A Novel Intrusion Detection Model of Unknown Attacks Using Convolutional Neural Networks

With the increasing number of connected devices in the Internet of Things(IoT)era,the number of intrusions is also increasing.An intrusion detection system(IDS)is a secondary intelligent system for monitoring,detecting and alerting against malicious activity.IDS is important in developing advanced security models.This study reviews the importance of various techniques,tools,and methods used in IoT detection and/or prevention systems.Specifically,it focuses on machine learning(ML)and deep learning(DL)techniques for IDS.This paper proposes an accurate intrusion detection model to detect traditional and new attacks on the Internet of Vehicles.To speed up the detection of recent attacks,the proposed network architecture developed at the data processing layer is incorporated with a convolutional neural network(CNN),which performs better than a support vector machine(SVM).Processing data are enhanced using the synthetic minority oversampling technique to ensure learning accuracy.The nearest class mean classifier is applied during the testing phase to identify new attacks.Experimental results using the AWID dataset,which is one of the most common open intrusion detection datasets,revealed a higher detection accuracy(94%)compared to SVM and random forest methods.

Posture Detection of Heart Disease Using Multi-Head Attention Vision Hybrid(MHAVH)Model

Cardiovascular disease is the leading cause of death globally.This disease causes loss of heart muscles and is also responsible for the death of heart cells,sometimes damaging their functionality.A person’s life may depend on receiving timely assistance as soon as possible.Thus,minimizing the death ratio can be achieved by early detection of heart attack(HA)symptoms.In the United States alone,an estimated 610,000 people die fromheart attacks each year,accounting for one in every four fatalities.However,by identifying and reporting heart attack symptoms early on,it is possible to reduce damage and save many lives significantly.Our objective is to devise an algorithm aimed at helping individuals,particularly elderly individuals living independently,to safeguard their lives.To address these challenges,we employ deep learning techniques.We have utilized a vision transformer(ViT)to address this problem.However,it has a significant overhead cost due to its memory consumption and computational complexity because of scaling dot-product attention.Also,since transformer performance typically relies on large-scale or adequate data,adapting ViT for smaller datasets is more challenging.In response,we propose a three-in-one steam model,theMulti-Head Attention Vision Hybrid(MHAVH).Thismodel integrates a real-time posture recognition framework to identify chest pain postures indicative of heart attacks using transfer learning techniques,such as ResNet-50 and VGG-16,renowned for their robust feature extraction capabilities.By incorporatingmultiple heads into the vision transformer to generate additional metrics and enhance heart-detection capabilities,we leverage a 2019 posture-based dataset comprising RGB images,a novel creation by the author that marks the first dataset tailored for posture-based heart attack detection.Given the limited online data availability,we segmented this dataset into gender categories(male and female)and conducted testing on both segmented and original datasets.The training accuracy of our model reached an impressive 99.77%.Upon testing,the accuracy for male and female datasets was recorded at 92.87%and 75.47%,respectively.The combined dataset accuracy is 93.96%,showcasing a commendable performance overall.Our proposed approach demonstrates versatility in accommodating small and large datasets,offering promising prospects for real-world applications.

Portable FBAR based E-nose for cold chain real-time bananas shelf time detection

Being cheap,nondestructive,and easy to use,gas sensors play important roles in the food industry.However,most gas sensors are suitable more for laboratory-quality fast testing rather than for cold-chain continuous and cumulative testing.Also,an ideal electronic nose(E-nose)in a cold chain should be stable to its surroundings and remain highly accurate and portable.In this work,a portable film bulk acoustic resonator(FBAR)-based E-nose was built for real-time measurement of banana shelf time.The sensor chamber to contain the portable circuit of the E-nose is as small as a smartphone,and by introducing an air-tight FBAR as a reference,the E-nose can avoid most of the drift caused by surroundings.With the help of porous layer by layer(LBL)coating of the FBAR,the sensitivity of the E-nose is 5 ppm to ethylene and 0.5 ppm to isoamyl acetate and isoamyl butyrate,while the detection range is large enough to cover a relative humidity of 0.8.In this regard,the E-nose can easily discriminate between yellow bananas with green necks and entirely yellow bananas while allowing the bananas to maintain their biological activities in their normal storage state,thereby showing the possibility of real-time shelf time detection.This portable FBAR-based E-nose has a large testing scale,high sensitivity,good humidity tolerance,and low frequency drift to its surroundings,thereby meeting the needs of cold-chain usage.

