Traditional multi-path transmission routing can improve the reliability of transmission, but the maintenance and update of paths need to consume a lot of resources. Opportunistic routing, compared with traditional routing strategies, is suitable for wireless sensor network transmission, but it faces the problem that there will be conflicts in simultaneous propagation. In this paper, combining reliable routing and opportunistic routing, considering opportunistic routing with node location information to determine the priority of forwarding candidate nodes can well balance the relationship between energy consumption and reliable transmission.

Along with the recent growth of IOT applications, related security issues have also received a great attention. Various IOT vulnerabilities have been investigated so far, among which, internal attacks are the most important challenge that are mostly aimed at destroying IOT standard routing protocol (RPL). Recent studies have introduced trust concept as a practical tool for timely diagnosis and prevention of such attacks. In this paper trust evaluation is performed based on investigating the traffic flow of devices and detecting their behavior deviations in case of RPL attack scenarios, which is formulated as a sequence prediction problem and a new Trust-based RPL Attacks Detection (TRAD) algorithm is proposed using Recurrent Neural Networks (RNNs). Traffic behavior prediction based on historical behavior and deviation analysis, provides the possibility of anomaly detection, which has an enormous effect on the accuracy and predictability of attack detection algorithms. According to the results, the proposed model is capable of detecting compromised IOT nodes in different black-hole and selective-forwarding attack scenarios, just at the beginning time of the first attack, which provides the possibility of early detection and isolation of malicious nodes from the routing process.

In wireless sensor networks (WSNs), data communication is performed using different routing protocols. One of the mostly used routing protocol is cluster-based routing protocol. The foundation of cluster-based routing protocol is the formation of clusters and selection of Cluster Head (CH) for energy-efficient transmission. CH are solely responsible for data packets transmission among nodes. But, such protocols are susceptible to network attacks. In this paper, a novel secure cluster-based routing protocol is designed for safe data transmission. Based on the findings, the designed solution offers excellent capabilities in terms of packet delivery ratio (PDR), energy usage, throughput, and End-to-End delay.

The intra cluster routing protocol designed in this paper is based on the trust model of vehicle nodes. The trust model is mainly composed of the reliability of vehicle nodes and other attribute factors of vehicle nodes. The cloud data center uses principal component analysis to calculate the trust value. The attributes of the vehicle nodes in the cluster are forwarded to the cluster head along with the Hello message packet. The cluster head forms an attribute list and sends it to the cloud data center, which calculates the trust value. When there are multiple paths to choose, the vehicle nodes in the cluster take the trust value of each node on the path as an important basis for path selection, forming the routing process in the cluster. By adding trust computation, reliable data transmission between vehicle nodes can be guaranteed. The experimental results show that the intra cluster routing protocol designed by the trust model in this scheme is effective and feasible, and the maximum speed of vehicle nodes has an impact on the performance of the routing algorithm.

Vehicular Ad Hoc Networks (VANETs) rely heavily on trustworthy message exchanges between vehicles to enhance traffic efficiency and transport safety. Although cryptographybased methods are capable of alleviating threats from unauthenticated attackers, they can not prevent attacks from those legitimate network participants. This paper proposes a trust model to deal with attackers from the latter case, who can tamper with their received messages and deliberately decrease the trust value of benign vehicles. The trust evaluation process is formed by two stages: (i) the local trust evaluation at vehicles and (ii) trust aggregation on Road Side Units (RSUs). In the local trust evaluation stage, vehicles detect attacks and calculate the trust value for others in a distributed manner. Also, the social metrics of vehicles are calculated based on interaction records and trajectories. In the trust aggregation stage, each RSU collects local data from nearby vehicles and derives aggregation weights from the eigenvector centrality of the local trust network and social metrics. Then the RSU broadcasts the aggregated trust value towards vehicles in proximity. These vehicles can thus obtain a more accurate and comprehensive view. Vehicles with trust value below a preset threshold will be considered malicious. Extensive simulations based on the ONE simulator show that the proposed model (TECS) outperforms another benchmark model (IWOT-V) regarding the malicious vehicle detection and the delivery rate of authentic messages.

