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Qin, Yishuai, Xiao, Bing, Li, Yaodong, Yu, Jintao.  2021.  Structure adjustment of early warning information system based on timeliness. 2021 IEEE 5th Advanced Information Technology, Electronic and Automation Control Conference (IAEAC). 5:2742–2747.
Aimed at the high requirement of timeliness in the process of information assurance, this paper describes the average time delay of information transmission in the system, and designs a timeliness index that can quantitatively describe the ability of early warning information assurance. In response to the problem that system capability cannot meet operational requirements due to enemy attacks, this paper analyzes the structure of the early warning information system, Early warning information complex network model is established, based on the timeliness index, a genetic algorithm based on simulated annealing with special chromosome coding is proposed.the algorithm is used to adjust the network model structure, the ability of early warning information assurance has been improved. Finally, the simulation results show the effectiveness of the proposed method.
Zhang, Qiao-Jia, Ye, Qing, Li, Liang, Liu, Si-jie, Chen, Kai-qiang.  2021.  An efficient selective encryption scheme for HEVC based on hyperchaotic Lorenz system. 2021 IEEE 5th Advanced Information Technology, Electronic and Automation Control Conference (IAEAC). 5:683—690.
With the wide application of video information, the protection of video information from illegal access has been widely investigated recently. An efficient selective encryption scheme for high efficiency video coding (HEVC) based on hyperchaotic Lorenz system is proposed. Firstly, the hyperchaotic Lorenz system is discretized and the generated chaotic state values are converted into chaotic pseudorandom sequences for encryption. The important syntax elements in HEVC are then selectively encrypted with the generated stream cipher. The experimental results show that the encrypted video is highly disturbed and the video information cannot be recognized. Through the analysis of objective index results, it is shown that the scheme is both efficient and security.
Andres Lara-Nino, Carlos, Diaz-Perez, Arturo, Morales-Sandoval, Miguel.  2021.  A comparison of Differential Addition and Doubling in Binary Edwards Curves for Elliptic Curve Cryptography. 2021 Fifth World Conference on Smart Trends in Systems Security and Sustainability (WorldS4). :12—18.
Binary Edwards curves (BEC) over finite fields can be used as an additive cyclic elliptic curve group to enable elliptic curve cryptography (ECC), where the most time consuming is scalar multiplication. This operation is computed by means of the group operation, either point addition or point doubling. The most notorious property of these curves is that their group operation is complete, which mitigates the need to verify for special cases. Different formulae for the group operation in BECs have been reported in the literature. Of particular interest are those designed to work with the differential properties of the Montgomery ladder, which offer constant time computation of the scalar multiplication as well as reduced field operations count. In this work, we review and compare the complexity of BEC differential addition and doubling in terms of field operations. We also provide software implementations of scalar multiplications which employ these formulae under a fair scenario. Our work provides insights on the advantages of using BECs in ECC. Our study of the different formulae for group addition in BEC also showcases the advantages and limitations of the different design strategies employed in each case.
Hu, Yifang, He, Jianjun, Xu, Luyao.  2021.  Infrared and Visible Image Fusion Based on Multiscale Decomposition with Gaussian and Co-Occurrence Filters. 2021 4th International Conference on Pattern Recognition and Artificial Intelligence (PRAI). :46—50.
The fusion of infrared and visible images using traditional multi-scale decomposition methods often leads to the loss of detailed information or the blurring of image edges, which is because the contour information and the detailed information within the contour cannot be retained simultaneously in the fusion process. To obtain high-quality fused images, a hybrid multi-scale decomposition fusion method using co-occurrence and Gaussian filters is proposed in this research. At first, by making full use of the smoothing effect of the Gaussian filter and edge protection characteristic of the co-occurrence filter, source images are decomposed into multiple hierarchical structures with different characteristics. Then, characteristics of sub-images at each level are analyzed, and the corresponding fusion rules are designed for images at different levels. At last, the final fused image obtained by combining fused sub-images of each level has rich scene information and clear infrared targets. Compared with several traditional multi-scale fusion algorithms, the proposed method has great advantages in some objective evaluation indexes.
Tushar, Venkataramanan, V., Srivastava, A., Hahn, A..  2020.  CP-TRAM: Cyber-Physical Transmission Resiliency Assessment Metric. IEEE Transactions on Smart Grid. 11:5114—5123.
