Biblio

Wireless Sensor Network (WSN)is a predominant technology that is widely used in many applications such as industrial sectors, defense, environment, habitat monitoring, medical fields etc., These applications are habitually delegated for observing sensitive and confidential raw data such as adversary position, movement in the battle field, location of personnel in a building, changes in environmental condition, regular medical updates from patient side to doctors or hospital control rooms etc., Security becomes inevitable in WSN and providing security is being truly intricate because of in-built nature of WSN which is assailable to attacks easily. Node involved in WSN need to route the data to the neighboring nodes wherein any attack in the node could lead to fiasco. Of late trust mechanisms have been considered to be an ideal solution that can mitigate security problems in WSN. This paper aims to investigate various existing trust-based Secure Routing (SR) protocols and mechanisms available for the wireless sensing connection. The concept of the present trust mechanism is also analyzed with respect to methodology, trust metric, pros, cons, and complexity involved. Finally, the security resiliency of various trust models against the attacks is also analyzed.

Data are the basis in Digital Twin (DT) to set up bidirectional mapping between physical and virtual spaces, and realize critical environmental sensing, decision making and execution. Thus, trustworthiness is a necessity in data content as well as data operations. A dual blockchain framework is proposed to realize comprehensive data security in various DT scenarios. It is highly adaptable, scalable, evolvable, and easy to be integrated into Digital Twin Network (DTN) as enhancement.

Spatial crowdsourcing has stimulated various new applications such as taxi calling and food delivery. A key enabler for these spatial crowdsourcing based applications is to plan routes for crowd workers to execute tasks given diverse requirements of workers and the spatial crowdsourcing platform. Despite extensive studies on task planning in spatial crowdsourcing, few have accounted for the location privacy of tasks, which may be misused by an untrustworthy platform. In this paper, we explore efficient task planning for workers while protecting the locations of tasks. Specifically, we define the Privacy-Preserving Task Planning (PPTP) problem, which aims at both total revenue maximization of the platform and differential privacy of task locations. We first apply the Laplacian mechanism to protect location privacy, and analyze its impact on the total revenue. Then we propose an effective and efficient task planning algorithm for the PPTP problem. Extensive experiments on both synthetic and real datasets validate the advantages of our algorithm in terms of total revenue and time cost.

In this paper, we propose a scheme that supports statistic query and authorized access control on an Encrypted Electronic Medical Records Databases(EMDB). Different from other schemes, it is based on Differential-Privacy(DP), which can protect the privacy of patients. By deploying an improved Multi-Authority Attribute-Based Encryption(MA-ABE) scheme, all authorities can distribute their search capability to clients under different authorities without additional negotiations. To our best knowledge, there are few studies on statistical queries on encrypted data. In this work, we consider that support differentially-private statistical queries. To improve search efficiency, we leverage the Bloom Filter(BF) to judge whether the keywords queried by users exists. Finally, we use experiments to verify and evaluate the feasibility of our proposed scheme.

The burgeoning networked computing devices create many distributed systems and generate new signals on a large scale. Many Internet of Things (IoT) applications, such as peer-to-peer streaming of multimedia data, crowdsourcing, and measurement by sensor networks, can be modeled as a form of big data. Processing massive data calls for new data structures and algorithms different from traditional ones designed for small-scale problems. For measurement from networked distributed systems, we consider an essential data format: signals on graphs. Due to limited computing resources, the sensor nodes in the distributed systems may outsource the computing tasks to third parties, such as cloud platforms, arising a severe concern on data privacy. A de-facto solution is to have third parties only process encrypted data. We propose a novel and efficient privacy-preserving secure outsourced computation protocol for denoising signals on the graph based on the information-theoretic secure multi-party computation (ITS-MPC). Denoising the data makes paths for further meaningful data processing. From experimenting with our algorithms in a testbed, the results indicate a better efficiency of our approach than a counterpart approach with computational security.

