Novel cyber-physical collaborative detection and localization method against dynamic load altering attacks in smart energy grids

Owing to the integration of energy digitization and artificial intelligence technology,smart energy grids can realize the stable,efficient and clean operation of power systems.However,the emergence of cyber-physical attacks,such as dynamic load-altering attacks(DLAAs)has introduced great challenges to the security of smart energy grids.Thus,this study developed a novel cyber-physical collaborative security framework for DLAAs in smart energy grids.The proposed framework integrates attack prediction in the cyber layer with the detection and localization of attacks in the physical layer.First,a data-driven method was proposed to predict the DLAA sequence in the cyber layer.By designing a double radial basis function network,the influence of disturbances on attack prediction can be eliminated.Based on the prediction results,an unknown input observer-based detection and localization method was further developed for the physical layer.In addition,an adaptive threshold was designed to replace the traditional precomputed threshold and improve the detection performance of the DLAAs.Consequently,through the collaborative work of the cyber-physics layer,injected DLAAs were effectively detected and located.Compared with existing methodologies,the simulation results on IEEE 14-bus and 118-bus power systems verified the superiority of the proposed cyber-physical collaborative detection and localization against DLAAs.

Modified Black Widow Optimization-Based Enhanced Threshold Energy Detection Technique for Spectrum Sensing in Cognitive Radio Networks

This study develops an Enhanced Threshold Based Energy Detection approach(ETBED)for spectrum sensing in a cognitive radio network.The threshold identification method is implemented in the received signal at the secondary user based on the square law.The proposed method is implemented with the signal transmission of multiple outputs-orthogonal frequency division multiplexing.Additionally,the proposed method is considered the dynamic detection threshold adjustments and energy identification spectrum sensing technique in cognitive radio systems.In the dynamic threshold,the signal ratio-based threshold is fixed.The threshold is computed by considering the Modified Black Widow Optimization Algorithm(MBWO).So,the proposed methodology is a combination of dynamic threshold detection and MBWO.The general threshold-based detection technique has different limitations such as the inability optimal signal threshold for determining the presence of the primary user signal.These limitations undermine the sensing accuracy of the energy identification technique.Hence,the ETBED technique is developed to enhance the energy efficiency of cognitive radio networks.The projected approach is executed and analyzed with performance and comparison analysis.The proposed method is contrasted with the conventional techniques of theWhale Optimization Algorithm(WOA)and GreyWolf Optimization(GWO).It indicated superior results,achieving a high average throughput of 2.2 Mbps and an energy efficiency of 3.8,outperforming conventional techniques.

Adaptive double-threshold energy detection algorithm for cognitive radio

Due to the fact that the conventional spectrum sensing algorithm is susceptible to noise, an adaptive double-threshold energy detection algorithm for a cognitive radio is proposed. Based on double-threshold energy detection, the algorithm can adaptively switch between one-round sensing and two-round sensing by comparing the observations with the pre-fixed thresholds. Mathematical expressions for the probability of detection, the probability of false alarm, and the sensing time are derived. The relationships including signal to noise ratio （SNR） vs. the probability of detection and SNR vs. the sensing time are plotted using Monte Carlo simulation and the algorithm is verified in a real cognitive system based on GNU Radio and universal software radio peripheral （USRP）. Simulation and experimental results show that, compared with the existing spectrum sensing method, the proposed algorithm can achieve a higher probability of detection within a reasonable sensing time.

Speech Endpoint Detection in Noisy Environments Using EMD and Teager Energy Operator

Accurate endpoint detection is a necessary capability for speech recognition. A new energy measure method based on the empirical mode decomposition （EMD） algorithm and Teager energy operator （TEO） is proposed to locate endpoint intervals of a speech signal embedded in noise. With the EMD, the noise signals can be decomposed into different numbers of sub-signals called intrinsic mode functions （IMFs）, which is a zero-mean AM-FM component. Then TEO can be used to extract the desired feature of the modulation energy for IMF components. In order to show the effectiveness of the proposed method, examples are presented to show that the new measure is more effective than traditional measures. The present experimental results show that the measure can be used to improve the performance of endpoint detection algorithms and the accuracy of this algorithm is quite satisfactory and acceptable.