Natural disasters and cyber intrusions threaten the normal operation of the critical electric grid infrastructure. There is still no widely accepted methodology to quantify the resilience in power systems. In this work, power system resiliency refers to the ability of the system to keep provide energy to the critical load even with adverse events. A significant amount of work has been done to quantify the resilience for distribution systems. Even though critical loads are located in distribution system, transmission system play a critical role in supplying energy to distribution feeder in addition to the Distributed Energy Resources (DERs). This work focuses on developing a framework to quantify the resiliency of cyber-physical transmission systems. Quantifying the resiliency of the transmission network, is important to determine and devise suitable control mechanisms to minimize the effects of undesirable events in the power grid. The proposed metric is based on both system infrastructure and with changing operating conditions. A graphical analysis along with measure of critical parameters of the network is performed to quantify the redundancy and vulnerabilities in the physical network of the system. A similar approach is used to quantify the cyber-resiliency. The results indicate the capability of the proposed framework to quantify cyber-physical resilience of the transmission systems.
Conference Name: IEEE Transactions on Smart Grid
Tronchin, Davide, Francescon, Roberto, Campagnaro, Filippo, Signori, Alberto, Petroccia, Roberto, Pelekanakis, Konstantinos, Paglierani, Pietro, Alves, João, Zorzi, Michele.  2021.  A Secure Cross-Layer Communication Stack for Underwater Acoustic Networks. OCEANS 2021: San Diego – Porto. :1–8.
Underwater Acoustic Networks (UANs) have long been recognized as an instrumental technology in various fields, from ocean monitoring to defense settings. Their security, though, has been scarcely investigated despite the strategic areas involved and the intrinsic vulnerability due to the broadcast nature of the wireless medium. In this work, we focus on attacks for which the attacker has partial or total knowledge of the network protocol stack. Our strategy uses a watchdog layer that allows upper layers to gather knowledge of overheard packets. In addition, a reputation system that is able to label nodes as trustful or suspicious is analyzed and evaluated via simulations. The proposed security mechanism has been implemented in the DESERT Underwater framework and a simulation study is conducted to validate the effectiveness of the proposed solution against resource exhaustion and sinkhole attacks.
Raj, Mariam, Tahir, Shahzaib, Khan, Fawad, Tahir, Hasan, Zulkifl, Zeeshan.  2021.  A Novel Fog-based Framework for Preventing Cloud Lock-in while Enabling Searchable Encryption. 2021 International Conference on Digital Futures and Transformative Technologies (ICoDT2). :1—6.
Cloud computing has helped in managing big data and providing resources remotely and ubiquitously, but it has some latency and security concerns. Fog has provided tremendous advantages over cloud computing which include low latency rate, improved real-time interactions, reduced network traffic overcrowding, and improved reliability, however, security concerns need to be addressed separately. Another major issue in the cloud is Cloud Lock-in/Vendor Lock-in. Through this research, an effort has been made to extend fog computing and Searchable Encryption technologies. The proposed system can reduce the issue of cloud lock-in faced in traditional cloud computing. The SE schemes used in this paper are Symmetric Searchable Encryption (SSE) and Multi-keyword Ranked Searchable Encryption (MRSE) to achieve confidentiality, privacy, fine-grained access control, and efficient keyword search. This can help to achieve better access control and keyword search simultaneously. An important use of this technique is it helps to prevent the issue of cloud/vendor lock-in. This can shift some computation and storage of index tables over fog nodes that will reduce the dependency on Cloud Service Providers (CSPs).
Hirano, Takato, Kawai, Yutaka, Koseki, Yoshihiro.  2021.  DBMS-Friendly Searchable Symmetric Encryption: Constructing Index Generation Suitable for Database Management Systems. 2021 IEEE Conference on Dependable and Secure Computing (DSC). :1—8.
Searchable symmetric encryption enables users with the secret key to conduct keyword search on encrypted data without decryption. Recently, dynamic searchable symmetric encryption (DSSE) which provides secure functionalities for adding or deleting documents has been studied extensively. Many DSSE schemes construct indexes in order to efficiently conduct keyword search. On the other hand, the indexes constructed in DSSE are complicated and independent to indexes supported by database management systems (DBMSs). Plug-in developments over DBMSs are often restricted, and therefore it is not easy to develop softwares which can deploy DSSE schemes to DBMSs. In this paper, we propose a DBMS-friendly searchable symmetric encryption scheme which can generate indexes suitable for DBMSs. Our index can narrow down encrypted data which should be conducted keyword search, and be combined with well-used indexes supported by many DBMSs. Our index consists of a small portion of an output value of a cryptographic deterministic function (e.g. pseudo-random function or hash function). We also show an experiment result of our scheme deployed to DBMSs.