WebAssembly is an Increasingly popular lightweight binary instruction format, which can be efficiently embedded and sandboxed. Languages like C, C++, Rust, Go, and many others can be compiled into WebAssembly. This paper describes Twine, a WebAssembly trusted runtime designed to execute unmodified, language-independent applications. We leverage Intel SGX to build the runtime environment without dealing with language-specific, complex APIs. While SGX hardware provides secure execution within the processor, Twine provides a secure, sandboxed software runtime nested within an SGX enclave, featuring a WebAssembly system interface (WASI) for compatibility with unmodified WebAssembly applications. We evaluate Twine with a large set of general-purpose benchmarks and real-world applications. In particular, we used Twine to implement a secure, trusted version of SQLite, a well-known full-fledged embeddable database. We believe that such a trusted database would be a reasonable component to build many larger application services. Our evaluation shows that SQLite can be fully executed inside an SGX enclave via WebAssembly and existing system interface, with similar average performance overheads. We estimate that the performance penalties measured are largely compensated by the additional security guarantees and its full compatibility with standard WebAssembly. An indepth analysis of our results indicates that performance can be greatly improved by modifying some of the underlying libraries. We describe and implement one such modification in the paper, showing up to 4.1 × speedup. Twine is open-source, available at GitHub along with instructions to reproduce our experiments.

This paper presents an implementation of a novel approach, utilizing hybrid data partitioning, to secure sensitive data and improve query performance. In this novel approach, vertical and horizontal data partitioning are combined together in an approach that called hybrid partitioning and the new approach is implemented using Microsoft SQL server to generate divided/partitioned relations. A group of proposed rules is applied to the query request process using query binning (QB) and Metadata of partitioning. The proposed approach is validated using experiments involving a collection of data evaluated by outcomes of advanced stored procedures. The suggested approach results are satisfactory in achieving the properties of defining the data security: non-linkability and indistinguishability. The results of the proposed approach were satisfactory. The proposed novel approach outperforms a well-known approach called PANDA.

Order-preserving encryption (OPE) plays an important role in securing outsourced databases. OPE schemes can be either Stateless or Stateful. Stateful schemes can achieve the ideal security of order-preserving encryption, i.e., “reveal no information about the plaintexts besides order.” However, comparing to stateless schemes, stateful schemes require maintaining some state information locally besides encryption keys and the ciphertexts are mutable. On the other hand, stateless schemes only require remembering encryption keys and thus is more efficient. It is a common belief that stateless schemes cannot provide the same level of security as stateful ones because stateless schemes reveal the relative distance among their corresponding plaintext. In real world applications, such security defects may lead to the leakage of statistical and sensitive information, e.g., the data distribution, or even negates the whole encryption. In this paper, we propose a practical and secure stateless order-preserving encryption scheme. With prior knowledge of the data to be encrypted, our scheme can achieve IND-CCPA (INDistinguishability under Committed ordered Chosen Plaintext Attacks) security for static data set. Though the IND-CCPA security can't be met for dynamic data set, our new scheme can still significantly improve the security in real world applications. Along with the encryption scheme, in this paper we also provide methods to eliminate access pattern leakage in communications and thus prevents some common attacks to OPE schemes in practice.

Data confidentiality is an important requirement for clients when outsourcing databases to the cloud. Trusted execution environments, such as Intel SGX, offer an efficient solution to this confidentiality problem. However, existing TEE-based solutions are not optimized for column-oriented, in-memory databases and pose impractical memory requirements on the enclave. We present EncDBDB, a novel approach for client-controlled encryption of a column-oriented, in-memory databases allowing range searches using an enclave. EncDBDB offers nine encrypted dictionaries, which provide different security, performance, and storage efficiency tradeoffs for the data. It is especially suited for complex, read-oriented, analytic queries as present, e.g., in data warehouses. The computational overhead compared to plaintext processing is within a millisecond even for databases with millions of entries and the leakage is limited. Compressed encrypted data requires less space than a corresponding plaintext column. Furthermore, EncDBDB's enclave is very small reducing the potential for security-relevant implementation errors and side-channel leakages.

In the last decade the use of artificial neural networks (ANNs) in many fields like image processing or speech recognition has become a common practice because of their effectiveness to solve complex tasks. However, in such a rush, very little attention has been paid to security aspects. In this work we explore the possibility to embed a watermark into the ANN parameters. We exploit model redundancy and adaptation capacity to lock a subset of its parameters to carry the watermark sequence. The watermark can be extracted in a simple way to claim copyright on models but can be very easily attacked with model fine-tuning. To tackle this culprit we devise a novel watermark aware training strategy. We aim at delving into the loss landscape to find an optimal configuration of the parameters such that we are robust to fine-tuning attacks towards the watermarked parameters. Our experimental results on classical ANN models trained on well-known MNIST and CIFAR-10 datasets show that the proposed approach makes the embedded watermark robust to fine-tuning and compression attacks.