Research on Low Energy Consumption Distributed Fault Detection Mechanism in Wireless Sensor Network

Wireless sensor network is an important technical support for ubiquitous communication. For the serious impacts of network failure caused by the unbalanced energy consumption of sensor nodes, hardware failure and attacker intrusion on data transmission, a low energy consumption distributed fault detection mechanism in wireless sensor network(LEFD) is proposed in this paper. Firstly, the time correlation information of nodes is used to detect fault nodes in LEFD, and then the spatial correlation information is adopted to detect the remaining fault nodes, so as to check the states of nodes comprehensively and improve the efficiency of data transmission. In addition, the nodes do not need to exchange information with their neighbor nodes in the initial detection process since LEFD adopts the data sensed by node itself to detect some types of faults, thus reducing the energy consumption of nodes effectively. Finally, LEFD also considers the nodes that may have transient faults. Performance analysis and simulation results show that the proposed detection mechanism can improve the transmission performance and reduce the energy consumption of network effectively.

Determination of Threshold for Energy Detection in Cognitive Radio Sensor Networks

The Internet of Things （loT） is called the world＇ s third wave of the information industry. As the core technology of IoT, Cognitive Radio Sensor Networks （CRSN） technology can improve spectrum utilization efficiency and lay a sofid foundation for large-scale application of IoT. Reliable spectrum sensing is a crucial task of the CR. For energy de- tection, threshold will determine the probability of detection （Pd） and the probability of false alarm Pf at the same time. While the threshold increases, Pd and Pf will both decrease. In this paper we focus on the maximum of the difference of Pd and Pf, and try to find out how to determine the threshold with this precondition. Simulation results show that the proposed method can effectively approach the ideal optimal result.

A Lightweight Intrusion Detection System Using Convolutional Neural Network and Long Short-Term Memory in Fog Computing

The Internet of Things(IoT)links various devices to digital services and significantly improves the quality of our lives.However,as IoT connectivity is growing rapidly,so do the risks of network vulnerabilities and threats.Many interesting Intrusion Detection Systems(IDSs)are presented based on machine learning(ML)techniques to overcome this problem.Given the resource limitations of fog computing environments,a lightweight IDS is essential.This paper introduces a hybrid deep learning(DL)method that combines convolutional neural networks(CNN)and long short-term memory(LSTM)to build an energy-aware,anomaly-based IDS.We test this system on a recent dataset,focusing on reducing overhead while maintaining high accuracy and a low false alarm rate.We compare CICIoT2023,KDD-99 and NSL-KDD datasets to evaluate the performance of the proposed IDS model based on key metrics,including latency,energy consumption,false alarm rate and detection rate metrics.Our findings show an accuracy rate over 92%and a false alarm rate below 0.38%.These results demonstrate that our system provides strong security without excessive resource use.The practicality of deploying IDS with limited resources is demonstrated by the successful implementation of IDS functionality on a Raspberry Pi acting as a Fog node.The proposed lightweight model,with a maximum power consumption of 6.12 W,demonstrates its potential to operate effectively on energy-limited devices such as low-power fog nodes or edge devices.We prioritize energy efficiency whilemaintaining high accuracy,distinguishing our scheme fromexisting approaches.Extensive experiments demonstrate a significant reduction in false positives,ensuring accurate identification of genuine security threats while minimizing unnecessary alerts.

An Improved Modal Strain Energy Method for Damage Detection in Offshore Platform Structures

The development of robust damage detection methods for offshore structures is crucial to prevent catastrophes caused by structural failures. In this research, we developed an Improved Modal Strain Energy （IMSE） method for detecting damage in offshore platform structures based on a traditional modal strain energy method （the Stubbs index method）. The most significant difference from the Stubbs index method was the application of modal frequencies. The goal was to improve the robustness of the traditional method. To demonstrate the effectiveness and practicality of the proposed IMSE method, both numerical and experimental studies were conducted for different damage scenarios using a jacket platform structure. The results demonstrated the effectiveness of the IMSE method in damage location when only limited, spatially incomplete, and noise-polluted modal data is available. Comparative studies showed that the IMSE index outperformed the Stubbs index and exhibited stronger robustness, confirming the superiority of the proposed approach.

Cooperative Spectrum Sensing over Generalized Fading Channels Based on Energy Detection

This paper analyzes the unified performance of energy detection(ED) of spectrum sensing(SS) over generalized fading channels in cognitive radios(CRs). The detective performance of SS will be obviously affected by fading channels among communication nodes, and ED has the advantages of fast implementation, no requirement of priori received information and low complexity, so it is meaningful to investigate ED over various fading channels. The probability density function(p.d.f.) of α-κ-μ distribution is derived to evaluate energy efficiency for sensing systems.The detection probability with Marcum-Q function has been derived and the close-form expressions with moment generating function(MGF) method are deduced to achieve SS.Furthermore, exact closed-form analytic expressions for average area under the receiver operating characteristics curve( AUC) also have been deduced to analyze the performance characteristics of ED over α-κ-μ fading channels.Besides, cooperative spectrum sensing(CSS) with diversity reception has been applied to improve the detection accuracy and mitigate the shadowed fading features with OR-rule. At last, the results show that the detection capacity of ED will be evidently affected by α-κ-μ fading channels, but appropriate channel parameters can improve sensing performance. In addition, the establishedED-fading pattern is approved by simulations,and it can significantly enhance the detection performance of proposed algorithms.

