Packet Analysis Research Articles

Early Age Concrete Strength Monitoring Using Power Spectral Density and Wavelet Packet Analysis

Concrete is a complex building material. Under normal curing conditions, concrete strength shows a nonlinear development process at an early age (1∼28 d). In the first few days after the completion of pouring, the strength of concrete increases slowly. Subsequently, the strength of concrete increases rapidly, reaching about 90% of its age strength. Finally, its strength gradually stabilizes. This paper introduces the experiment of 28‐day concrete age strength monitoring based on embedded piezoelectric smart aggregate (SA). Two piezoelectric SAs were embedded in a concrete‐filled GFRP (glass fiber reinforced polymer) tube column, one of which emitted a sinusoidal sweep signal and the other SA received the signal. With the hydration reaction of concrete, the stress wave would be significantly different when passing through concrete, and the received signal is changing constantly. Through power spectral density and wavelet packet energy analysis, the monitoring signal of concrete age within 28 days was analyzed. The experimental results show that the wavelet packet energy and power spectral density of the sensor monitoring signal show a nonlinear growth trend with time during the concrete target age. It can be divided into three stages, and the fifth day and the fourteenth day are the demarcation point of energy growth. And the trend of energy change corresponds well with the change of actual concrete strength and age. Comparing and analyzing the received signal energy of the sensor and the power spectral density function of the stress wave signal of the concrete specimen, the trend of the amplitude in the natural frequency domain is found to be the same in the three stages.

LooCipher Ransomware Detection Using Lightweight Packet Characteristics

Abstract Ransomware activities have been rising steadily. The network traffic characteristics in a network packet analysis are available immediately to explore anomalies and find any offensive behaviors. This paper applies a lightweight ICEAP (Identify-Collect-Examine-Analyze-Present) approach for effectively identifying LooCipher ransomware activities instead of establishing complex systems or creating various programs. This proposed approach tracks online behaviors and understands the source/destination entities. With this innovative detection method, analysts can merge the eigenvalues into security mechanisms, uncover network threats by analyzing the full payload, and detect infected ransomware in a minimum effort.

Frequency Spectrum and Wavelet Packet Analyses of Blasting Vibration Signals for Different Charge Structures in Blasting Peripheral Holes

Based on the blasting principle of the cutting seam cartridge, smooth blasting with the charge structures of the usual cartridge and cutting seam cartridge has been designed and implemented, respectively, for different peripheral holes in the same face. Meanwhile, the blasting vibration has been monitored. Through the analysis of the frequency spectrum of blasting vibration signals, it is found that the maximum blasting vibration velocity of the cutting seam cartridge charge is lower than that of the usual cartridge charge, from 0.21 m/s to 0.12 m/s. Moreover, the blasting energy distribution is more balanced. Especially in the low‐frequency part, the blasting energy is less, and there is a transferring trend to the high‐frequency part, which shows that the cutting seam cartridge charge has a better optimization effect. Furthermore, using wavelet packet analysis, the cutting seam cartridge charge could effectively reduce the energy concentration in the low‐frequency part. The energy distribution is much more dispersed, and the disturbance to the structure could be less, which is conducive to the stability of the structure. According to the blasting effect, the overbreak and underexcavation quantity at the cutting seam cartridge charge is better than that at the usual cartridge charge.

Debonding Detection of Reinforced Concrete (RC) Beam with Near-Surface Mounted (NSM) Pre-stressed Carbon Fiber Reinforced Polymer (CFRP) Plates Using Embedded Piezoceramic Smart Aggregates (SAs)