Logic encryption is a method to improve hardware security by inserting key gates on carefully selected signals in a logic design. Various logic encryption schemes have been proposed in the past decade. Many attack methods to thwart these logic locking schemes have also emerged. The satisfiability (SAT) attack can recover correct keys for many logic obfuscation methods. Recently proposed sensitivity analysis attack can decrypt stripped functionality based logic encryption schemes. This article presents a new encryption scheme named SRTLock, which is resilient against both attacks. SRTLock method first generates 0-injection circuits and encrypts the functionality of these nodes with the key inputs. In the next step, these values are used to control the sensitivity of the functionally stripped output for specific input patterns. The resultant locked circuit is resilient against the SAT and sensitivity analysis attacks. Experimental results demonstrating this on several attacks using standard benchmark circuits are presented.

Logic obfuscation has been proposed as a counter-measure against supply chain threats such as overproduction and IP piracy. However, the functional corruption it offers can be exploited by oracle-guided pruning attacks to recover the obfuscation key, forcing existing logic obfuscation methods to trivialize their output corruption which in turn leads to a diminished protection scope. In this paper, we address this quandary through an FSM obfuscation methodology that delivers obfuscation scope not only through external secrets but more importantly through inherent state transition patterns. We leverage a minimum-cut graph partitioning algorithm to divide the FSM diagram and implement the resulting partitions with distinct FF configurations, enabled by a novel synthesis methodology supporting reconfigurable FFs. The obfuscated FSM can be activated by invoking key values to dynamically switch the FF configuration at a small number of inter-partition transitions. Yet, the overall obfuscation scope comprises far more intra-partition transitions which are driven solely by the inherent transition sequences and thus reveal no key trace. We validate the security of the proposed obfuscation method against numerous functional and structural attacks. Experimental results confirm its delivery of extensive obfuscation scope at marginal overheads.

This project develops a face recognition-based door locking system with two-factor authentication using OpenCV. It uses Raspberry Pi 4 as the microcontroller. Face recognition-based door locking has been around for many years, but most of them only provide face recognition without any added security features, and they are costly. The design of this project is based on human face recognition and the sending of a One-Time Password (OTP) using the Twilio service. It will recognize the person at the front door. Only people who match the faces stored in its dataset and then inputs the correct OTP will have access to unlock the door. The Twilio service and image processing algorithm Local Binary Pattern Histogram (LBPH) has been adopted for this system. Servo motor operates as a mechanism to access the door. Results show that LBPH takes a short time to recognize a face. Additionally, if an unknown face is detected, it will log this instance into a "Fail" file and an accompanying CSV sheet.

With the development in IC technology, testing the designs is becoming more and more complex. In the design, process testing consumes 60-80% of the time. The basic testing principle is providing the circuit under test (CUT) with input patterns, observing output responses, and comparing against the desired response called the golden response. As the density of the device are rising leads to difficulty in examining the sub-circuit of the chip. So, testing of design is becoming a time-consuming and costly process. Attaching additional logic to the circuit resolves the issue by testing itself. BIST is a relatively a design for testability technique to facilitate thorough testing of ICs and it comprises the test pattern generator, circuit under test, and output response analyzer. Quick diagnosis and very high fault coverage can be ensured by BIST. As complexity in the circuit is increasing, testing urges TPGs (Test Pattern Generators) to generate the test patterns for the CUT to sensitize the faults. TPGs are vulnerable to malicious activities such as scan-based side-channel attacks. Secret data saved on the chip can be extracted by an attacker by scanning out the test outcomes. These threats lead to the emergence of securing TPGs. This work demonstrates providing a secured test pattern generator for BIST circuits by locking the logic of TPG with a password or key generated by the key generation circuit. Only when the key is provided test patterns are generated. This provides versatile protection to TPG from malicious attacks such as scan-based side-channel attacks, Intellectual Property (IP) privacy, and IC overproduction.

The ever-increasing cost and complexity of cutting-edge manufacturing and test processes have migrated the semiconductor industry towards a globalized business model. With many untrusted entities involved in the supply chain located across the globe, original intellectual property (IP) owners face threats such as IP theft/piracy, tampering, counterfeiting, reverse engineering, and overproduction. Logic locking has emerged as a promising solution to protect integrated circuits (ICs) against supply chain vulnerabilities. It inserts key gates to corrupt circuit functionality for incorrect key inputs. A logic-locked chip test can be performed either before or after chip activation (becoming unlocked) by loading the unlocking key into the on-chip tamperproof memory. However, both pre-activation and post-activation tests suffer from lower test coverage, higher test cost, and critical security vulnerabilities. To address the shortcomings, we propose LL-ATPG, a logic-locking aware test method that applies a set of valet (dummy) keys based on a target test coverage to perform manufacturing test in an untrusted environment. LL-ATPG achieves high test coverage and minimizes test time overhead when testing the logic-locked chip before activation without sharing the unlocking key. We perform security analysis of LL-ATPG and experimentally demonstrate that sharing the valet keys with the untrusted foundry does not create additional vulnerability for the underlying locking method.