Comparison of liquid-liquid extraction-thin layer chromatography with solid-phase extraction-high-performance thin layer chromatography in detection of urinary morphine

Liquid-liquid extraction-thin layer chromatography （LLE-TLC） has been a common and routine combined method for detection of drugs in biological materials. Solid-phase extraction （SPE） is gradually replacing the tra- ditional LLE method. High performance thin layer chromatography （HPTLC） has several advantages over TLC. The present work studied the higher efficiency of a new SPE-HPTLC method over that of a routine LLE-TLC method, in extraction and detection of urinary morphine. Fifty-eight urine samples, primarily identified as mor- phine-positive samples by a strip test, ＇were re-screened by LLE-TLC and SPE-HPTLC. The results of LLE-TLC and SPE-HPTLC were then compared with each other. The results showed that the SPE-HPTLC detected 74% of total samples as morphine-positive samples whereas the LLE-TLC detected 48% of the same samples. We further discussed the effect of codeine abuse on TLC analysis of urinary morphine. Regarding the importance of morphine detection in urine, the present combined SPE-HPTLC method is suggested as a replacement method for detection of urinary morphine by many reference laboratories.

Highly sensitive and stable SERS probes of alternately deposited Ag and Au layers on 3D SiO2 nanogrids for detection of trace mercury ions

The hazard of Hg ion pollution triggers the motivation to explore a fast, sensitive, and reliable detection method. Here, we design and fabricate novel 36-nm-thick Ag-Au composite layers alternately deposited on three-dimensional （3D） periodic SiO2 nanogrids as surface-enhanced Raman scattering （SERS） probes. The SERS effects of the probes depend mainly on the positions and intensities of their localized surface plasmon resonance （LSPR） peaks, which is confirmed by the absorption spectra from finite-difference time-domain （FDTD） calculations. By optimizing the structure and material to maximize the intrinsic electric field enhancement based on the design method of 3D periodic SERS probes proposed, high performance of the Ag-Au/SiO2 nanogrid probes is achieved with the stability further enhanced by annealing. The optimized probes show the outstanding stability with only 4.0% SERS intensity change during 10-day storage, the excellent detection uniformity of 5.78% （RSD）, the detection limit of 5.0 × 10-12 M （1 ppt）, and superior selectivity for Hg ions. The present study renders it possible to realize the rapid and reliable detection of trace heavy metal ions by developing high- performance 3D periodic structure SERS probes by designing novel 3D structure and optimizing plasmonic material.

Role of Cross Layer Based Intrusion Detection System for Wireless Domain

Wireless mesh networks are very common both for organizations and individuals. Many laptops, computers have wireless cards pre-installed for buyer. However a wireless networking has many security issues. An intrusions detection system aim to detect the different attacks against network and system. An intrusion detection system should be capable for detecting the misuse of the network whether it will be by the authenticated user or by an attacker. They detect attempts and active misuse either by legitimate users of the information systems or by external. The present paper deals with cross layer based intrusion detection system for wireless domain—a critical anlaysis. The present paper deals with role of cross layer based intrusion detection system for wireless domain.

The Behaviour of Vertical Bell Laboratories Layered Space-Time Algorithm Combined with Multiuser Detection Schemes in Wireless Communication System

This paper provides the performance analysis of multiuser Vertical Bell Laboratories Layered Space-Time (V-BLAST) system receiver structures for Multiple-input Multiple-Output (MIMO) channel at a base station with assumption of perfect channel estimation and perfect timing delay estimation. In MIMO channels the receivers such as decorrelator, Minimum Mean Square Error (MMSE) and Multistage Parallel Interference Cancellation (MPIC) receiver outperform the conventional receiver. Withal, since the multiple antenna interference led to a strong impact on the performance degradation of a multistage interference cancellation receiver, the performance of MPIC receiver was highly degraded based on system loading.

A VERTICAL LAYERED SPACE-TIME CODE AND ITS CLOSED-FORM BLIND SYMBOL DETECTION

Vertical layered space-time codes have demonstrated the enormous potential to accommodate rapid flow data. Thus far, vertical layered space-time codes assumed that perfect estimates of current channel fading conditions are available at the receiver. However, increasing the number of transmit antennas increases the required training interval and reduces the available time in which data may be transmitted before the fading coefficients change. In this paper, a vertical layered space-time code is proposed. By applying the subspace method to the layered space-time code, the symbols can be detected without training symbols and channel estimates at the transmitter or the receiver. Monte Carlo simulations show that performance can approach that of the detection method with the knowledge of the channel.