Gattineni, Pradeep, Dharan, G.R Sakthi.  2021.  Intrusion Detection Mechanisms: SVM, random forest, and extreme learning machine (ELM). 2021 Third International Conference on Inventive Research in Computing Applications (ICIRCA). :273–276.
Intrusion detection method cautions and through build recognition rate. Through determine worries forth execution support vector machine (SVM), multilayer perceptron and different procedures have endured utilized trig ongoing work. Such strategies show impediments & persist not effective considering use trig enormous informational indexes, considering example, outline & system information. Interruption recognition outline utilized trig examining colossal traffic information; consequently, a proficient grouping strategy important through beat issue. Aforementioned issue considered trig aforementioned paper. Notable AI methods, specifically, SVM, arbitrary backwoods, & extreme learning machine (ELM) persist applied. These procedures persist notable trig view epithetical their capacity trig characterization. NSL-information revelation & knowledge mining informational collection components. Outcomes demonstrate a certain ELM beats different methodologies.
Ramadhan, Hani, Kwon, Joonho.  2021.  Enhancing Learned Index for A Higher Recall Trajectory K-Nearest Neighbor Search. 2021 IEEE International Conference on Big Data (Big Data). :6006—6007.
Learned indices can significantly shorten the query response time of k-Nearest Neighbor search of points data. However, extending the learned index for k-Nearest Neighbor search of trajectory data may return incorrect results (low recall) and require longer pruning time. Thus, we introduce an enhancement for trajectory learned index which is a pruning step for a learned index to retrieve the k-Nearest Neighbors correctly by learning the query workload. The pruning utilizes a predicted range query that covers the correct neighbors. We show that that our approach has the potential to work effectively in a large real-world trajectory dataset.
Lee, Sungwon, Ha, Jeongwon, Seo, Junho, Kim, Dongkyun.  2021.  Avoiding Content Storm Problem in Named Data Networking. 2021 Twelfth International Conference on Ubiquitous and Future Networks (ICUFN). :126–128.
Recently, methods are studied to overcome various problems for Named Data Networking(NDN). Among them, a new method which can overcome content storm problem is required to reduce network congestion and deliver content packet to consumer reliably. According to the various studies, the content storm problems could be overcame by scoped interest flooding. However, because these methods do not considers not only network congestion ratio but also the number another different paths, the correspond content packets could be transmitted unnecessary and network congestion could be worse. Therefore, in this paper, we propose a new content forwarding method for NDN to overcome the content storm problem. In the proposed method, if the network is locally congested and another paths are generated, an intermediate node could postpone or withdraw the content packet transmission to reduce congestion.
Kai, Yun, Qiang, Huang, Yixuan, Ma.  2021.  Construction of Network Security Perception System Using Elman Neural Network. 2021 2nd International Conference on Computer Communication and Network Security (CCNS). :187—190.
The purpose of the study is to improve the security of the network, and make the state of network security predicted in advance. First, the theory of neural networks is studied, and its shortcomings are analyzed by the standard Elman neural network. Second, the layers of the feedback nodes of the Elman neural network are improved according to the problems that need to be solved. Then, a network security perception system based on GA-Elman (Genetic Algorithm-Elman) neural network is proposed to train the network by global search method. Finally, the perception ability is compared and analyzed through the model. The results show that the model can accurately predict network security based on the experimental charts and corresponding evaluation indexes. The comparative experiments show that the GA-Elman neural network security perception system has a better prediction ability. Therefore, the model proposed can be used to predict the state of network security and provide early warnings for network security administrators.
Wang, Xiangyu, Ma, Jianfeng, Liu, Ximeng, Deng, Robert H., Miao, Yinbin, Zhu, Dan, Ma, Zhuoran.  2020.  Search Me in the Dark: Privacy-preserving Boolean Range Query over Encrypted Spatial Data. IEEE INFOCOM 2020 - IEEE Conference on Computer Communications. :2253–2262.