{On considering workplace thefts as a major problem, there is a requirement of designing a vandal proof door hardware and locking mechanism for ensuring the security of our property. So the door lock system with extra security features with a user friendly cost is suggested in this paper. When a stranger comes at the door, he/she has to pass three security levels for unlocking the solenoid locks present at the door and if he fails to do so, the door will remain locked. These three levels are of three extraordinary security features as one of them is using Fingerprint sensor, second is using a knocking pattern, and the last lock is unlocked by the preset pin/pattern entered by the user. Since, in addition to these features, there is one more option for the case of appearing of guest at the door and that is the Image capturing using web-camera present at the door and here the owner of the house is able to unlock all the locks if he wants the guest to enter the home. This all will be monitored by Node MCU}.

Rapid development of wireless technologies has driven the evolution of smart grid application. In smart grid, authentication plays an important role for secure communication between smart meter and service provider. Hence, the design of secure authenticated key agreement schemes has received significant attention from researchers. In these schemes, a trusted third party directly participates in key agreement process. Although, this third party is assumed as trusted, however we cannot reject the possibility that being a third party, it can also be malicious. In the existing works, either the established session key is revealed to the agents of a trusted third party, or a trusted third party agent can impersonate the smart meter and establish a valid session key with the service provider, which is likely to cause security vulnerabilities. Therefore, there is a need to design a secure authentication scheme so that only the deserving entities involved in the communication can establish and know the session key. This paper proposes a new secure authenticated key agreement scheme for smart grid considering the fact that the third party can also be malicious. The security of the proposed scheme has been thoroughly evaluated using an adversary model. Correctness of the scheme has been analyzed using the broadly accepted Burrows-Abadi-Needham (BAN) Logic. In addition, the formal security verification of the proposed scheme has been performed using the widely accepted Automated Validation of Internet Security Protocols and Applications (AVISPA) simulation tool. Results of this simulation confirm that the proposed scheme is safe. Detailed security analysis shows the robustness of the scheme against various known attacks. Moreover, the comparative performance study of the proposed scheme with other relevant schemes is presented to demonstrate its practicality.

Due to the large number of terminal nodes and wide deployment of power IOT, it is vulnerable to attacks such as physical hijacking, communication link theft and replay. In order to sense and measure the security risks and threats of massive power IOT terminals in real time, a security threat assessment for power IOT terminals based on knowledge graph was proposed. Firstly, the basic data, operation data and alarm threat data of power IOT terminal equipment are extracted and correlated, and the power IOT terminal based on knowledge graph is constructed. Then, the real-time monitoring data of the power IOT terminal is preprocessed. Based on the knowledge graph of the power IOT terminal, the safety analysis and operation analysis of the terminal are carried out, and the threat index of the power IOT terminal is perceived in real time. Finally, security operation and maintenance personnel make disposal decisions on the terminals according to the threat index of power IOT terminals to ensure the safe and stable operation of power IOT terminal nodes. The experimental results show that compared with the traditional IPS, the method can effectively detect the security threat of the power IOT terminal and reduce the alarm vulnerability rate.

The study of networks is an extensively investigated field of research, with networks and network structure often encoding relationships describing certain systems or processes. Critical infrastructure is understood as being a structure whose failure or damage has considerable impact on safety, security and wellbeing of society, with power systems considered a classic example. The work presented in this paper builds on the long-lasting foundations of network and complex network theory, proposing an extension in form of rebalance based flow centrality for structural vulnerability assessment and critical component identification in adaptive network topologies. The proposed measure is applied to power system vulnerability analysis, with performance demonstrated on the IEEE 30-, 57- and 118-bus test system, outperforming relevant methods from the state-of-the-art. The proposed framework is deterministic (guaranteed), analytically obtained (interpretable) and generalizes well with changing network parameters, providing a complementary tool to power system vulnerability analysis and planning.

The security of the power grid is the first element of its operation. This paper aims at finding the vulnerability nodes in the power grid to prevent it from being destroyed. A novel comprehensive vulnerability index is proposed to the singleness of evaluation indicators for existing literature by integrating the power grid's topology information and operating state. Taking IEEE-118 as an example, the simulation analysis proves that the proposed vulnerability index has certain discriminative advantages and the best weighting factor is obtained through correlation analysis.