Metal Nano Layer Coating for Improving the Detection and Recognition of Micro-Poisons Using Reflection Spectroscopic Measurement

Metal nano layer coating for increasing the sensitivity of spectroscopic measurements is proposed and experimentally demonstrated in this paper. The metal nano layer will attract the micro-poisons from any measured aqueous sample increasing the concentration of the micro-poison in the vicinity of the surface and significantly improves the sensitivity of the spectroscopic measurement. The demonstration was carried out using Fourier Transform Infra-Red (FTIR) operating in the MIR 400 cm-1 - 4000 cm-1 and 5 nm Gold layer which was grown on silicon oxide substrate. In the experimental demonstration Malathion organophosphate pesticide was used as micro-poison. The spectroscopic measurement proves that Malathion was attracted to the metal nano layer. Furthermore, the absorption lines of Malathion were detected and recognized. This proof of principle can be applied to any Internal Reflection Elements (IRE) and it can be used to purify any aqueous solutions and atmosphere from micro-poisons which will be attracted to the metal Nano layer.

Intelligent Detection Method of Substation Environmental Targets Based on MD-Yolov7

The complex operating environment in substations, with different safety distances for live equipment, is a typical high-risk working area, and it is crucial to accurately identify the type of live equipment during automated operations. This paper investigates the detection of live equipment under complex backgrounds and noise disturbances, designs a method for expanding lightweight disturbance data by fitting Gaussian stretched positional information with recurrent neural networks and iterative optimization, and proposes an intelligent detection method for MD-Yolov7 substation environmental targets based on fused multilayer feature fusion (MLFF) and detection transformer (DETR). Subsequently, to verify the performance of the proposed method, an experimental test platform was built to carry out performance validation experiments. The results show that the proposed method has significantly improved the performance of the detection accuracy of live devices compared to the pairwise comparison algorithm, with an average mean accuracy (mAP) of 99.2%, which verifies the feasibility and accuracy of the proposed method and has a high application value.

Atomic-Scale Layer-by-Layer Deposition of Fe SiAl@ZnO@Al_(2)O_(3) Hybrid with Threshold Anti-Corrosion and Ultra-High Microwave Absorption Properties in Low-Frequency Bands

Developing highly efficient magnetic microwave absorb-ers(MAs)is crucial,and yet challenging for anti-corrosion properties in extremely humid and salt-induced foggy environments.Herein,a dual-oxide shell of ZnO/Al_(2)O_(3) as a robust barrier to FeSiAl core is introduced to mitigate corrosion resistance.The FeSiAl@ZnO@Al_(2)O_(3) layer by layer hybrid structure is realized with atomic-scale precision through the atomic layer deposition technique.Owing to the unique hybrid structure,the FeSiAl@ZnO@Al_(2)O_(3) exhibits record-high micro-wave absorbing performance in low-frequency bands covering L and S bands with a minimum reflection loss(RLmin)of-50.6 dB at 3.4 GHz.Compared with pure FeSiAl(RLmin of-13.5 dB,a bandwidth of 0.5 GHz),the RLmin value and effective bandwidth of this designed novel absorber increased up to~3.7 and~3 times,respectively.Fur-thermore,the inert ceramic dual-shells have improved 9.0 times the anti-corrosion property of FeSiAl core by multistage barriers towards corrosive medium and obstruction of the electric circuit.This is attributed to the large charge transfer resistance,increased impedance modulus|Z|0.01 Hz,and frequency time constant of FeSiAl@ZnO@Al_(2)O_(3).The research demonstrates a promising platform toward the design of next-generation MAs with improved anti-corrosion properties.

