Biblio

The fifth-generation (5G) standard is the last telecommunication technology, widely considered to have the most important characteristics in the future network industry. The 5G system infrastructure contains three principle interfaces, each one follows a set of protocols defined by the 3rd Generation Partnership Project group (3GPP). For the next generation network, 3GPP specified two authentication methods systematized in two protocols namely 5G Authentication and Key Agreement (5G-AKA) and Extensible Authentication Protocol (EAP). Such protocols are provided to ensure the authentication between system entities. These two protocols are critical systems, thus their reliability and correctness must be guaranteed. In this paper, we aim to formally re-examine 5G-AKA protocol using micro Common Representation Language 2 (mCRL2) language to verify such a security protocol. The mCRL2 language and its associated toolset are formal tools used for modeling, validation, and verification of concurrent systems and protocols. In this context, the authentication protocol 5G-AKA model is built using Algebra of Communication Processes (ACP), its properties are specified using Modal mu-Calculus and the properties analysis exploits Model-Checker provided with mCRL2. Indeed, we propose a new mCRL2 model of 3GPP specification considering 5G-AKA protocol and we specify some properties that describe necessary requirements to evaluate the correctness of the protocol where the parsed properties of Deadlock Freedom, Reachability, Liveness and Safety are positively assessed.

Phasor measurement units (PMUs) are used in power grids across North America to measure the amplitude, phase, and frequency of an alternating voltage or current. PMU's use the IEEE C37.118 protocol to send telemetry to phasor data collectors (PDC) and human machine interface (HMI) workstations in a control center. However, the C37.118 protocol utilizes the internet protocol stack without any authentication mechanism. This means that the protocol is vulnerable to false data injection (FDI) and false command injection (FCI). In order to study different scenarios in which C37.118 protocol's integrity and confidentiality can be compromised, we created a testbed that emulates a C37.118 communication network. In this testbed we conduct FCI and FDI attacks on real-time C37.118 data packets using a packet manipulation tool called Scapy. Using this platform, we generated C37.118 FCI and FDI datasets which are processed by multi-label machine learning classifier algorithms, such as Decision Tree (DT), k-Nearest Neighbor (kNN), and Naive Bayes (NB), to find out how effective machine learning can be at detecting such attacks. Our results show that the DT classifier had the best precision and recall rate.

Power grids are the most extensive man-made systems that are difficult to control and monitor. With the development of conventional power grids and moving toward smart grids, power systems have undergone vast changes since they use the Internet to transmit information and control commands to different parts of the power system. Due to the use of the Internet as a basic infrastructure for smart grids, attackers can sabotage the communication networks and alter the measurements. Due to the complexity of the smart grids, it is difficult for the network operator to detect such cyber-attacks. The attackers can implement the attack in a manner that conventional Bad Data detection (BDD) systems cannot detect since it may not violate the physical laws of the power system. This paper uses the cross wavelet transform (XWT) to detect stealth false data injections attacks (FDIAs) against state estimation (SE) systems. XWT can capture the coherency between measurements of adjacent buses and represent it in time and frequency space. Then, we train a machine learning classification algorithm to distinguish attacked measurements from normal measurements by applying a feature extraction technique.

With the increasing use of information and communication technology in electrical power grids, the security of energy supply is increasingly threatened by cyber-attacks. Traditional cyber-security measures, such as firewalls or intrusion detection/prevention systems, can be used as mitigation and prevention measures, but their effective use requires a deep understanding of the potential threat landscape and complex attack processes in energy information systems. Given the complexity and lack of detailed knowledge of coordinated, timed attacks in smart grid applications, we need information and insight into realistic attack scenarios in an appropriate and practical setting. In this paper, we present a man-in-the-middle-based attack scenario that intercepts process communication between control systems and field devices, employs false data injection techniques, and performs data corruption such as sending false commands to field devices. We demonstrate the applicability of the presented attack scenario in a physical smart grid laboratory environment and analyze the generated data under normal and attack conditions to extract domain-specific knowledge for detection mechanisms.