The application of reinforced concrete (RC) beam with near-surface mounted (NSM) pre-stressed carbon fiber reinforced polymer (CFRP) plates has been increasingly widespread in civil engineering. However, debonding failure occurs easily in the early loading stage because of the prestress change at the end of CFRP plate. Therefore, it is important to find reliable, convenient and economical technical means to closely monitor the secure bonding between CFRP and concrete. In this paper, an active sensing approach for generating and sensing stress wave by embedded smart aggregates (SAs) is proposed, which provides a guarantee for the secure connection between CFRP and concrete. Two specimens with different non-pre-stressed bond lengths were fabricated in the laboratory. Six SAs were installed at different positions of the structure to monitor the degree of debonding damage during the loading process. The experiments showed that the optimal length of non-pre-stressed CFRP bond section (300 mm) can significantly improve the load characteristics and enhance the service performance of the structure. The theoretical analysis of wavelet packet shows that increasing the length of non-pre-stressed CFRP bond section can slow down the occurrence and propagation of debonding cracks. The debonding crack in the tension end region is earlier than that in the bond end region. The research results reflect that the developed approach can monitor the damage process caused by debonding cracks and provide early warning for the initial damage and the debonding failure.

Evaluation of the acoustic emission monitoring method for stress corrosion cracking on aboveground storage tank floor steel

Stress corrosion cracking (SCC), which is one of the main threats to the structural integrity of aboveground storage tanks (ASTs), has recently attracted much attention. In this paper, the SCC of the storage tank floor was studied by the acoustic emission (AE) and electrochemical methods. Throughout the whole SCC process, the AE signal characteristic parameters, cumulating features, and correlations were analyzed, and the signal time-frequency local features were extracted via wavelet packet analysis based on the MATLAB program. A good correlation of the instantaneous variations between the AE activity and the corrosion behavior was found. Microcrack creeping and macrocrack propagation were the most important AE sources in SCC; these sources can be recognized by the AE parameters and the AE frequency spectrum. The results in this paper will be beneficial for the diagnosis of corrosion severity and the recognition of the corrosion sources by AE online inspection and will further provide an assessment of the AST integrity by means of a risk-based inspection technique.

A Multiple Leaks’ Localization Method in a Pipeline Based on Change in the Sound Velocity

This article considers a multiple leaks' localization method in fluid pipelines based on the change in the sound velocity of an ultrasonic signal. First, the sound velocity of an ultrasonic signal is denoised by wavelet packet analysis (WPA). Second, the mathematical relationship between the position of a leak and the change in the sound velocity of an ultrasonic signal at the ends of a pipeline is established based on the motion equation and the continuity equation in the transient flow equation of a pipeline. As a consequence, the localization of the leakage position of the proposed method depends only on the change in the sound velocity of the ultrasonic signal at the ends of the pipeline rather than the time difference in a negative pressure wave arriving at the ends of the pipeline. Finally, the change in the sound velocity of the ultrasonic signal is used and it is proven to be efficient to locate the actual leak number. The simulation results show that the proposed method can locate multiple leak positions in a pipeline.

Binary neutron stars gravitational wave detection based on wavelet packet analysis and convolutional neural networks

This work investigates the detection of binary neutron stars gravitational wave based on convolutional neural network (CNN). To promote the detection performance and efficiency, we proposed a scheme based on wavelet packet (WP) decomposition and CNN. The WP decomposition is a time-frequency method and can enhance the discriminant features between gravitational wave signal and noise before detection. The CNN conducts the gravitational wave detection by learning a function mapping relation from the data under being processed to the space of detection results. This function-mapping-relation style detection scheme can detection efficiency significantly. In this work, instrument effects are considered, and the noise are computed from a power spectral density (PSD) equivalent to the Advanced LIGO design sensitivity. The quantitative evaluations and comparisons with the state-of-art method matched filtering show the excellent performances for BNS gravitational wave detection. On efficiency, the current experiments show that this WP-CNN-based scheme is more than 960 times faster than the matched filtering.