Simultaneous Multi-vehicle Detection and Tracking Framework with Pavement Constraints Based on Machine Learning and Particle Filter Algorithm

Due to the large variations of environment with ever-changing background and vehicles with different shapes, colors and appearances, to implement a real-time on-board vehicle recognition system with high adaptability, efficiency and robustness in complicated environments, remains challenging. This paper introduces a simultaneous detection and tracking framework for robust on-board vehicle recognition based on monocular vision technology. The framework utilizes a novel layered machine learning and particle filter to build a multi-vehicle detection and tracking system. In the vehicle detection stage, a layered machine learning method is presented, which combines coarse-search and fine-search to obtain the target using the AdaBoost-based training algorithm. The pavement segmentation method based on characteristic similarity is proposed to estimate the most likely pavement area. Efficiency and accuracy are enhanced by restricting vehicle detection within the downsized area of pavement. In vehicle tracking stage, a multi-objective tracking algorithm based on target state management and particle filter is proposed. The proposed system is evaluated by roadway video captured in a variety of traffics, illumination, and weather conditions. The evaluating results show that, under conditions of proper illumination and clear vehicle appearance, the proposed system achieves 91.2% detection rate and 2.6% false detection rate. Experiments compared to typical algorithms show that, the presented algorithm reduces the false detection rate nearly by half at the cost of decreasing 2.7%–8.6% detection rate. This paper proposes a multi-vehicle detection and tracking system, which is promising for implementation in an on-board vehicle recognition system with high precision, strong robustness and low computational cost.

An Efficient Impersonation Attack Detection Method in Fog Computing

Fog computing paradigm extends computing,communication,storage,and network resources to the network’s edge.As the fog layer is located between cloud and end-users,it can provide more convenience and timely services to end-users.However,in fog computing(FC),attackers can behave as real fog nodes or end-users to provide malicious services in the network.The attacker acts as an impersonator to impersonate other legitimate users.Therefore,in this work,we present a detection technique to secure the FC environment.First,we model a physical layer key generation based on wireless channel characteristics.To generate the secret keys between the legitimate users and avoid impersonators,we then consider a Double Sarsa technique to identify the impersonators at the receiver end.We compare our proposed Double Sarsa technique with the other two methods to validate our work,i.e.,Sarsa and Q-learning.The simulation results demonstrate that the method based on Double Sarsa outperforms Sarsa and Q-learning approaches in terms of false alarm rate(FAR),miss detection rate(MDR),and average error rate(AER).

Selfish behavior detection of cooperative relay

A selfish behavior detection technique is investigated to assist secure cooperative trans- mission at the physical layer. The detection technique calculates the correlation ratio between signals received from the diversity branches to determine the relay＇ s behavior in amplify and forward coop- eration strategy. The correlation ratio is considerably reduced because the relay＇ s selfish behavior makes the correlation between the received signals in the diversity branch degraded. Simulation re- sults show that the proposed mechanism can effectively detect selfish nodes and performance will be improved significantly with the destination＇ s detection technique.

Anti-Corrosion and Reconstruction of Surface Crystal Plane for Zn Anodes by an Advanced Metal Passivation Technique

For the aqueous Zn-ion battery,dendrite formation,corrosion,and interfacial parasitic reactions are major issues,which greatly inhibits their practical application.How to develop a method of Zn construction or treatment to solve these issues for Zn anodes are still great challenges.Herein,a simple and cheap metal passivation technique is proposed for Zn anodes from a corrosion science perspective.Similar to the metal anticorrosion engineering,the formed interfacial protective layer in a chemical way can sufficiently solve the corrosion issues.Furthermore,the proposed passivity approach can reconstruct Zn surface-preferred crystal planes,exposing more(002)planes and improving surface hydrophilicity,which inhibits the formation of Zn dendrites and hydrogen evolution effectively.As expected,the passivated Zn achieves outstanding cycling life(1914 h)with low voltage polarization(<40 mV).Even at 6 mA cm^(−2) and 3 mA h cm^(−2),it can achieve stable Zn deposition over 460 h.The treated Zn anode coupled with MnO_(2) cathode shows prominently reinforced full batteries service life,making it a potential Zn anode candidate for excellent performance aqueous Zn-ion batteries.The proposed passivation approach provides a guideline for other metal electrodes preparation in various batteries and establishes the connections between corrosion science and batteries.