Power system security assessment and enhancement in grids with high penetration of renewables is critical for pragmatic power system planning. Static Security Assessment (SSA) is a fast response tool to assess system stability margins following considerable contingencies assuming post fault system reaches a steady state. This paper presents a contingency ranking methodology using static security indices to rank credible contingencies considering severity. A Modified IEEE 9 bus system integrating renewables was used to test the approach. The static security indices used independently provides accurate results in identifying severe contingencies but further assessment is needed to provide an accurate picture of static security assessment in an increased time frame of the steady state. The indices driven for static security assessment could accurately capture and rank contingencies with renewable sources but due to intermittency of the renewable source various contingency ranking lists are generated. This implies that using indices in future grids without consideration on intermittent nature of renewables will make it difficult for the grid operator to identify severe contingencies and assist the power system operator to make operational decisions. This makes it necessary to integrate the behaviour of renewables in security indices for practical application in real time security assessment.

Static security assessment is of great significance to ensure the stable transmission of electric power and steady operation of load. The scale of power system trends to expand due to the development of interconnected grid, and the security analysis of the entire network has become time-consuming. On the basis of synthesizing the efficiency and accuracy, a new method is developed. This method adopts a novel dynamic power flow (DPF) model considering the influence of governor deadband and amplitude-limit on the steady state quantitatively. In order to reduce the computation cost, a contingency screening algorithm based on binary search method is proposed. Static security assessment based on the proposed DPF models is applied to calculate the security margin constrained by severe contingencies. The ones with lower margin are chosen for further time-domain (TD) simulation analysis. The case study of a practical grid verifies the accuracy of the proposed model compared with the conventional one considering no governor nonlinearity. Moreover, the test of a practical grid in China, along with the TD simulation, demonstrates that the proposed method avoids massive simulations of all contingencies as well as provides detail information of severe ones, which is effective for security analysis of practical power grids.

This short paper discusses a case study of multi cluster visualization of Static Analysis Security Testing (SAST) warnings in large clusters catering to a majority of Cisco products in hierarchical organizational and checker views. This serves as a one stop shop for real-time visualization of Static Analysis (SA) warning trends, chart, downloading reports, and to effectively address the potential security weaknesses detected. Presently leading SAST tools like Coverity, codesonar, Klocwork etc do not provide inter-cluster or enterprise-wide visualization to effectively address the SA warnings.

In this paper, we discuss the methodology of generalizing the optimal control law from learned component tasks to unlearned composite tasks on Multi-Agent Systems (MASs), by using the linearity composition principle of linearly solvable optimal control (LSOC) problems. The proposed approach achieves both the compositionality and optimality of control actions simultaneously within the cooperative MAS framework in both discrete and continuous-time in a sample-efficient manner, which reduces the burden of re-computation of the optimal control solutions for the new task on the MASs. We investigate the application of the proposed approach on the MAS with coordination between agents. The experiments show feasible results in investigated scenarios, including both discrete and continuous dynamical systems for task generalization without resampling.

Current permissionless cryptocurrencies such as Bitcoin suffer from a limited transaction rate and slow confirmation time, which hinders further adoption. Payment channels are one of the most promising solutions to address these problems, as they allow the parties of the channel to perform arbitrarily many payments in a peer-to-peer fashion while uploading only two transactions on the blockchain. This concept has been generalized into payment channel networks where a path of payment channels is used to settle the payment between two users that might not share a direct channel between them. However, this approach requires the active involvement of each user in the path, making the system less reliable (they might be offline), more expensive (they charge fees per payment), and slower (intermediaries need to be actively involved in the payment). To mitigate this issue, recent work has introduced the concept of virtual channels (IEEE S&P’19), which involve intermediaries only in the initial creation of a bridge between payer and payee, who can later on independently perform arbitrarily many off-chain transactions. Unfortunately, existing constructions are only available for Ethereum, as they rely on its account model and Turing-complete scripting language. The realization of virtual channels in other blockchain technologies with limited scripting capabilities, like Bitcoin, was so far considered an open challenge.In this work, we present the first virtual channel protocols that are built on the UTXO-model and require a scripting language supporting only a digital signature scheme and a timelock functionality, being thus backward compatible with virtually every cryptocurrency, including Bitcoin. We formalize the security properties of virtual channels as an ideal functionality in the Universal Composability framework and prove that our protocol constitutes a secure realization thereof. We have prototyped and evaluated our protocol on the Bitcoin blockchain, demonstrating its efficiency: for n sequential payments, they require an off-chain exchange of 9+2n transactions or a total of 3524+695n bytes, with no on-chain footprint in the optimistic case. This is a substantial improvement compared to routing payments in a payment channel network, which requires 8n transactions with a total of 3026n bytes to be exchanged.