Speech enhancement through voice activity detection using speech absence probability based on Teager energy

In this work, a novel voice activity detection （VAD） algorithm that uses speech absence probability （SAP） based on Teager energy （TE） was proposed for speech enhancement. The proposed method employs local SAP （LSAP） based on the TE of noisy speech as a feature parameter for voice activity detection （VAD） in each frequency subband, rather than conventional LSAP. Results show that the TE operator can enhance the abiTity to discriminate speech and noise and further suppress noise components. Therefore, TE-based LSAP provides a better representation of LSAP, resulting in improved VAD for estimating noise power in a speech enhancement algorithm. In addition, the presented method utilizes TE-based global SAP （GSAP） derived in each frame as the weighting parameter for modifying the adopted TE operator and improving its performance. The proposed algorithm was evaluated by objective and subjective quality tests under various environments, and was shown to produce better results than the conventional method.

Alcoholism Detection by Wavelet Energy Entropy and Linear Regression Classifier

Alcoholism is an unhealthy lifestyle associated with alcohol dependence.Not only does drinking for a long time leads to poor mental health and loss of self-control,but alcohol seeps into the bloodstream and shortens the lifespan of the body’s internal organs.Alcoholics often think of alcohol as an everyday drink and see it as a way to reduce stress in their lives because they cannot see the damage in their bodies and they believe it does not affect their physical health.As their drinking increases,they become dependent on alcohol and it affects their daily lives.Therefore,it is important to recognize the dangers of alcohol abuse and to stop drinking as soon as possible.To assist physicians in the diagnosis of patients with alcoholism,we provide a novel alcohol detection system by extracting image features of wavelet energy entropy from magnetic resonance imaging(MRI)combined with a linear regression classifier.Compared with the latest method,the 10-fold cross-validation experiment showed excellent results,including sensitivity 91.54±1.47%,specificity 93.66±1.34%,Precision 93.45±1.27%,accuracy 92.61±0.81%,F1 score 92.48±0.83%and MCC 85.26±1.62%.

Kernel fuzzy c-means clustering on energy detection based cooperative spectrum sensing

Cooperation in spectral sensing （SS） offers a fast and reliable detection of primary user （PU） transmission over a frequency spectrum at the expense of increased energy consumption. Since the fusion center （FC） has to handle a large set of data, a duster based approach, specifically fuzzy c-means clustering （FCM）, has been extensively used in energy detection based cooperative spectrum sensing （CSS）. However, the performance of FCM degrades at low signal-to-noise ratios （SNR） and in the presence of multiple PUs as energy data patterns at the FC are often found to be non-spherical i.e. overlapping. To address the problem, this work explores the scope of kernel fuzzy c-means （KFCM） on energy detection based CSS through the projection of non-linear input data to a high dimensional feature space. Extensive simulation results are shown to highlight the improved detection of multiple PUs at low SNR with low energy consumption. An improvement in the detection probability by ～6.78% and ～6.96% at -15 dBW and -20 dBW, respectively, is achieved over the existing FCM method.

A Diffusion-Based Distributed Collaborative Energy Detection Algorithm for Spectrum Sensing in Cognitive Radio

Spectrum sensing is one of the most important steps in cognitive radio. In this paper, a new fully-distributed collaborative energy detection algorithm based on diffusion cooperation scheme and consensus filtering theory is proposed, which doesn’t need the center node to fuse the detection results of all users. The secondary users only exchange information with their neighbors to obtain the detection data, and then make the corresponding decisions independently according to the pre-defined threshold. Simulations show that the proposed algorithm is more superior to the existing centralized collaborative energy detection algorithm in terms of the detecting performance and robustness in the insecurity situation.

GRADIENT ENERGY DETECTION OF LSB STEGANOGRAPHY

The spatial Least Significant Bit (LSB) steganography results in the alteration of the smooth characteristics between adjoining pixels of the raw image. The relation between the length of embedded message and the gradient energy is theoretically analyzed, and then a steganalysis and detection method, named Gradient Energy-Flipping Rate (GEFR) detection is proposed. Based on the analysis of the variation of the gradient energy, which results from the LSB steganography in color and grayscale image, the secret message embedded in the target image is detected, and the length of the embedded message is estimated. The method is proved effective and accurate by simulation (detection rate reaches O.Olbit per pixel).