Experimental study of grout defect identification in precast column based on wavelet packet analysis

Grout defects always exist in sleeves of precast structures, but studies on grout defect identification are rarely performed. This article proposes a combination method of dynamic excitation technique and wavelet packet analysis for sleeve defect identification in the precast structure. Hammer excitation on a 1/2-scaled two-floor precast concrete frame structure with column rebar splicing by grout sleeves is conducted to collect column acceleration responses. Moreover, the corresponding energy spectrum is obtained by the wavelet packet analysis. Furthermore, three defect identification indices, that is, percentage of energy transfer, energy ratio variation deviation, and energy spectrum average deviation, are calculated and compared. Robustness analysis of the energy ratio variation deviation is carried out by adding white noise in the original acceleration response signals. The results show that (1) the percentage of energy transfer, the energy ratio variation deviation, and the energy spectrum average deviation are positively correlated with the grout defect degree where the energy ratio variation deviation is more sensitive in the identification of defects; (2) the energy ratio variation deviation robustness of the original signal with the inputted multiplicative white Gaussian noises is better than that with the inputted additive white Gaussian noise; and (3) the proposed defect identification method can characterize the sleeve grout defect degree in column.

Bimodal biometric pattern recognition system based on fusion of iris and palmprint using multi-resolution approach

A biometric pattern recognition system is a pattern recognition system which uses the biological traits to recognize individuals. Single biometric systems may not possess the required characteristics like permanence, acceptability and circumvention. In general, the performance of the unimodal biometrics systems is degraded due to noisy data acquisition from sensor, intra-class and inter-class similarities. Several such restrictions are removed when multiple sources of information are used. In this paper, a bimodal biometric system designed from iris pattern and palmprint pattern is described. The feature extractor is created from the wavelet packet analysis, and classifier is created based on neural network. Using wavelet packets and gray-level spatial dependence matrix, the iris code vector is constructed. With the help of Gabor wavelets and gray-level spatial dependence matrix, the palmprint code vector is computed. The features extracted from iris pattern and palmprint pattern are fused by feature-level fusion into a multimodal pattern vector of size 1408 bits. The recognition rate achieved by the LVQ neural network is 94.50%. This system can complete recognition in 15.25 microseconds so that it can be made use for implementing real-time recognition tasks.

A Hidden Markov Model Combined With Markov Games for Intrusion Detection in Cloud

Cloud computing has evolved as a new paradigm for management of an infrastructure and gained ample consideration in both industrial and academic area of research. A hidden Markov model (HMM) combined with Markov games can give a solution that may act as a countermeasure for many cyber security threats and malicious intrusions in a network or in a cloud. A HMM can be trained by using training sequences that may be obtained by analyzing the file traces of packet analyzer like Wireshark network analyzer. In this article, the authors have proposed a model in which HMM can be build using a set of training examples that are obtained by using a network analyzer (i.e., Wireshark). As it is not an intrusion detection system, the obtained file traces may be used as training examples to test a HMM model. It also predicts a probability value for each tested sequence and states if sequence is anomalous or not. A numerical example is also shown in this article that calculates the most optimal sequence of observations for both HMM and state sequence probabilities in case a HMM model is already given.

Research on rolling bearing fault diagnosis of high-speed train

The rolling bearing is the main vulnerable component of a rotating machinery on high-speed train, which is very important to the safe operation of high-speed train. Based on the fundamental analysis of the causes of rolling bearing faults on high-speed trains, the feasibility of wavelet packet analysis and BP neural network in rolling bearing fault diagnosis of high-speed trains is discussed. In this paper, wavelet packet is used to denoise data and extract features, then the training BP neural network is used to diagnose and analyze the characteristic data accurately, and the diagnosis results are obtained. By comparing the training results, the number of wavelet packet decomposition is determined, and the accuracy of BP neural network diagnosis is improved. In this paper, the analysis is carried out by using the bearing data of American electrical laboratory “Case Western Reserve University”, an efficient and fast method for rolling bearing diagnosis is presented, which can be applied to real-time fault diagnosis and monitoring of rolling bearing faults for high speed train.