Fully Homomorphic Encryption (FHE) allows a third party to perform arbitrary computations on encrypted data, learning neither the inputs nor the computation results. Hence, it provides resilience in situations where computations are carried out by an untrusted or potentially compromised party. This powerful concept was first conceived by Rivest et al. in the 1970s. However, it remained unrealized until Craig Gentry presented the first feasible FHE scheme in 2009.The advent of the massive collection of sensitive data in cloud services, coupled with a plague of data breaches, moved highly regulated businesses to increasingly demand confidential and secure computing solutions. This demand, in turn, has led to a recent surge in the development of FHE tools. To understand the landscape of recent FHE tool developments, we conduct an extensive survey and experimental evaluation to explore the current state of the art and identify areas for future development.In this paper, we survey, evaluate, and systematize FHE tools and compilers. We perform experiments to evaluate these tools’ performance and usability aspects on a variety of applications. We conclude with recommendations for developers intending to develop FHE-based applications and a discussion on future directions for FHE tools development.

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.

Threats and risks against supply chains are increasing and a framework to add the trustworthiness of supply chain has been considered. In this framework, organisations in the supply chain validate the conformance to the pre-defined requirements. The results of validations are linked each other to achieve the trustworthiness of the entire supply chain. In this paper, we further consider this framework for data supply chains. First, we implement the framework and evaluate the performance. The evaluation shows 500 digital evidences (logs) can be checked in 0.28 second. We also propose five methods to improve the performance as well as five new functionalities to improve usability. With these functionalities, the framework also supports maintaining the certificate chain.

Safety- and security-critical developers have long recognized the importance of applying a high degree of scrutiny to a system’s (or subsystem’s) I/O messages. However, lack of care in the development of message-handling components can lead to an increase, rather than a decrease, in the attack surface. On the DARPA Cyber-Assured Systems Engineering (CASE) program, we have focused our research effort on identifying cyber vulnerabilities early in system development, in particular at the Architecture development phase, and then automatically synthesizing components that mitigate against the identified vulnerabilities from high-level specifications. This approach is highly compatible with the goals of the LangSec community. Advances in formal methods have allowed us to produce hardware/software implementations that are both performant and guaranteed correct. With these tools, we can synthesize high-assurance “building blocks” that can be composed automatically with high confidence to create trustworthy systems, using a method we call Security-Enhancing Architectural Transformations. Our synthesis-focused approach provides a higherleverage insertion point for formal methods than is possible with post facto analytic methods, as the formal methods tools directly contribute to the implementation of the system, without requiring developers to become formal methods experts. Our techniques encompass Systems, Hardware, and Software Development, as well as Hardware/Software Co-Design/CoAssurance. We illustrate our method and tools with an example that implements security-improving transformations on system architectures expressed using the Architecture Analysis and Design Language (AADL). We show how message-handling components can be synthesized from high-level regular or context-free language specifications, as well as a novel specification language for self-describing messages called Contiguity Types, and verified to meet arithmetic constraints extracted from the AADL model. Finally, we guarantee that the intent of the message processing logic is accurately reflected in the application binary code through the use of the verified CakeML compiler, in the case of software, or the Restricted Algorithmic C toolchain with ACL2-based formal verification, in the case of hardware/software co-design.