With the increasing popularity of geo-positioning technologies and mobile Internet, spatial keyword data services have attracted growing interest from both the industrial and academic communities in recent years. Meanwhile, a massive amount of data is increasingly being outsourced to cloud in the encrypted form for enjoying the advantages of cloud computing while without compromising data privacy. Most existing works primarily focus on the privacy-preserving schemes for either spatial or keyword queries, and they cannot be directly applied to solve the spatial keyword query problem over encrypted data. In this paper, we study the challenging problem of Privacy-preserving Boolean Range Query (PBRQ) over encrypted spatial databases. In particular, we propose two novel PBRQ schemes. Firstly, we present a scheme with linear search complexity based on the space-filling curve code and Symmetric-key Hidden Vector Encryption (SHVE). Then, we use tree structures to achieve faster-than-linear search complexity. Thorough security analysis shows that data security and query privacy can be guaranteed during the query process. Experimental results using real-world datasets show that the proposed schemes are efficient and feasible for practical applications, which is at least ×70 faster than existing techniques in the literature.
ISSN: 2641-9874
Song, Fuyuan, Qin, Zheng, Zhang, Jixin, Liu, Dongxiao, Liang, Jinwen, Shen, Xuemin Sherman.  2020.  Efficient and Privacy-preserving Outsourced Image Retrieval in Public Clouds. GLOBECOM 2020 - 2020 IEEE Global Communications Conference. :1–6.
With the proliferation of cloud services, cloud-based image retrieval services enable large-scale image outsourcing and ubiquitous image searching. While enjoying the benefits of the cloud-based image retrieval services, critical privacy concerns may arise in such services since they may contain sensitive personal information. In this paper, we propose an efficient and Privacy-Preserving Image Retrieval scheme with Key Switching Technique (PPIRS). PPIRS utilizes the inner product encryption for measuring Euclidean distances between image feature vectors and query vectors in a privacy-preserving manner. Due to the high dimension of the image feature vectors and the large scale of the image databases, traditional secure Euclidean distance comparison methods provide insufficient search efficiency. To prune the search space of image retrieval, PPIRS tailors key switching technique (KST) for reducing the dimension of the encrypted image feature vectors and further achieves low communication overhead. Meanwhile, by introducing locality sensitive hashing (LSH), PPIRS builds efficient searchable indexes for image retrieval by organizing similar images into a bucket. Security analysis shows that the privacy of both outsourced images and queries are guaranteed. Extensive experiments on a real-world dataset demonstrate that PPIRS achieves efficient image retrieval in terms of computational cost.
ISSN: 2576-6813
Liu, Zepeng, Xiao, Shiwu, Dong, Huanyu.  2021.  Identification of Transformer Magnetizing Inrush Current Based on Empirical Mode Decomposition. 2021 IEEE 4th International Electrical and Energy Conference (CIEEC). :1–6.
Aiming at the fact that the existing feature quantities cannot well identify the magnetizing inrush current during remanence and bias and the huge number of feature quantities, a new identification method using empirical mode decomposition energy index and artificial intelligence algorithm is proposed in 'this paper. Decomposition and denoising are realized through empirical mode decomposition, and then the corresponding energy index is obtained for the waveform of each inherent modal component and simplified by the mean impact value method. Finally, the accuracy of prediction using artificial intelligence algorithm is close to 100%. This reflects the practicality of the method proposed in 'this article.
Zhang, Fengqing, Jiang, Xiaoning.  2021.  The Zero Trust Security Platform for Data Trusteeship. 2021 4th International Conference on Advanced Electronic Materials, Computers and Software Engineering (AEMCSE). :1014–1017.
Cloud storage is a low-cost and convenient storage method, but the nature of cloud storage determines the existence of security risks for data uploaded by users. In order to ensure the security of users' data in third-party cloud platforms, a zero trust security platform for data trusteeship is proposed. The platform introduces the concept of zero trust, which meets the needs of users to upload sensitive data to untrusted third-party cloud platforms by implementing multiple functional modules such as sensitivity analysis service, cipher index service, attribute encryption service.
Piazza, Nancirose.  2020.  Classification Between Machine Translated Text and Original Text By Part Of Speech Tagging Representation. 2020 IEEE 7th International Conference on Data Science and Advanced Analytics (DSAA). :739–740.
Classification between machine-translated text and original text are often tokenized on vocabulary of the corpi. With N-grams larger than uni-gram, one can create a model that estimates a decision boundary based on word frequency probability distribution; however, this approach is exponentially expensive because of high dimensionality and sparsity. Instead, we let samples of the corpi be represented by part-of-speech tagging which is significantly less vocabulary. With less trigram permutations, we can create a model with its tri-gram frequency probability distribution. In this paper, we explore less conventional ways of approaching techniques for handling documents, dictionaries, and the likes.