In Mobile Adhoc Networks (MANETs), resilient optimization is based on the least energy utilization as well as privacy. The crucial concerns for the productive design to provide multi-hop routing are security and energy consumption. Concerning these problems, we present in this paper an author proposed routing protocol called Protected Quality of Service (QoS) aware Energy Efficient Routing protocol. It is developed on trust along with energy efficiency and points to improve MANET security.

Internet of Things (IoT) has become extremely prominent for industrial applications and stealthy modification deliberately done by insertion of Hardware Trojans has increased widely due to globalization of Integrated Circuit (IC) production. In the proposed work, Hardware Trojan is detected at the gate level by considering netlist of the desired circuits. To mitigate with golden model dependencies, proposed work is based on unsupervised detection of Hardware Trojans which automatically extracts useful features without providing clear desired outcomes. The relevant features from feature dataset are selected using eXtreme Gradient Boosting (XGBoost) algorithm. Average True Positive Rate (TPR) is improved about 30\% by using Clustering-based local outlier factor (CBLOF) algorithm when compared to local outlier factor algorithm. The simulation is employed on Trust-HUB circuits and achieves an average of 99.83\% True Negative Rate (TNR) and 99.72\% accuracy which shows the efficiency of the detection method even without labelling data.

Hardware Trojans (HT) are minuscule circuits embedded by an adversary for malicious purposes. Such circuits posses stealthy nature and can cause disruption upon activation. To detect the presence of such circuits, appropriate test vectors need to be applied. In this regard, the genetic algorithm (GA) seems to be the most promising technique due to its exploration capability. However, like most of the existing techniques, GA also suffers from exploring the huge search space. In this article a GA based methodology is proposed incorporating the information about potential inputs into it. Experimental results analysis signifies that the identification of the relevant inputs for GA provides an optimal solution. The significance of proposed methodology is endorsed by applying the proposed GA technique on different ISCAS ’85 benchmark circuits. A noteworthy improvement on run time is observed while simultaneously providing improved test set quality than the state-of-the art technique.

Recently, hardware Trojan has become a serious security concern in the integrated circuit (IC) industry. Due to the globalization of semiconductor design and fabrication processes, ICs are highly vulnerable to hardware Trojan insertion by malicious third-party vendors. Therefore, the development of effective hardware Trojan detection techniques is necessary. Testability measures have been proven to be efficient features for Trojan nets classification. However, most of the existing machine-learning-based techniques use supervised learning methods, which involve time-consuming training processes, need to deal with the class imbalance problem, and are not pragmatic in real-world situations. Furthermore, no works have explored the use of anomaly detection for hardware Trojan detection tasks. This paper proposes a semi-supervised hardware Trojan detection method at the gate level using anomaly detection. We ameliorate the existing computation of the Sandia Controllability/Observability Analysis Program (SCOAP) values by considering all types of D flip-flops and adopt semi-supervised anomaly detection techniques to detect Trojan nets. Finally, a novel topology-based location analysis is utilized to improve the detection performance. Testing on 17 Trust-Hub Trojan benchmarks, the proposed method achieves an overall 99.47\% true positive rate (TPR), 99.99\% true negative rate (TNR), and 99.99\% accuracy.

There have been reports of threats that cause electromagnetic information leakage by inserting Hardware Trojans (HT) into the signal traces around components on the printed circuit board (PCB). In this threat, the HT insertion is assumed not only at the manufacturing stage but also during the in-transit or in the field after shipment, and the threat may extend to devices that are not considered to be threatened by HT insertion implemented inside conventional ICs. This paper discusses the detection method for the HT insertion, which is implementable on a PCB without external measurement equipment. Additionally, we validate the method in more practical situations, detecting the HT on populated PCBs. The method employs an on-chip touch sensor to measure the changes in electrical characteristics caused by HT insertion. Specifically, HT insertion is detected by observing the change in capacitance and insulation resistance associated with HT insertion using the on-chip sensor, and detecting the difference from the measurement result when HT is not inserted to signal traces. In the experiment, we build an evaluation environment, which emulates a populated PCB, based on the HT insertion method reported in previous studies and observe the change in capacitance and insulation resistance on the connected signal trace using a microprocessor equipped with a constant current source and an analog-digital converter that constitute the onchip sensor. Then, we show that HT insertion on the signal trace can be detected from the output values of the on-chip sensor before and after HT insertion.