The Internet of Drones (IoD) is a distributed network control system that mainly manages unmanned aerial vehicle access to controlled airspace and provides navigation between so-called nodes. Securing the transmission of real-time information from the nodes in these applications is essential. The limited drone nodes, data storage, computing and communication capabilities necessitate the need to design an effective and secure authentication scheme. Recently, research has proposed remote user authentication and the key agreement on IoD and claimed that their schemes satisfied all security issues in these networks. However, we found that their schemes may lead to losing access to the drone system due to the corruption of using a key management system and make the system completely unusable. To solve this drawback, we propose a lightweight and anonymous two-factor authentication scheme for drones. The proposed scheme is based on an asymmetric cryptographic method to provide a secure system and is more suitable than the other existing schemes by securing real-time information. Moreover, the comparison shows that the proposed scheme minimized the complexity of communication and computation costs.

The increased need for online account security and the prominence of smartphones in today’s society has led to smartphone-based two-factor authentication schemes, in which the second factor is a code received on the user’s smartphone. Evolving two-factor authentication mechanisms suggest using the proximity of the user’s devices as the second authentication factor, avoiding the inconvenience of user-device interaction. These mechanisms often use low-range communication technologies or the similarities of devices’ environments to prove devices’ proximity and user authenticity. However, such mechanisms are vulnerable to colocated adversaries. This paper proposes Vibe-an implicit two-factor authentication mechanism, which uses a vibration communication channel to prove users’ authenticity in a secure and non-intrusive manner. Vibe’s design provides security at the physical layer, reducing the attack surface to the physical surface shared between devices. As a result, it protects users’ security even in the presence of co-located adversaries-the primary drawback of the existing systems. We prototyped Vibe and assessed its performance using commodity hardware in different environments. Our results show an equal error rate of 0.0175 with an end-to-end authentication latency of approximately 3.86 seconds.

At present, the security protection of distribution automation system is faced with complex and diverse operating environment, and the main use of public network may bring greater security risks, there are still some deficiencies. According to the actual situation of distribution automation of China Southern Power Grid, this paper designs multiple protection technology, carries out encryption distribution terminal research, and realizes end-to-end longitudinal security protection of distribution automation system, which is effectively improving the anti-attack ability of distribution terminal.

Blockchain technology is the need of an hour for ensuring security and data privacy. However, very limited tools and documentation are available, therefore, the traditional code-centric implementation of Blockchain is challenging for programmers and developers due to inherent complexities. To overcome these challenges, in this article, a novel and efficient framework is proposed that is based on the Model-Driven Architecture. Particularly, a Meta-model (M2 level Ecore Model) is defined that contains the concepts of Blockchain technology. As a part of tool support, a tree editor (developed using Eclipse Modeling Framework) and a Sirius based graphical modeling tool with a drag-drop palette have been provided to allow modeling and visualization of simple and complex Blockchain-based scenarios for security labs in a very user-friendly manner. A Model to Text (M2T) transformation code has also been written using Acceleo language that transforms the modeled scenarios into java code for Blockchain application in the security lab. The validity of the proposed framework has been demonstrated via a case study. The results prove that our framework can be reliably used and further extended for automation and development of Blockchain-based application for security labs with simplicity.

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.

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.

Different security issues are a common problem for open source packages archived to and delivered through software ecosystems. These often manifest themselves as software weaknesses that may lead to concrete software vulnerabilities. This paper examines various security issues in Python packages with static analysis. The dataset is based on a snapshot of all packages stored to the Python Package Index (PyPI). In total, over 197 thousand packages and over 749 thousand security issues are covered. Even under the constraints imposed by static analysis, (a) the results indicate prevalence of security issues; at least one issue is present for about 46% of the Python packages. In terms of the issue types, (b) exception handling and different code injections have been the most common issues. The subprocess module stands out in this regard. Reflecting the generally small size of the packages, (c) software size metrics do not predict well the amount of issues revealed through static analysis. With these results and the accompanying discussion, the paper contributes to the field of large-scale empirical studies for better understanding security problems in software ecosystems.