Arunagirinathan, Paranietharan, Venayagamoorthy, Ganesh K..  2020.  Situational Awareness of Power System Stabilizers’ Performance in Energy Control Centers. 2020 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE). :1–8.
Undamped power system oscillations are detrimental to stable and security of the electric grid. Historically, poorly damped low frequency rotor oscillations have caused system blackouts or brownouts. It is required to monitor the oscillation damping controllers such as power system stabilizers' (PSS) performance at energy control centers as well as at power plant control centers. Phasor measurement units (PMUs) based time response and frequency response information on PSS performance is collected. A fuzzy logic system is developed to combine the time and frequency response information to derive the situational awareness on PSS performance on synchronous generator's oscillation(s). A two-area four-machine benchmark power system is simulated on a real-time digital simulator platform. Fuzzy logic system developed is evaluated for different system disturbances. Situational awareness on PSS performance on synchronous generator's oscillation(s) allows the control center operator to enhance the power system operation more stable and secure.
Tavakolan, Mona, Faridi, Ismaeel A..  2020.  Applying Privacy-Aware Policies in IoT Devices Using Privacy Metrics. 2020 International Conference on Communications, Computing, Cybersecurity, and Informatics (CCCI). :1–5.
In recent years, user's privacy has become an important aspect in the development of Internet of Things (IoT) devices. However, there has been comparatively little research so far that aims to understanding user's privacy in connection with IoT. Many users are worried about protecting their personal information, which may be gathered by IoT devices. In this paper, we present a new method for applying the user's preferences within the privacy-aware policies in IoT devices. Users can prioritize a set of extendable privacy policies based on their preferences. This is achieved by assigning weights to these policies to form ranking criteria. A privacy-aware index is then calculated based on these ranking. In addition, IoT devices can be clustered based on their privacy-aware index value. In this paper, we present a new method for applying the user's preferences within the privacy-aware policies in IoT devices. Users can prioritize a set of extendable privacy policies based on their preferences. This is achieved by assigning weights to these policies to form ranking criteria. A privacy-aware index is then calculated based on these ranking. In addition, IoT devices can be clustered based on their privacy-aware index value.
Zhang, Yaofang, Wang, Bailing, Wu, Chenrui, Wei, Xiaojie, Wang, Zibo, Yin, Guohua.  2020.  Attack Graph-Based Quantitative Assessment for Industrial Control System Security. 2020 Chinese Automation Congress (CAC). :1748–1753.
Industrial control systems (ICSs) are facing serious security challenges due to their inherent flaws, and emergence of vulnerabilities from the integration with commercial components and networks. To that end, assessing the security plays a vital role for current industrial enterprises which are responsible for critical infrastructure. This paper accomplishes a complex task of quantitative assessment based on attack graphs in order to look forward critical paths. For the purpose of application to a large-scale heterogeneous ICSs, we propose a flexible attack graph generation algorithm is proposed with the help of the graph data model. Hereafter, our quantitative assessment takes a consideration of graph indicators on specific nodes and edges to get the security metrics. In order to improve results of obtaining the critical attack path, we introduced a formulating selection rule, considering the asset value of industrial control devices. The experimental results show validation and verification of the proposed method.
Hu, Hongsheng, Dobbie, Gillian, Salcic, Zoran, Liu, Meng, Zhang, Jianbing, Zhang, Xuyun.  2020.  A Locality Sensitive Hashing Based Approach for Federated Recommender System. 2020 20th IEEE/ACM International Symposium on Cluster, Cloud and Internet Computing (CCGRID). :836–842.
The recommender system is an important application in big data analytics because accurate recommendation items or high-valued suggestions can bring high profit to both commercial companies and customers. To make precise recommendations, a recommender system often needs large and fine-grained data for training. In the current big data era, data often exist in the form of isolated islands, and it is difficult to integrate the data scattered due to privacy security concerns. Moreover, privacy laws and regulations make it harder to share data. Therefore, designing a privacy-preserving recommender system is of paramount importance. Existing privacy-preserving recommender system models mainly adapt cryptography approaches to achieve privacy preservation. However, cryptography approaches have heavy overhead when performing encryption and decryption operations and they lack a good level of flexibility. In this paper, we propose a Locality Sensitive Hashing (LSH) based approach for federated recommender system. Our proposed efficient and scalable federated recommender system can make full use of multiple source data from different data owners while guaranteeing preservation of privacy of contributing parties. Extensive experiments on real-world benchmark datasets show that our approach can achieve both high time efficiency and accuracy under small privacy budgets.