An Improved Outlier Detection Mechanism for Hierarchical Key Management in Hierarchical Mobile Ad-hoc Networks (MANETs)

The purpose of this paper is to present an outlier detection mechanism for hierarchical key management which utilizes hybrid (public and private) key management scheme for implementing lightweight cryptographic primitives in hierarchical MANETs. Further, a comparative analysis of key management schemes is to be performed for identifying an efficient key management scheme for hierarchical MANETs. In key management methods, public and private group key management schemes are integrated at local level (subgroup) and global level (network). These key management schemes uses various topologies for minimizing communicational and computational costs. After implementing an efficient key management scheme, outliers in network are identified using packet analysis at key generation, key distribution, message transmission and key re-generation phases. It is observed that Teo and Tan key management approach with shamir’s threshold key distribution mechanism is an efficient approach key management scheme for group authentication and hierarchical key management. Further, a minimum improvement of 9.7% and 0.91%, and maximum improvements of 25.3% and 87.7% are observed for Packet Delivery Rate (APDR) and Average Throughput (AT) respectively in a network of 1000 nodes. The proposed method requires four sequential analysis of outlier detection schemes at different layers of MANET protocol stack because single scheme per layer is not efficient in identifying outliers in proposed network. The proposed key management method is useful for MANETs in group authentication and implementation of lightweight cryptographic primitives with secure key. Further, outlier detection mechanism can be extended for identifying various active and passive attacks.

Instant Detection of Host in SDN (IDH-SDN)

SDN features are making it more popular day by day: centralized monitoring, control of network equipments, increased performance and flexibility in designing network policies as per organization requirements. The SDN controller deals with data & control plane separately. The SDN switches are simply data forwarding devices and controller decides control over forwarding data through them. Controller has a technique to identify the network switch and router nodes; but it does not identify the presence of hosts before they generated network traffic and is not able to create the packet forwarding rules, security policies. The objective of this paper is to detect connected host before they generate any traffic and store host details at controller level for future researches in area of development of new network tools, applications, optimizations techniques and security. Here, we propose Instant Detection of Host in SDN (IDH-SDN) to detect host before transmission of any data packet and store host details in a HostTable at controller level. In our experiment, various network topologies have been used to test host detection and data collection algorithm and results of all experiments verified with Wireshark network packet analyzer. The HostTable data may be used for various purposes such as development of new network tools, policies, security approaches in OpenFlow network.

Intensive packet domain mining engine (IPDME)

PurposeThe service denial threats are regularly regarded as tools for effortlessly triggering online-based services offline. Moreover, the present occurrences reveal that these threats are being constantly employed for masking other vulnerable threats like disseminating malware, information losses, wire scams and mining bitcoins (Sujithra et al., 2018; Boujnouni and Jedra, 2018). In some cases, service denials have been employed to cyberheist financial firms which sums around $100,000. Documentation from Neustar accounts that is about 70 percent of the financial sector are aware of the threat, and therefore, incidents result in few losses, more than 35 percent of service denial attempts are identified as malware soon after the threat is sent out (Divyavani and Dileep Kumar Reddy, 2018). Intensive packet analysis (IPA) explores the packet headers from Layers 2 to 4 along with the application information layer from Layers 5 to 7 for locating and evading vulnerable network-related threats. The networked systems could be simply contained by low potent service denial operations in case the supplies of the systems are minimized by the safety modules. The paper aims to discuss these issues.Design/methodology/approachThe initial feature will be resolved using the IPDME by locating the standard precise header delimiters such as carriage return line feed equally locating the header names. For the designed IPDME, the time difficulties in locating the initial position of the header field within a packet with static time expenses of four cycles. For buffering packets, the framework functions at the speed of cables. Soon after locating the header position, the value of the field is mined linearly from the position. Mining all the field values consequentially resolves the forthcoming restrictions which could be increased by estimating various information bytes per cycle and omitting non-required information packets. In this way, the exploration space is minimized from the packet length to the length of the header. Because of the minimized mining time, the buffered packets could be operated at an increasing time.FindingsBased on the assessments of IPDME against broadly employed SIP application layer function tools it discloses hardware offloading of IPDME it could minimize the loads on the essential system supplies of about 25 percent. The IPDME reveals that the acceleration of 22X– 75X as evaluated against PJSIP parser and SNORT SIP pre-processor. One IPDME portrays an acceleration of 4X–6X during 12 occurrences of SNORT parsers executing on 12 processors. The IPDME accomplishes 3X superior to 200 parallel occurrences of GPU speeded up processors. Additionally, the IPDME has very minimal latencies with 12X–1,010X minimal than GPUs. IPDME accomplishes minimal energy trails of nearly 0.75 W using two engines and for 15 engines it is 3.6 W, which is 22.5X–100X less as evaluated to the graphic-based GPU speeding up.Originality/valueIPDME assures that the system pools are not fatigued on Layer 7 mining by transmitting straightforwardly based on network intrusions without branching into the operating systems. IPDME averts the latencies because of the memory accesses by sidestepping the operating system which essentially permits the scheme to function at wired speed. Based on the safety perception, IPDME ultimately enhances the performance of the safety systems employing them. The increased bandwidth of the IPDME assures that the IPA’s could function at their utmost bandwidth. The service time for the threat independent traffic is enhanced because of minimization over the comprehensive latencies over the path among the network intrusions and the related applications.

