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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.
Sun, Weiqi, Li, Yuanlong, Shi, Liangren.  2020.  The Performance Evaluation and Resilience Analysis of Supply Chain Based on Logistics Network. 2020 39th Chinese Control Conference (CCC). :5772—5777.
With the development of globalization, more and more enterprises are involved in the supply chain network with increasingly complex structure. In this paper, enterprises and relations in the logistics network are abstracted as nodes and edges of the complex network. A graph model for a supply chain network to specified industry is constructed, and the Neo4j graph database is employed to store the graph data. This paper uses the theoretical research tool of complex network to model and analyze the supply chain, and designs a supply chain network evaluation system which include static and dynamic measurement indexes according to the statistical characteristics of complex network. In this paper both the static and dynamic resilience characteristics of the the constructed supply chain network are evaluated from the perspective of complex network. The numeric experimental simulations are conducted for validation. This research has practical and theoretical significance for enterprises to make strategies to improve the anti-risk capability of supply chain network based on logistics network information.
AKCENGİZ, Ziya, Aslan, Melis, Karabayır, Özgür, Doğanaksoy, Ali, Uğuz, Muhiddin, Sulak, Fatih.  2020.  Statistical Randomness Tests of Long Sequences by Dynamic Partitioning. 2020 International Conference on Information Security and Cryptology (ISCTURKEY). :68—74.
Random numbers have a wide usage in the area of cryptography. In practice, pseudo random number generators are used in place of true random number generators, as regeneration of them may be required. Therefore because of generation methods of pseudo random number sequences, statistical randomness tests have a vital importance. In this paper, a randomness test suite is specified for long binary sequences. In literature, there are many randomness tests and test suites. However, in most of them, to apply randomness test, long sequences are partitioned into a certain fixed length and the collection of short sequences obtained is evaluated instead. In this paper, instead of partitioning a long sequence into fixed length subsequences, a concept of dynamic partitioning is introduced in accordance with the random variable in consideration. Then statistical methods are applied. The suggested suite, containing four statistical tests: Collision Tests, Weight Test, Linear Complexity Test and Index Coincidence Test, all of them work with the idea of dynamic partitioning. Besides the adaptation of this approach to randomness tests, the index coincidence test is another contribution of this work. The distribution function and the application of all tests are given in the paper.
Zhang, Chi, Chen, Jinfu, Cai, Saihua, Liu, Bo, Wu, Yiming, Geng, Ye.  2020.  iTES: Integrated Testing and Evaluation System for Software Vulnerability Detection Methods. 2020 IEEE 19th International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom). :1455–1460.
To find software vulnerabilities using software vulnerability detection technology is an important way to ensure the system security. Existing software vulnerability detection methods have some limitations as they can only play a certain role in some specific situations. To accurately analyze and evaluate the existing vulnerability detection methods, an integrated testing and evaluation system (iTES) is designed and implemented in this paper. The main functions of the iTES are:(1) Vulnerability cases with source codes covering common vulnerability types are collected automatically to form a vulnerability cases library; (2) Fourteen methods including static and dynamic vulnerability detection are evaluated in iTES, involving the Windows and Linux platforms; (3) Furthermore, a set of evaluation metrics is designed, including accuracy, false positive rate, utilization efficiency, time cost and resource cost. The final evaluation and test results of iTES have a good guiding significance for the selection of appropriate software vulnerability detection methods or tools according to the actual situation in practice.
Liu, Xinlin, Huang, Jianhua, Luo, Weifeng, Chen, Qingming, Ye, Peishan, Wang, Dingbo.  2020.  Research on Attack Mechanism using Attack Surface. 2020 IEEE International Conference on Artificial Intelligence and Computer Applications (ICAICA). :137–141.
A approach to research on the attack mechanism designs through attack surface technology due to the complexity of the attack mechanism. The attack mechanism of a mimic architecture is analyzed in a relative way using attack surface metrics to indicate whether mimic architectures are safer than non-mimic architectures. The definition of the architectures attack surface in terms of the mimic brackets along three abstract dimensions referenced the system attack surface. The larger the attack surface, the more likely the architecture will be attacked.
Song, X., Dong, C., Yuan, D., Xu, Q., Zhao, M..  2020.  Forward Private Searchable Symmetric Encryption with Optimized I/O Efficiency. IEEE Transactions on Dependable and Secure Computing. 17:912–927.