With the rapid development of the electric power Internet-of-Things (power IoT) technology and the widespread use of general-purpose software, hardware and network facilities, the power IoT has become more and more open, which makes the traditional power system face new cyber security threats. In order to find the vulnerable device nodes and attack links in the power IoT system, this paper studies a set of attack path calculation methods and vulnerability node discovery algorithms, which can construct a power IoT attack simulation program based on the value of equipment assets and information attributes. What’s more, this paper has carried on the example analysis and verification on the improved IEEE RBTS Bus 2 system. Based on the above research plan, this paper finally developed a set of power IoT attack simulation tool based on distribution electronic stations, which can well find the vulnerable devices in the system.

As an important part of the intelligent measurement system, IOT terminal is in the “edge” layer of the intelligent measurement system architecture. It is the key node of power grid management and cloud fog integration. Its information security is the key to the construction of the security system of intelligent measurement, and the security link between the cloud and sensor measurement. With the in-depth integration of energy flow, information flow and business flow, and the in-depth application of digital technologies such as cloud computing, big data, internet of things, mobile Internet and artificial intelligence, the transformation and development of power system to digital and high-quality digital power grid has been accelerated. As a typical multi-dimensional complex system combining physical space and information space, the security threats and risks faced by the digital grid are more complex. The security risks in the information space will transfer the hazards to the power system and physical space. The Internet of things terminal is facing a more complex situation in the security field than before. This paper studies the feasibility of the security monitoring technology of the Internet of things terminal, in order to reduce the potential risks, improve the safe operation environment of the Internet of things terminal and improve the level of the security protection of the Internet of things terminal. One is to study the potential security problems of Internet of things terminal, and put forward the technical specification of security protection of Internet of things terminal. The second is to study the Internet of things terminal security detection technology, research and develop terminal security detection platform, and realize the unified detection of terminal security protection. The third is to study the security monitoring technology of the Internet of things terminal, develop the security monitoring system of the Internet of things terminal, realize the terminal security situation awareness and threat identification, timely discover the terminal security vulnerabilities, and ensure the stable and safe operation of the terminal and related business master station.

Smart grids have to protect meter measurements against false data injection attacks. By modifying the meter measurements, the attacker misleads the control decisions of the control center, which results in physical damages of power systems. In this paper, we propose a reinforcement learning based vulnerability analysis scheme for data injection attack without relying on the power system topology. This scheme enables the attacker to choose the data injection attack vector based on the meter measurements, the power system status, the previous injected errors and the number of meters to compromise. By combining deep reinforcement learning with prioritized experience replay, the proposed scheme more frequently replays the successful vulnerability detection experiences while bypassing the bad data detection, which is able to accelerate the learning speed. Simulation results based on the IEEE 14 bus system show that this scheme increases the probability of successful vulnerability detection and reduce the number of meters to compromise compared with the benchmark scheme.

5G smart grid applications rely on its high-performance transmission and bearer network. With the help of complex network theory, this paper first analyzes the complex network characteristic parameters of 5G smart grid, and explains the necessity and supporting significance of network vulnerability analysis for efficient transmission of 5G network. Then the node importance analysis algorithm based on node degree and clustering coefficient (NIDCC) is proposed. According to the results of simulation analysis, the power network has smaller path length and higher clustering coefficient in terms of static parameters, which indicates that the speed and breadth of fault propagation are significantly higher than that of random network. It further shows the necessity of network vulnerability analysis. By comparing with the other two commonly used algorithms, we can see that NIDCC algorithm can more accurately estimate and analyze the weak links of the network. It is convenient to carry out the targeted transformation of the power grid and the prevention of blackout accidents.

The power cyber-physical system combines the cyber network with the traditional electrical power network, which can monitor and control the operation of the power grid stably and efficiently. Since the system's structure and function is complicated and large, it becomes fragile as a result. Therefore, establishing a reasonable and effective CPS model and discussing its vulnerability performance under external attacks is essential and vital for power grid operation. This paper uses the theory of complex networks to establish a independent system model by IEEE-118-node power network and 200-node scale-free information network, introducing information index to identify and sort important nodes in the network, and then cascade model of the power cyber-physical system based on the node load capacity is constructed and the vulnerability assessment analysis is carried out. The simulation shows that the disintegration speed of the system structure under deliberate attacks is faster than random attacks; And increasing the node threshold can effectively inhibit the propagation of failure.