methode to detect the concentration of lead ion is using directional coupler and flat mirror as a reflecting component. The performance of this new detection system that was developed will be shows: (i) sensitivity; (ii) linearity; (iii) operation range; and (iv) the limit of detection (LOD). To shows this performance, the system was tried to detect the concentration of lead ion in distilled water from 0.01 ppm to 0.07 ppm with four replication for each concentration. The performance of this alternative methode to detect lead ion has a 26.78 mV/ppm of sensitivity of with a linearity more than 99%, operation range operation range start from 0.01 ppm to 0.07 ppm, and LOD reaches 0.01 ppm.