Recently, several practical attacks raised serious concerns over the security of searchable encryption. The attacks have brought emphasis on forward privacy, which is the key concept behind solutions to the adaptive leakage-exploiting attacks, and will very likely to become a must-have property of all new searchable encryption schemes. For a long time, forward privacy implies inefficiency and thus most existing searchable encryption schemes do not support it. Very recently, Bost (CCS 2016) showed that forward privacy can be obtained without inducing a large communication overhead. However, Bost's scheme is constructed with a relatively inefficient public key cryptographic primitive, and has poor I/O performance. Both of the deficiencies significantly hinder the practical efficiency of the scheme, and prevent it from scaling to large data settings. To address the problems, we first present FAST, which achieves forward privacy and the same communication efficiency as Bost's scheme, but uses only symmetric cryptographic primitives. We then present FASTIO, which retains all good properties of FAST, and further improves I/O efficiency. We implemented the two schemes and compared their performance with Bost's scheme. The experiment results show that both our schemes are highly efficient.
Ferreira, B., Portela, B., Oliveira, T., Borges, G., Domingos, H. J., Leitao, J..  2020.  Boolean Searchable Symmetric Encryption with Filters on Trusted Hardware. IEEE Transactions on Dependable and Secure Computing. :1–1.
The prevalence and availability of cloud infrastructures has made them the de facto solution for storing and archiving data, both for organizations and individual users. Nonetheless, the cloud's wide spread adoption is still hindered by dependability and security concerns, particularly in applications with large data collections where efficient search and retrieval services are also major requirements. This leads to an increased tension between security, efficiency, and search expressiveness. In this paper we tackle this tension by proposing BISEN, a new provably-secure boolean searchable symmetric encryption scheme that improves these three complementary dimensions by exploring the design space of isolation guarantees offered by novel commodity hardware such as Intel SGX, abstracted as Isolated Execution Environments (IEEs). BISEN is the first scheme to support multiple users and enable highly expressive and arbitrarily complex boolean queries, with minimal information leakage regarding performed queries and accessed data, and verifiability regarding fully malicious adversaries. Furthermore, BISEN extends the traditional SSE model to support filter functions on search results based on generic metadata created by the users. Experimental validation and comparison with the state of art shows that BISEN provides better performance with enriched search semantics and security properties.
Ti, Y., Wu, C., Yu, C., Kuo, S..  2020.  Benchmarking Dynamic Searchable Symmetric Encryption Scheme for Cloud-Internet of Things Applications. IEEE Access. 8:1715–1732.
Recently, the rapid development of Internet of things (IoT) has resulted in the generation of a considerable amount of data, which should be stored. Therefore, it is necessary to develop methods that can easily capture, save, and modify these data. The data generated using IoT contain private information; therefore sufficient security features should be incorporated to ensure that potential attackers cannot access the data. Researchers from various fields are attempting to achieve data security. One of the major challenges is that IoT is a paradigm of how each device in the Internet infrastructure is interconnected to a globally dynamic network. When searching in dynamic cloud-stored data, sensitive data can be easily leaked. IoT data storage and retrieval from untrusted cloud servers should be secure. Searchable symmetric encryption (SSE) is a vital technology in the field of cloud storage. SSE allows users to use keywords to search for data in an untrusted cloud server but the keywords and the data content are concealed from the server. However, an SSE database is seldom used by cloud operators because the data stored on the cloud server is often modified. The server cannot update the data without decryption because the data are encrypted by the user. Therefore, dynamic SSE (DSSE) has been developed in recent years to support the aforementioned requirements. Instead of decrypting the data stored by customers, DSSE adds or deletes encrypted data on the server. A number of DSSE systems based on linked list structures or blind storage (a new primitive) have been proposed. From the perspective of functionality, extensibility, and efficiency, these DSSE systems each have their own advantages and drawbacks. The most crucial aspect of a system that is used in the cloud industry is the trade-off between performance and security. Therefore, we compared the efficiency and security of multiple DSSE systems and identified their shortcomings to develop an improved system.
Cao, L., Kang, Y., Wu, Q., Wu, R., Guo, X., Feng, T..  2020.  Searchable encryption cloud storage with dynamic data update to support efficient policy hiding. China Communications. 17:153–163.