Energy Theft Detection in Smart Grids:Taxonomy,Comparative Analysis,Challenges,and Future Research Directions

Electricity theft is one of the major issues in developing countries which is affecting their economy badly.Especially with the introduction of emerging technologies,this issue became more complicated.Though many new energy theft detection(ETD)techniques have been proposed by utilising different data mining(DM)techniques,state&network(S&N)based techniques,and game theory(GT)techniques.Here,a detailed survey is presented where many state-of-the-art ETD techniques are studied and analysed for their strengths and limitations.Three levels of taxonomy are presented to classify state-of-the-art ETD techniques.Different types and ways of energy theft and their consequences are studied and summarised and different parameters to benchmark the performance of proposed techniques are extracted from literature.The challenges of different ETD techniques and their mitigation are suggested for future work.It is observed that the literature on ETD lacks knowledge management techniques that can be more effective,not only for ETD but also for theft tracking.This can help in the prevention of energy theft,in the future,as well as for ETD.

Research on Hybrid Renewable Energy Systems with Fault Detection Technology

Early and accurate fault detection and diagnosis for renewable energy systems can increase their safety and ensure the continuity of their service. This paper presents a comprehensive review of different fault detection and diagnosis methods for hybrid renewable energy systems consisting of a wind turbine power generator, a PV （photovoltaic） array, a PEM （polymer electrolyte membrane） fuel cell and a battery storage system. The need of batteries to store the generated power from the solar panel, wind turbine or PEM fuel cell is also emphasized. Finally, an overview of the current methods used in the diagnosing of the lead-acid battery degradation is given.

Adaptive Multi-scale Edge Detection Based on Region Energy

An adaptive multiscale edge detection method based on region energy analysis is presented here. Region energy distributions of both sides in different edge directions are studied. Based on the analysis and on the ratio between region energy threshold difference and region area, the adaptive multiscale edge detection rnethod is developed. The experiment result shows that the new method is effective, feasible and noise-resistant in image detection.

Robust and Low-Complexity Synchronization for Energy Detection UWB Receiver

A simple method using aided sliding rectangular windows for synchronization in energy detector （ED） receiver is proposed for impulse-based ultra wideband radios （IR-UWB） under binary pulse position modulation （PPM）, therefore grants an attractive solution for gaining low complexity while the accompanying performance loss in terms of UWB signal reception is comparatively low. Also, a method is developed to sup- press noise through accumulation of integrated results before synchronization point is reached. This proposed method can effectively reduce the impact of one of the major performance-degrading factors in ED receivers, i. e., noise caused by heightened noise floor due to large bandwidth product. Our theoretic work on this im- proved synchronization performance and relevant simulations are conducted on IEEE 802.15.4a channel mod- els, and results show that the proposed design scheme can effectively decrease both the probability of false alarm and probability of missed detection.

Mitigation of primary user emulation attack using a new energy detection method in cognitive radio networks

A most promising solution to the expansion of spectrum efficiency is cognitive radio(CR)and this expansion is achieved by permitting the licensed frequency bands to be accessed by unlicensed secondary users(SUs)with a lack of interference with licensed primary users(PUs).This utilization of CR networks in the spectrum sensing causes vulnerable attacks like primary user emulation(PUE)attack and here PUs play the role of malicious user and do not permit other users to utilize PUs channel even in their unavailability.On the basis of the traditional single-threshold energy detection algorithm,a novel modified double-threshold energy detector is formulated in the CR network and the detection probability,miss detection probability,probability of false alarm,and their inter-relationship are analyzed.This paper develops a modified double threshold energy detection cooperative spectrum sensing technique to alleviate the PUE attack.Finally,performance-based evaluation is carried out between the proposed and the existing energy detection spectrum sensing method that had no consideration on PUE attack.The resultant of the simulation in MATLAB has revealed that the proposed model has significantly mitigated PUE attack by means of providing outstanding performance.

Speech Resampling Detection Based on Inconsistency of Band Energy

Speech resampling is a typical tempering behavior,which is often integrated into various speech forgeries,such as splicing,electronic disguising,quality faking and so on.By analyzing the principle of resampling,we found that,compared with natural speech,the inconsistency between the bandwidth of the resampled speech and its sampling ratio will be caused because the interpolation process in resampling is imperfect.Based on our observation,a new resampling detection algorithm based on the inconsistency of band energy is proposed.First,according to the sampling ratio of the suspected speech,a band-pass Butterworth filter is designed to filter out the residual signal.Then,the logarithmic ratio of band energy is calculated by the suspected speech and the filtered speech.Finally,with the logarithmic ratio,the resampled and original speech can be discriminated.The experimental results show that the proposed algorithm can effectively detect the resampling behavior under various conditions and is robust to MP3 compression.