Emerging Analog Trojans such as A2, large-delay Trojans, and row-hammer have been shown to be more stealthier than previously known digital Trojans. They are smaller sized, do not rely on inputs for triggering, and the trigger for their payload can be made arbitrarily delayed, like a ticking time bomb. Furthermore, analog Trojans can easily evade detection due to their novel nature and incompatibility with the digital design and validation flow. In this paper, we propose a current signature-based detection scheme, which can effectively catch various analog Trojans at both run-time and production time validation. The paper includes techniques that advance Trojan detection method through incorporating detection of transient variation in the power supply current. Proposed current-sensor can sense currents down to 10s of nano-Amps improving over prior power sensing based techniques. Further, a configurable design of current sensor is developed to enable large range sensing capability. The design is also developed to be compatible with the digital design flow and can be logic obfuscated. This detection method can be used at run-time to potentially fence off activation of analog Trojans in the field through early warning signals. The commercial 65nm CMOS technology is utilized to verify the proposed idea.

This work proposes a novel hardware Trojan detection method that leverages static structural features and behavioral characteristics in field programmable gate array (FPGA) netlists. Mapping of hardware design sources to look-up-table (LUT) networks makes these features explicit, allowing automated feature extraction and further effective Trojan detection through machine learning. Four-dimensional features are extracted for each signal and a random forest classifier is trained for Trojan net classification. Experiments using Trust-Hub benchmarks show promising Trojan detection results with accuracy, precision, and F1-measure of 99.986\%, 100\%, and 99.769\% respectively on average.

In recent years, with the globalization of semiconductor processing and manufacturing, integrated circuits have gradually become vulnerable to malicious attackers. In order to detect Hardware Trojans (HTs) hidden in integrated circuits, it has become one of the hottest issues in the field of hardware security. In this paper, we propose to apply Principal Component Analysis (PCA) and Support Vector Machine (SVM) to hardware Trojan detection, using PCA algorithm to extract features from small differences in side channel information, and then obtain the principal components. The SVM detection model is optimized by means of cross-validation and logarithmic interval. Finally, it is determined whether the original circuit contains a hardware Trojan. In the experiment, we use the SAKURA-G FPGA board, Agilent oscilloscope, and ISE simulation software to complete the experimental work. The test results of five different HTs show that the average True Positive Rate (TPR) of the proposed method for HTs can reach 99.48\%, along with an average True Negative Rate (TNR) of 99.2\%, and an average detection time of 9.66s.

In order to visually present all kinds of hardware Trojan horse detection methods and their relationship, a method is proposed to construct the knowledge graph of hardware Trojan horse detection technology. Firstly, the security-related knowledge of hardware Trojan horse is analyzed, then the entity recognition and relationship extraction are carried out by using BiLSTM-CRF model, and the construction of knowledge graph is completed. Finally, the knowledge is stored and displayed visually by using graph database neo4j. The combination of knowledge graph and hardware Trojan security field can summarize the existing detection technologies, provide a basis for the analysis of hardware Trojans, vigorously promote the energy Internet security construction, and steadily enhance the energy Internet active defense capability.

Outsourcing Integrated Circuits(ICs) pave the way for including malicious circuits commonly known as Hardware Trojans. Trojans can be divided into functional and parametric Trojans. Trojans of the first kind are made by adding or removing gates to or from the golden reference design. Trojans of the following type, the golden circuit is modified by decreasing connecting wire’s thickness, exposing the chip to radiation, etc. Hardware Trojan detection schemes can be broadly classified into dynamic and static detection schemes depending on whether or not the input stimulus is applied. The proposed method aims to detect functional Trojans using the static detection method. The work proposes a generic, scalable Trojan detection method. The defender does not have the luxury of knowing the type of Trojan the circuit is infected with, making it difficult for accurate detection. In addition, the proposed method does not require propagating the Trojan effect on the output, magnifying the Trojan effect, or any other voting or additional algorithms to accurately detect the Trojan as in previous literature. The proposed method analyses synthesis reports for Trojan detection. Game theory, in addition, aids the defender in optimal decisionmaking. The proposed method has been evaluated on ISCAS’85 and ISCAS’89 circuits. The proffered method detects various types of Trojans of varying complexities in less time and with 100\% accuracy.