Ciphertext policy attribute based encryption (CP-ABE) can provide high finegrained access control for cloud storage. However, it needs to solve problems such as property privacy protection, ciphertext search and data update in the application process. Therefore, based on CP-ABE scheme, this paper proposes a dynamically updatable searchable encryption cloud storage (DUSECS) scheme. Using the characteristics of homomorphic encryption, the encrypted data is compared to achieve efficient hiding policy. Meanwhile, adopting linked list structure, the DUSECS scheme realizes the dynamic data update and integrity detection, and the search encryption against keyword guessing attacks is achieved by combining homomorphic encryption with aggregation algorithm. The analysis of security and performance shows that the scheme is secure and efficient.
Zhang, M., Chen, Y., Huang, J..  2020.  SE-PPFM: A Searchable Encryption Scheme Supporting Privacy-Preserving Fuzzy Multikeyword in Cloud Systems. IEEE Systems Journal. :1–9.
Cloud computing provides an appearing application for compelling vision in managing big-data files and responding queries over a distributed cloud platform. To overcome privacy revealing risks, sensitive documents and private data are usually stored in the clouds in a cipher-based manner. However, it is inefficient to search the data in traditional encryption systems. Searchable encryption is a useful cryptographic primitive to enable users to retrieve data in ciphertexts. However, the traditional searchable encryptions provide lower search efficiency and cannot carry out fuzzy multikeyword queries. To solve this issue, in this article, we propose a searchable encryption that supports privacy-preserving fuzzy multikeyword search (SE-PPFM) in cloud systems, which is built by asymmetric scalar-product-preserving encryptions and Hadamard product operations. In order to realize the functionality of efficient fuzzy searches, we employ Word2vec as the primitive of machine learning to obtain a fuzzy correlation score between encrypted data and queries predicates. We analyze and evaluate the performance in terms of token of multikeyword, retrieval and match time, file retrieval time and matching accuracy, etc. The experimental results show that our scheme can achieve a higher efficiency in fuzzy multikeyword ciphertext search and provide a higher accuracy in retrieving and matching procedure.
Guo, T., Zhou, R., Tian, C..  2020.  On the Information Leakage in Private Information Retrieval Systems. IEEE Transactions on Information Forensics and Security. 15:2999—3012.
We consider information leakage to the user in private information retrieval (PIR) systems. Information leakage can be measured in terms of individual message leakage or total leakage. Individual message leakage, or simply individual leakage, is defined as the amount of information that the user can obtain on any individual message that is not being requested, and the total leakage is defined as the amount of information that the user can obtain about all the other messages except the one being requested. In this work, we characterize the tradeoff between the minimum download cost and the individual leakage, and that for the total leakage, respectively. Coding schemes are proposed to achieve these optimal tradeoffs, which are also shown to be optimal in terms of the message size. We further characterize the optimal tradeoff between the minimum amount of common randomness and the total leakage. Moreover, we show that under individual leakage, common randomness is in fact unnecessary when there are more than two messages.
D’Alterio, P., Garibaldi, J. M., John, R. I..  2020.  Constrained Interval Type-2 Fuzzy Classification Systems for Explainable AI (XAI). 2020 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE). :1–8.
In recent year, there has been a growing need for intelligent systems that not only are able to provide reliable classifications but can also produce explanations for the decisions they make. The demand for increased explainability has led to the emergence of explainable artificial intelligence (XAI) as a specific research field. In this context, fuzzy logic systems represent a promising tool thanks to their inherently interpretable structure. The use of a rule-base and linguistic terms, in fact, have allowed researchers to create models that are able to produce explanations in natural language for each of the classifications they make. So far, however, designing systems that make use of interval type-2 (IT2) fuzzy logic and also give explanations for their outputs has been very challenging, partially due to the presence of the type-reduction step. In this paper, it will be shown how constrained interval type-2 (CIT2) fuzzy sets represent a valid alternative to conventional interval type-2 sets in order to address this issue. Through the analysis of two case studies from the medical domain, it is shown how explainable CIT2 classifiers are produced. These systems can explain which rules contributed to the creation of each of the endpoints of the output interval centroid, while showing (in these examples) the same level of accuracy as their IT2 counterpart.