Beyza, Jesus, Bravo, Victor M., Garcia-Paricio, Eduardo, Yusta, Jose M., Artal-Sevil, Jesus S..  2020.  Vulnerability and Resilience Assessment of Power Systems: From Deterioration to Recovery via a Topological Model based on Graph Theory. 2020 IEEE International Autumn Meeting on Power, Electronics and Computing (ROPEC). 4:1–6.
Traditionally, vulnerability is the level of degradation caused by failures or disturbances, and resilience is the ability to recover after a high-impact event. This paper presents a topological procedure based on graph theory to evaluate the vulnerability and resilience of power grids. A cascading failures model is developed by eliminating lines both deliberately and randomly, and four restoration strategies inspired by the network approach are proposed. In the two cases, the degradation and recovery of the electrical infrastructure are quantified through four centrality measures. Here, an index called flow-capacity is proposed to measure the level of network overload during the iterative processes. The developed sequential framework was tested on a graph of 600 nodes and 1196 edges built from the 400 kV high-voltage power system in Spain. The conclusions obtained show that the statistical graph indices measure different topological aspects of the network, so it is essential to combine the results to obtain a broader view of the structural behaviour of the infrastructure.
Zheng, Zhihao, Cao, Zhenfu, Shen, Jiachen.  2020.  Practical and Secure Circular Range Search on Private Spatial Data. 2020 IEEE 19th International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom). :639–645.
With the location-based services (LBS) booming, the volume of spatial data inevitably explodes. In order to reduce local storage and computational overhead, users tend to outsource data and initiate queries to the cloud. However, sensitive data or queries may be compromised if cloud server has access to raw data and plaintext token. To cope with this problem, searchable encryption for geometric range is applied. Geometric range search has wide applications in many scenarios, especially the circular range search. In this paper, a practical and secure circular range search scheme (PSCS) is proposed to support searching for spatial data in a circular range. With our scheme, a semi-honest cloud server will return data for a given circular range correctly without uncovering index privacy or query privacy. We propose a polynomial split algorithm which can decompose the inner product calculation neatly. Then, we define the security of our PSCS formally and prove that it is secure under same-closeness-pattern chosen-plaintext attacks (CLS-CPA) in theory. In addition, we demonstrate the efficiency and accuracy through analysis and experiments compared with existing schemes.
Xiong, Xiaoping, Sun, Di, Hao, Shaolei, Lin, Guangyang, Li, Hang.  2020.  Detection of False Data Injection Attack Based on Improved Distortion Index Method. 2020 IEEE 20th International Conference on Communication Technology (ICCT). :1161—1168.
With the advancement of communication technology, the interoperability of the power grid operation has improved significantly, but due to its dependence on the communication system, it is extremely vulnerable to network attacks. Among them, the false data injection attack utilizes the loophole of bad data detection in the system and attacks the state estimation system, resulting in frequent occurrence of abnormal data in the system, which brings great harm to the power grid. In view of the fact that false data injection attacks are easy to avoid traditional bad data detection methods, this paper analyzes the different situations of false data injection attacks based on the characteristics of the power grid. Firstly, it proposes to apply the distortion index method to false data injection attack detection. Experiments prove that the detection results are good and can be complementary to traditional detection methods. Then, combined with the traditional normalized residual method, this paper proposes the improved distortion index method based on the distortion index, which is good at detecting abnormal data. The use of improved distortion index method to detect false data injection attacks can make up for the defect of the lack of universality of traditional detection methods, and meet the requirements of anomaly detection efficiency. Finally, based on the MATLAB power simulation test system, experimental simulation is carried out to verify the effectiveness and universality of the proposed method for false data injection attack detection.
Mouris, Dimitris, Georgios Tsoutsos, Nektarios.  2020.  Pythia: Intellectual Property Verification in Zero-Knowledge. 2020 57th ACM/IEEE Design Automation Conference (DAC). :1–6.
The contemporary IC supply chain depends heavily on third-party intellectual property (3PIP) that is integrated to in-house designs. As the correctness of such 3PIPs should be verified before integration, one important challenge for 3PIP vendors is proving the functionality of their designs while protecting the privacy of circuit implementations. In this work, we present Pythia that employs zero-knowledge proofs to enable vendors convince integrators about the functionality of a circuit without disclosing its netlist. Pythia automatically encodes netlists into zero knowledge-friendly format, evaluates them on different inputs, and proves correctness of outputs. We evaluate Pythia using the ISCAS'85 benchmark suite.