With the development of streaming media, soft real-time system in today’s life could participate in the use of more extensive areas. The use frequency was also increasing. Consequently, modern processors were equipped with software control mechanisms such as DVFS (Dynamic Voltage Frequency Scaling) to allow operating systems to meet required performance while reducing power consumption. Therefore, we propose a task scheduling algorithm combined DVFS technology and time deterministic cyclic scheduling to achieve energy saving effect. First, the algorithm needed to minimize the preemption between tasks to reduce latency, so we created a buffer to save periodic tasks to avoid preemption. Second, to reduce the computational cost of the scheduling scheme, a scheduling template were designed to perform tasks. In this paper, the scheduling of periodic tasks, task scheduling would be designed when the task scheduling template would be fixed length. Besides, this algorithm supported that task could adopt appropriate voltage and frequency through DVFS technology in idle time under the condition of satisfying task dependence. Experimental analysis showed that the proposed algorithm could effectively reduce the system energy consumption while ensuring the completion of the task.

Message-locked Encryption (MLE) is the most common approach used in encrypted deduplication systems. However, the systems based on MLE are vulnerable to frequency analysis attacks, because MLE encrypts the identical plaintexts into the identical ciphertexts, which is deterministic. The state-of-theart defense scheme, which named TED, lacks key verification and uses a single key server to record frequency information. Once the key server is compromised, TED will be vulnerable to brute-force attacks. In addition, TED’s key generation algorithm needs to be designed more exquisitely to strengthen protection, and its security indicator is not comprehensive. We propose SDAF, which supports key verification and enhanced protection against frequency analysis attacks. Based on chameleon hash, SDAF realizes key verification to prevent malicious key servers from generating fake encryption keys. In order to disturb the frequency information, SDAF introduces reservoir sample to generate uniformly distributed encryption keys, and uses multiple key servers, which interact with each other via multi-party PSI and rotate spontaneously to avoid the single point of failure. Moreover, a new indicator Kurtosis is pointed out to evaluate the security against frequency analysis attacks. We implement the prototypes of SDAF. The experiments of the real-world data sets show that, compared with the existing schemes, SDAF can better resist frequency analysis attacks with lower time overheads.

Frequency hopping (FH) technology is one of the most effective technologies in the field of radio countermeasures, meanwhile, the recognition of FH signal has become a research hotspot. FH signal is a typical non-stationary signal whose frequency varies nonlinearly with time and the time-frequency analysis technique provides a very effective method for processing this kind of signal. With the renaissance of deep learning, methods based on time-frequency analysis and deep learning are widely studied. Although these methods have achieved good results, the recognition accuracy still needs to be improved. Through the observation of the datasets, we found that there are still difficult samples that are difficult to identify. Through further analysis, we propose a horizontal spatial attention (HSA) block, which can generate spatial weight vector according to the signal distribution, and then readjust the feature map. The HSA block is a plug-and-play module that can be integrated into common convolutional neural network (CNN) to further improve their performance and these networks with HSA block are collectively called HANets. The HSA block also has the advantages of high recognition accuracy (especially under low SNRs), easy to implant, and almost no influence on the number of parameters. We verified our method on two datasets and a series of comparative experiments show that the proposed method achieves good results on FH datasets.

Inertia plays a key role in power system resistance to active power disturbance. Under the background of large-scale renewable energy participating in power systems, the problem of weak inertia support brings challenges to power system security and stability operation. Based on the analysis of system equivalent inertia time constant, the inertia time constant of renewable energy access to the system in different scenarios are solved in this paper. According to the effects of inertia time constant change on the dynamic characteristics of system frequency, the assessment indexes of equivalent inertia time constant and the rate of change of frequency (RoCoF) is proposed. Then the inertia of high proportional renewable energy system and frequency stability is evaluated, combined with the assessment index of frequency deviation. Finally, the maximum renewable energy penetration of the system is analyzed with the proposed indexes. IEEE 30-bus system is used to verify the effectiveness of the proposed method by analyzing the RoCoF and equivalent inertia time constant assessment indexes.