With the rapid increase of practical problem complexity and code scale, the threat of software security is increasingly serious. Consequently, it is crucial to pay attention to the analysis of software source code vulnerability in the development stage and take efficient measures to detect the vulnerability as soon as possible. Machine learning techniques have made remarkable achievements in various fields. However, the application of machine learning in the domain of vulnerability static analysis is still in its infancy and the characteristics and performance of diverse methods are quite different. In this survey, we focus on a source code-oriented static vulnerability analysis method using machine learning techniques. We review the studies on source code vulnerability analysis based on machine learning in the past decade. We systematically summarize the development trends and different technical characteristics in this field from the perspectives of the intermediate representation of source code and vulnerability prediction model and put forward several feasible research directions in the future according to the limitations of the current approaches.

Quasi-identifier is an attribute combined with other attributes to identify specific tuples or partial tuples. Improper selection of quasi-identifiers will lead to the failure of current privacy protection anonymization technology. Therefore, in this paper, we propose a method to solve single structured relational data quasi-identifiers based on functional dependency and determines the attribute classification standard. Firstly, the solution scope of quasi-identifier is determined to be all attributes except identity attributes and critical attributes. Secondly, the real data set is used to evaluate the dependency relationship between the indefinite attribute subset and the identity attribute to solve the quasi-identifiers set. Finally, we propose an algorithm to find all quasi-identifiers and experiment on real data sets of different sizes. The results show that our method can achieve better performance on the same dataset.

Symbiotic radio (SR) has recently emerged as a promising technology to boost spectrum efficiency of wireless communications by allowing reflective communications underlying the active RF communications. In this paper, we leverage SR to boost physical layer security by using an array of passive reflecting elements constituting the intelligent reflecting surface (IRS), which is reconfigurable to induce diverse RF radiation patterns. In particular, by switching the IRS's phase shifting matrices, we can proactively create dynamic channel conditions, which can be exploited by the transceivers to extract common channel features and thus used to generate secret keys for encrypted data transmissions. As such, we firstly present the design principles for IRS-assisted key generation and verify a performance improvement in terms of the secret key generation rate (KGR). Our analysis reveals that the IRS's random phase shifting may result in a non-uniform channel distribution that limits the KGR. Therefore, to maximize the KGR, we propose both a heuristic scheme and deep reinforcement learning (DRL) to control the switching of the IRS's phase shifting matrices. Simulation results show that the DRL approach for IRS-assisted key generation can significantly improve the KGR.

Common randomness of channels offers the possibility to create cryptographic keys without the need for a key exchange procedure. Channel reciprocity for TDD (time-division duplexing) systems has been used for this purpose many times. FDD (frequency-division duplexing) systems, however, were long considered to not provide any usable symmetry. However, since the scattering transmission parameters S\textbackslashtextlessinf\textbackslashtextgreater12\textbackslashtextless/inf\textbackslashtextgreater and S\textbackslashtextlessinf\textbackslashtextgreater21\textbackslashtextless/inf\textbackslashtextgreater would ideally be the same due to reciprocity, when using neighboring frequency ranges for both directions, they would just follow a continuous curve when putting them next to each other. To not rely on absolute phase, we use phase differences between antennas and apply a polynomial curve fitting, thereafter, quantize the midpoint between the two frequency ranges with the two measurement directions. This is shown to work even with some spacing between the two bands. For key reconciliation, we force the measurement point from one direction to be in the midpoint of the quantization interval by a grid shift (or likewise measurement data shift). Since the histogram over the quantization intervals does not follow a uniform distribution, some source coding / hashing will be necessary. The key disagreement rate toward an eavesdropper was found to be close to 0.5. Additionally, when using an antenna array, a random permutation of antenna measurements can even further improve the protection against eavesdropping.

A threat of eavesdropping display screen image of information device is caused by unintended EM leakage emanation. QR bar-code is capable of error correction, and its information is possibly read from a damaged screen image from EM leakage. A new design of QR bar-code proposed in this paper uses selected colors in consideration of correlation between the EM wave leakage and display color. Proposed design of QR bar-code keeps error correction of displayed image, and makes it difficult to read information on the eavesdropped image.