Learning Behavior of Distribution System Discrete Control Devices for Cyber-Physical Security

Conventional cyber-security intrusion detection systems monitor network traffic for malicious activity and indications that an adversary has gained access to the system. The approach discussed here expands the idea of a traditional intrusion detection system within electrical power systems, specifically power distribution networks, by monitoring the physical behavior of the grid. This is achieved through the use of high-rate distribution Phasor Measurement Units (PMUs), alongside SCADA packets analysis, for the purpose of monitoring the behavior of discrete control devices. In this work we present a set of algorithms for passively learning the control logic of voltage regulators and switched capacitor banks. Upon detection of an abnormal operation, the operator is alerted and further action can be taken. The proposed learning algorithms are validated on both simulated data and on measured PMU data from a utility pilot deployment site.

Signal processing and identification on the surface of Pinus massoniana Lamb. glulam using acoustic emission and improvement complete ensemble empirical mode decomposition with adaptive noise

A signal processing method to analyze and investigate acoustic emission (AE) signal characteristics on the surface of anisotropic Pinus massoniana Lamb. glulam material is introduced. The classical pencil lead break (PLB) method is used to simulate the AE source and research the AE signal characteristics of the specimen along the direction of both parallel and perpendicular to the grain. An effective noise-reduction method, which combines improvement complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN) and wavelet packet (WP) analysis, is proposed to denoise the original AE signal in the specimen and calculate the propagation rates of the AE signal in two directions based on the principle of cross-correlation. Moreover, the energy attenuation characteristics of the AE signal in the process of propagation are also evaluated. The experimental results indicate that the proposed approach can effectively reduce the noise of the AE signal in glulam, and it can be seen that the frequency of the AE signal is mainly concentrated at 40 kHz from the spectrum diagrams of the signal. Meanwhile, the propagation rate of the AE signal along the grain direction is approximately twice that of the perpendicular to grain, while the law of the energy attenuation is just the opposite, and provides an effective theoretical basis for the research of the influence of the adhesive layer inside the glulam on the characteristics of AE signal.

Lattice Structural Analysis on Sniffing to Denial of Service Attacks

Sniffing is one of the most prominent causes for most of the attacks in the digitized computing environment. Through various packet analyzers or sniffers available free of cost, the network packets can be captured and analyzed. The sensitive information of the victim like user credentials, passwords, a PIN which is of more considerable interest to the assailants can be stolen through sniffers. This is the primary reason for most of the variations of DDoS attacks in the network from a variety of its catalog of attacks. An effective and trusted framework for detecting and preventing these sniffing has greater significance in today's computing. A counter hack method to avoid data theft is to encrypt sensitive information. This paper provides an analysis of the most prominent sniffing attacks. Moreover, this is one of the most important strides to guarantee system security. Also, a Lattice structure has been derived to prove that sniffing is the prominent activity for DoS or DDoS attacks.