Lei, X., Tu, G.-H., Liu, A. X., Xie, T..  2020.  Fast and Secure kNN Query Processing in Cloud Computing. 2020 IEEE Conference on Communications and Network Security (CNS). :1–9.
Advances in sensing and tracking technology lead to the proliferation of location-based services. Location service providers (LSPs) often resort to commercial public clouds to store the tremendous geospatial data and process location-based queries from data users. To protect the privacy of LSP's geospatial data and data user's query location against the untrusted cloud, they are required to be encrypted before sending to the cloud. Nevertheless, it is not easy to design a fast and secure location-based query processing scheme over the encrypted data. In this paper, we propose a Fast and Secure kNN (FSkNN) scheme to support secure k nearest neighbor (k NN) search in cloud computing. We reveal the inherent connection between an Sk NN protocol and a secure range query protocol and further describe how to construct FSkNN based on a secure range query protocol. FSkNN leverages a customized accuracy-assured strategy to ensure the result accuracy and adopts a data structure named random Bloom filter (RBF) to build a secure index for efficiently searching. We formally prove the security of FSkNN under the random oracle model. Our evaluation results show that FSkNN is highly practical.
Oliver, J., Ali, M., Hagen, J..  2020.  HAC-T and Fast Search for Similarity in Security. 2020 International Conference on Omni-layer Intelligent Systems (COINS). :1–7.
Similarity digests have gained popularity for many security applications like blacklisting/whitelisting, and finding similar variants of malware. TLSH has been shown to be particularly good at hunting similar malware, and is resistant to evasion as compared to other similarity digests like ssdeep and sdhash. Searching and clustering are fundamental tools which help the security analysts and security operations center (SOC) operators in hunting and analyzing malware. Current approaches which aim to cluster malware are not scalable enough to keep up with the vast amount of malware and goodware available in the wild. In this paper, we present techniques which allow for fast search and clustering of TLSH hash digests which can aid analysts to inspect large amounts of malware/goodware. Our approach builds on fast nearest neighbor search techniques to build a tree-based index which performs fast search based on TLSH hash digests. The tree-based index is used in our threshold based Hierarchical Agglomerative Clustering (HAC-T) algorithm which is able to cluster digests in a scalable manner. Our clustering technique can cluster digests in O (n logn) time on average. We performed an empirical evaluation by comparing our approach with many standard and recent clustering techniques. We demonstrate that our approach is much more scalable and still is able to produce good cluster quality. We measured cluster quality using purity on 10 million samples obtained from VirusTotal. We obtained a high purity score in the range from 0.97 to 0.98 using labels from five major anti-virus vendors (Kaspersky, Microsoft, Symantec, Sophos, and McAfee) which demonstrates the effectiveness of the proposed method.
Jin, Y., Tian, Z., Zhou, M., Wang, H..  2020.  MuTrack: Multiparameter Based Indoor Passive Tracking System Using Commodity WiFi. ICC 2020 - 2020 IEEE International Conference on Communications (ICC). :1—6.
Device-Free Localization and Tracking (DFLT) acts as a key component for the contactless awareness applications such as elderly care and home security. However, the random phase errors in WiFi signal and weak target echoes submerged in background clutter signals are mainly obstacles for current DFLT systems. In this paper, we propose the design and implementation of MuTrack, a multiparameter based DFLT system using commodity WiFi devices with a single link. Firstly, we select an antenna with maximum reliability index as the reference antenna for signal sanitization in which the conjugate operation removes the random phase errors. Secondly, we design a multi-dimensional parameters estimator and then refine path parameters by optimizing the complete data of path components. Finally, the Hungarian Kalman Filter based tracking method is proposed to derive accurate locations from low-resolution parameter estimates. We extensively validate the proposed system in typical indoor environment and these experimental results show that MuTrack can achieve high tracking accuracy with the mean error of 0.82 m using only a single link.
Av, N., Kumar, N. A..  2020.  Image Encryption Using Genetic Algorithm and Bit-Slice Rotation. 2020 11th International Conference on Computing, Communication and Networking Technologies (ICCCNT). :1–6.