Round-trip transmission scheme is one of key scheme for the high-precise fiber time synchronization system. Here an asymmetric channel attack against practical roundtrip time synchronization system is proposed and experimentally demonstrated. Using the achieved asymmetric channel attack module, the accuracy of the time synchronization system can be reduced from 90 ps to 538 ps as designed. It shows that channel symmetry assumption in practical applications could be broken by such attack method, and this attack could not be found without single-way-delay monitoring.

Mechanical vibration signals of GIS equipment are important information to reflect the operating status of equipment, but the vibration excitation of existing research is mostly based on a single power frequency current, and the detection effect has certain limitations. Therefore, in order to explore the influence of current frequency on GIS mechanical vibration characteristics, this paper carried out research on GIS mechanical vibration characteristics under variable frequency current excitation. Firstly, the mechanical vibration simulation platform of 110 kV GIS equipment under variable frequency current excitation was built in the laboratory. Then, the vibration signals generated by the equipment shell under normal operation state were collected based on the mechanical vibration detection system. Finally, the evolution laws of time domain and frequency domain vibration spectra of GIS equipment under different current frequencies and loads were studied. The results show that the overall time domain waveforms are smooth and the main vibration frequencies are twice the frequencies of excitation currents. Under the condition of the variable frequency current excitation with the same amplitude, the amplitudes of time domain and frequency domain vibration spectra of vibration signals are the largest when the GIS equipment is excited by 1200 A current at 40 Hz and 2400 A current at 80 Hz. Under the condition of the variable amplitude currents excitation with the same frequency, the amplitudes of vibration signals are positively correlated with the amplitudes of currents, and the distributions of frequency spectra are highly concentrated.

Large-scale renewable energy participates in the power grid through power electronic equipment, which cannot provide stable and effective inertia support for the power system. Based on the rate of change of frequency at the time of disturbance and the virtual inertia control of the energy storage system, the supporting effect of the energy storage on the inertia of a high-proportional renewable energy system is analyzed in this paper. Then an energy storage capacity configuration calculation method is proposed considering the equivalent inertia time constant and virtual inertia control parameters. Next, the quantitative analysis index is proposed based on the supporting effect of inertia, which provides analysis methods for renewable energy participating in the power grid and energy storage capacity configuration. Finally, the IEEE 30-bus system is used to analyze system frequency response characteristics under different energy storage capacity configuration scenarios. The effectiveness of the proposed method is verified.

The paper presents the stages of constructing a highly informative digital image of the time-frequency representation of information signals of cyber-physical systems. Signal visualization includes the stage of displaying the signal on the frequency-time plane, the stage of two-dimensional digital filtering and the stage of extracting highly informative components of the signal image. The use of two-dimensional digital filtering allows you to select the most informative component of the image of a complex analyzed information signal. The obtained digital image of the signal of the cyber-physical system is a highly informative initial information for solving a wide range of different problems of information security systems in cyberphysical systems with the subsequent use of machine learning technologies.

This paper studies a power conversion system supplying a High-Speed Permanent Magnet Motor (HSPMM). In opposite of classical approach, this study observes a dynamic trajectory modelling an electric drive chain with a constant acceleration of the machine to its nominal speed. This global approach allows to observe different phenomena at the same time (resonance, subharmonic, and harmonic distortion - THD) specific to the trajectory. The method reconciles electrical phenomena with a powerful mechanism of analysis from the Short-Time Fourier Transform (STFT) and the visual representation of the frequency spectrum (spectrogram tool). The Predictive Time-Frequency analysis applied on Electric Drive Systems (PreTiFEDS) offers a powerful tool for engineers and electric conversion system architects when designing the drive system chain.

The benefits of applying and integrating robotics and automation machinery in production plans are being followed by the peak of cybersecurity issues associated with them. This study presents the threat model for a production plant integrated with different components such as PLCs, machine tools, sensors, actuators, and robots. Attending to the heterogeneity of components, protocols, and devices, this paper tries to represent the possible threats that would be affecting the factory and proposes a set of changes and mitigations that would increase their cybersecurity and resilience.