Behavioral Feature and Correlative Detection of Multiple Types of Node in the Internet of Vehicles

Undoubtedly,uncooperative or malicious nodes threaten the safety of Internet of Vehicles(IoV)by destroying routing or data.To this end,some researchers have designed some node detection mechanisms and trust calculating algorithms based on some different feature parameters of IoV such as communication,data,energy,etc.,to detect and evaluate vehicle nodes.However,it is difficult to effectively assess the trust level of a vehicle node only by message forwarding,data consistency,and energy sufficiency.In order to resolve these problems,a novel mechanism and a new trust calculating model is proposed in this paper.First,the four tuple method is adopted,to qualitatively describing various types of nodes of IoV;Second,analyzing the behavioral features and correlation of various nodes based on route forwarding rate,data forwarding rate and physical location;third,designing double layer detection feature parameters with the ability to detect uncooperative nodes and malicious nodes;fourth,establishing a node correlative detection model with a double layer structure by combining the network layer and the perception layer.Accordingly,we conducted simulation experiments to verify the accuracy and time of this detection method under different speed-rate topological conditions of IoV.The results show that comparing with methods which only considers energy or communication parameters,the method proposed in this paper has obvious advantages in the detection of uncooperative and malicious nodes of IoV;especially,with the double detection feature parameters and node correlative detection model combined,detection accuracy is effectively improved,and the calculation time of node detection is largely reduced.

Symbols detection for frequency-selective V-BLAST OFDM systems

As the combining form of the orthogonal frequency-division multiplexing (OFDM) technique and the vertical Bell Labs layered space-time (V-BLAST) architecture, the V-BLAST OFDM system can better meet the demand of next-generation (NextG) broadband mobile wireless multimedia communications. The symbols detection problem of the V-BLAST OFDM system is investigated under the frequency-selective fading environment. The joint space-frequency demultiplexing operation is proposed in the V-BLAST OFDM system. Successively, one novel half-rate rotational invariance joint space-frequency coding scheme for the V-BLAST OFDM system is proposed. By elegantly exploiting the above rotational invariance property, we derive one direct symbols detection scheme without knowing channels state information (CSI) for the frequency-selective V-BLAST OFDM system. Extensive simulation results demonstrate the validity of the novel half-rate rotational invariance joint space-frequency coding scheme and the performance of the direct symbols detection scheme.

Application of Ground Penitrating Radar Method in Pipe Laying Detection

Ground Penetrating Radar method was used in detecting the flaws of underground pipeline. The GPR layer disturbing image was summarized by using a rational method in fieldwork and the in-door interpretation of data. The mark radar images of disturbance of slight, middle, and strong were obtained. The result shows that the radar method can not only determine the position of the concrete pipeline underground, but it can detect the laying quality of pipeline as well.

BLIND MULTIUSER DETECTION FOR V-BLAST CODED MULTICARRIER CDMA SYSTEM

Focusing on the space-time coded multiuser mobile communication systems in the frequency-selective fading environment, this paper proposes a Vertical Bell labs LAyered Space-Time (V-BLAST) coded Multicarrier Code-Division Multiple-Access (MC-CDMA) scheme and its blind channel identification algorithm. This algorithm employs an ESPRIT-like method and the singular value decomposition, and the channels between every transmit antenna of every user and every receive antenna of the base station are blindly estimated with a closed-form solution. Based on it, an equivalent Minimum Mean-Squared Error (MMSE) time-domain multiuser detector is derived. Moreover, the proposed scheme exploits the precoding in the transmitter in order to eliminate the constraint of more receive antennas than transmit ones, required by most conventional V-BLAST codec schemes. Computer simulation results demonstrate the validity of this proposed scheme.

Contour Detection-Based Realistic Finite-Difference-Time-Domain Models for Microwave Breast Cancer Detection

In this paper, a collection of three-dimensional(3D)numerical breast models are developed based on clinical magnetic resonance images(MRIs). A hybrid contour detection method is used to create the contour, and the internal space is filled with different breast tissues, with each corresponding to a specified interval of MRI pixel intensity. The developed models anatomically describe the complex tissue structure and dielectric properties in breasts. Besides, they are compatible with finite-difference-time-domain(FDTD)grid cells. Convolutional perfect matched layer(CPML)is applied in conjunction with FDTD to simulate the open boundary outside the model. In the test phase, microwave breast cancer detection simulations are performed in four models with varying radiographic densities. Then, confocal algorithm is utilized to reconstruct the tumor images. Imaging results show that the tumor voxels can be recognized in every case, with 2 mm location error in two low density cases and 7 mm─8 mm location errors in two high density cases, demonstrating that the MRI-derived models can characterize the individual difference between patients' breasts.