Cryptography is a powerful means of delivering information in a secure manner. Over the years, many image encryption algorithms have been proposed based on the chaotic system to protect the digital image against cryptography attacks. In chaotic encryption, it jumbles the image to vary the framework of the image. This makes it difficult for the attacker to retrieve the original image. This paper introduces an efficient image encryption algorithm incorporating the genetic algorithm, bit plane slicing and bit plane rotation of the digital image. The digital image is sliced into eight planes and each plane is well rotated to give a fully encrypted image after the application of the Genetic Algorithm on each pixel of the image. This makes it less prone to attacks. For decryption, we perform the operations in the reverse order. The performance of this algorithm is measured using various similarity measures like Structural Similarity Index Measure (SSIM). The results exhibit that the proposed scheme provides a stronger level of encryption and an enhanced security level.
Doğu, S., Alidoustaghdam, H., Dilman, İ, Akıncı, M. N..  2020.  The Capability of Truncated Singular Value Decomposition Method for Through the Wall Microwave Imaging. 2020 IEEE Microwave Theory and Techniques in Wireless Communications (MTTW). 1:76–81.
In this study, a truncated singular value decomposition (TSVD) based computationally efficient through the wall imaging (TWI) is addressed. Mainly, two different scenarios with identical and non-identical multiple scatterers behind the wall have been considered. The scattered data are processed with special scheme in order to improve quality of the results and measurements are performed at four different frequencies. Next, effects of selecting truncation threshold in TSVD methods are analyzed and a detailed quantitative comparison is provided to demonstrate capabilities of these TSVD methods over selection of truncation threshold.
Gracy, S., Milošević, J., Sandberg, H..  2020.  Actuator Security Index for Structured Systems. 2020 American Control Conference (ACC). :2993–2998.
Given a network with a set of vulnerable actuators (and sensors), the security index of an actuator equals the minimum number of sensors and actuators that needs to be compromised so as to conduct a perfectly undetectable attack using the said actuator. This paper deals with the problem of computing actuator security indices for discrete-time LTI network systems, using a structured systems framework. We show that the actuator security index is generic, that is for almost all realizations the actuator security index remains the same. We refer to such an index as generic security index (generic index) of an actuator. Given that the security index quantifies the vulnerability of a network, the generic index is quite valuable for large scale energy systems. Our second contribution is to provide graph-theoretic conditions for computing the generic index. The said conditions are in terms of existence of linkings on appropriately-defined directed (sub)graphs. Based on these conditions, we present an algorithm for computing the generic index.
Sun, J., Ma, J., Quan, J., Zhu, X., I, C..  2019.  A Fuzzy String Matching Scheme Resistant to Statistical Attack. 2019 International Conference on Networking and Network Applications (NaNA). :396–402.
The fuzzy query scheme based on vector index uses Bloom filter to construct vector index for key words. Then the statistical attack based on the deviation of frequency distribution of the vector index brings out the sensitive information disclosure. Using the noise vector, a fuzzy query scheme resistant to the statistical attack serving for encrypted database, i.e. S-BF, is introduced. With the noise vector to clear up the deviation of frequency distribution of vector index, the statistical attacks to the vector index are resolved. Demonstrated by lab experiment, S-BF scheme can achieve the secure fuzzy query with the powerful privation protection capability for encrypted cloud database without the loss of fuzzy query efficiency.
Yadav, M. K., Gugal, D., Matkar, S., Waghmare, S..  2019.  Encrypted Keyword Search in Cloud Computing using Fuzzy Logic. 2019 1st International Conference on Innovations in Information and Communication Technology (ICIICT). :1–4.
Research and Development, and information management professionals routinely employ simple keyword searches or more complex Boolean queries when using databases such as PubMed and Ovid and search engines like Google to find the information they need. While satisfying the basic needs of the researcher, basic search is limited which can adversely affect both precision and recall, decreasing productivity and damaging the researchers' ability to discover new insights. The cloud service providers who store user's data may access sensitive information without any proper authority. A basic approach to save the data confidentiality is to encrypt the data. Data encryption also demands the protection of keyword privacy since those usually contain very vital information related to the files. Encryption of keywords protects keyword safety. Fuzzy keyword search enhances system usability by matching the files perfectly or to the nearest possible files against the keywords entered by the user based on similar semantics. Encrypted keyword search in cloud using this logic provides the user, on entering keywords, to receive best possible files in a more secured manner, by protecting the user's documents.