Surface quality monitoring in abrasive water jet machining of Ti6Al4V–CFRP stacks through wavelet packet analysis of acoustic emission signals

Machining such as trimming and drilling of aerospace composite structures is often required to meet the intended geometric tolerances and functional requirements. Abrasive water jet (AWJ) is a primary candidate for high speed machining of difficult-to-cut materials. The AWJ process performance is sensitive to the online faults and non-optimal process parameters, necessitating efficient techniques for online process control. In this study, acoustic emission (AE) signals are used to monitor AWJ machining of stacked titanium-CFRP. Owing to the non-stationary nature of the AE signals, this work is focused on the precision-driven predictive approach in simultaneous time-frequency domain. The AE signals were analyzed using wavelet packet transform (WPT), and an algorithm was proposed to identify and characterize these signals. Thirty-five different mother wavelets and decomposition levels up to 10 were used. The wavelet parameters (mother wavelet and decomposition) were deemed optimal when the identified signal characteristics could strongly correlate with the process parameters and kerf wall quality (surface roughness). Coiflets and Symlets were identified as the optimal wavelets with energy-entropy coefficient as the qualifying characteristic of the wavelet packet resulting in R2 > 90%. A comparative study was conducted to qualify the proposed algorithm against standard time domain analysis measures. The maximum R2 and CV (RMSD)—coefficient of variation of root mean square deviation for time domain was observed as 88.6% and 12.5% respectively as opposed to R2 = 97.12% and CV (RMSD)= 6% for the proposed WPT algorithm. Overall, an efficient algorithm was proposed in monitoring the process quality and controlling the process parameters based on the identified signal signatures.

Robust Nonfragile H∞ Control of Lateral Semiactive Suspension of Rail Vehicles

This paper focuses on the riding comfort of railway vehicle. A nonfragile control strategy is proposed based on robust theory for semiactive suspension system. First, the rail vehicle dynamic model was simplified reasonably, the control model of rail vehicle train lateral semiactive suspension was built, which involved lateral-moving, head-shaking, and side-rolling of the vehicle body and the lateral-moving of the two bogies, and the robust nonfragile H∞ control of head-shaking and side-rolling were designed, respectively. Then the H∞ norm was used to reflect riding comfort; the sufficient conditions for the existence of robust nonfragile H∞ control were developed based on linear matrix inequality (LMI) approach. The design of a robust nonfragile H∞ control with gain perturbation was changed into an optimization issue with linear inequality constraint and a linear objective function. And then the damping effect of the robust nonfragile H∞ controller was evaluated, the wavelet packet analysis theory was used to decompose and reconstruct the acceleration signal, the fragility of the controller was analyzed, and the theory of mathematical statistics was adopted to analyze the influence of robust nonfragile H∞ controller on the probability distribution of vibration acceleration. Finally, the simulation results show that the proposed control strategy can ensure the riding comfort of railway vehicle efficiently and has a comparatively strong nonfragility.

Pyrolysis kinetic study of waste milk packets using thermogravimetric analysis and product characterization

This work deals with the study of kinetic evaluation and pyrolysis of waste milk packets (polyethylene) for oil production. Thermogravimetric analysis (TGA) of waste milk packets was conducted in an inert atmosphere with heating rates of 5, 10 and 20 °C min−1 to analyse its thermal degradation behaviour. Pyrolysis kinetic parameters consist of activation energy and pre-exponentiation factor, and thermodynamic parameters include changes in Gibb’s free energy, enthalpy and entropy which were computed by Kissinger–Akahira–Sunose (KAS), Ozawa–Flynn–Wall (OFW) and Coats–Redfern methods using TGA data. The mean value of activation energy for pyrolysis of waste milk packets computed by KAS and OFW methods was 175.36 and 177.94 kJ mol−1, respectively, and the pre-exponential factors were found to be in the range of 1.37 × 1013–1.54 × 1014 min−1. Thermal pyrolysis of waste milk packets was performed in a semi-batch pyrolysis reactor. The maximum yield of oil was obtained as 45 wt% at pyrolysis temperature of 500 °C. The oil was analysed by ultimate analysis, Fourier transform infrared spectroscopy (FTIR) and gas chromatography–mass spectrometry (GC–MS). Chemical compounds present in oil were mainly paraffins, naphthenes and olefins with carbon number range of C9–C18. The boiling point of oil was found in the range of 63–360 °C.