Biblio

We propose an ideal functionality FCD and a construction ΠCD for oblivious and updatable committed databases. FCD allows a prover P to read, write, and update values in a database and to prove to a verifier V in zero-knowledge (ZK) that a value is read from or written into a certain position. The following properties must hold: (1) values stored in the database remain hidden from V; (2) a value read from a certain position is equal to the value previously written into that position; (3) (obliviousness) both the value read or written and its position remain hidden from V.ΠCD is based on vector commitments. After the initialization phase, the cost of read and write operations is independent of the database size, outperforming other techniques that achieve cost sublinear in the dataset size for prover and/or verifier. Therefore, our construction is especially appealing for large datasets. In existing “commit-and-prove” two-party protocols, the task of maintaining a committed database between P and V and reading and writing values into it is not separated from the task of proving statements about the values read or written. FCD allows us to improve modularity in protocol design by separating those tasks. In comparison to simply using a commitment scheme to maintain a committed database, FCD allows P to hide efficiently the positions read or written from V. Thanks to this property, we design protocols for e.g. privacy-preserving e-commerce and location-based services where V gathers aggregate statistics about the statements that P proves in ZK.

The concepts of information flow security and refinement are known to have had a troubled relationship ever since the seminal work of McLean. In this work we study refinements that support changes in data representation and semantics, including the addition of state variables that may induce new observational power or side channels. We propose a new epistemic approach to ignorance-preserving refinement where an abstract model is used as a specification of a system's permitted information flows, that may include the declassification of secret information. The core idea is to require that refinement steps must not induce observer knowledge that is not already available in the abstract model. Our study is set in the context of a class of shared variable multiagent models similar to interpreted systems in epistemic logic. We demonstrate the expressiveness of our framework through a series of small examples and compare our approach to existing, stricter notions of information-flow secure refinement based on bisimulations and noninterference preservation. Interestingly, noninterference preservation is not supported “out of the box” in our setting, because refinement steps may introduce new secrets that are independent of secrets already present at abstract level. To support verification, we first introduce a “cube-shaped” unwinding condition related to conditions recently studied in the context of value-dependent noninterference, kernel verification, and secure compilation. A fundamental problem with ignorance-preserving refinement, caused by the support for general data and observation refinement, is that sequential composability is lost. We propose a solution based on relational pre-and postconditions and illustrate its use together with unwinding on the oblivious RAM construction of Chung and Pass.

Recent controlled-channel attacks exploit timing differences in the rudimentary fetch-decode-execute logic of processors. These new attacks also pose a threat to software on embedded systems. Even when Trusted Execution Environments (TEEs) are used, interrupt latency attacks allow untrusted code to extract application secrets from a vulnerable enclave by scheduling interruption of the enclave. Constant-time programming is effective against these attacks but, as we explain in this paper, can come with some disadvantages regarding performance. To deal with this new threat, we propose a novel algorithm that hardens programs during compilation by aligning the execution time of corresponding instructions in secret-dependent branches. Our results show that, on a class of embedded systems with deterministic execution times, this approach eliminates interrupt latency side-channel leaks and mitigates limitations of constant-time programming. We have implemented our approach in the LLVM compiler infrastructure for the San-cus TEE, which extends the openMSP430 microcontroller, and we discuss applicability to other architectures. We make our implementation and benchmarks available for further research.

Capability machines such as CHERI provide memory capabilities that can be used by compilers to provide security benefits for compiled code (e.g., memory safety). The existing C to CHERI compiler, for example, achieves memory safety by following a principle called “pointers as capabilities” (PAC). Informally, PAC says that a compiler should represent a source language pointer as a machine code capability. But the security properties of PAC compilers are not yet well understood. We show that memory safety is only one aspect, and that PAC compilers can provide significant additional security guarantees for partial programs: the compiler can provide security guarantees for a compilation unit, even if that compilation unit is later linked to attacker-provided machine code.As such, this paper is the first to study the security of PAC compilers for partial programs formally. We prove for a model of such a compiler that it is fully abstract. The proof uses a novel proof technique (dubbed TrICL, read trickle), which should be of broad interest because it reuses the whole-program compiler correctness relation for full abstraction, thus saving work. We also implement our scheme for C on CHERI, show that we can compile legacy C code with minimal changes, and show that the performance overhead of compiled code is roughly proportional to the number of cross-compilation-unit function calls.

We investigate a compressive sampling (CS) stepped frequency ground penetrating radar for detection of underground objects, which uses Bayesian estimation and a probabilistic model for the target support. Due to the underground targets being sparse, the B-scan is a sparse image. Using the CS principle, the stepped frequency radar is implemented using a subset of random frequencies at each antenna position. For image reconstruction we use Markov Chain and Markov Random Field models for the target support in the B-scan, where we also estimate the model parameters using the Expectation Maximization algorithm. The approach is tested using Web radar data obtained by measuring the signal responses scattered off land mine targets in a laboratory experimental setup. Our approach results in improved performance compared to the standard denoising algorithm for image reconstruction.

This paper presents the application of Compressive Sampling (CS) to the realization of a wideband receiver for distributed spectrum monitoring. The proposed prototype performs the non-uniform sampling CS-based technique, while the signal reconstruction is realized by the Orthogonal Matching Pursuit (OMP) algorithm on a personal computer. A first experimental analysis has been conducted on the prototype by assessing several figures of merit, thus characterizing its performance in the time, frequency and modulation domains. The obtained results demonstrate that the proposed prototype can achieve good performance in all specified domains with Compression Ratios (CRs) up to 10 for a 4-QAM (Quadrature Amplitude Modulation) signal having carrier frequency of 350 MHz and working at a symbol rate of 46 MSym/s.

Application of computational intelligence methods in data analysis and optimization problems can allow feasible and optimal solutions of complicated engineering problems. This study demonstrates an intelligent analysis scheme for determination of optimal operating condition of an internal combustion engine. For this purpose, an artificial neural network learning model is used to represent engine behavior based on engine data, and a metaheuristic optimization method is implemented to figure out optimal operating states of the engine according to the neural network learning model. This data analysis scheme is used for adjustment of optimal engine speed and fuel rate parameters to provide a maximum torque under Nitrous oxide emission constraint. Harris hawks optimization method is implemented to solve the proposed optimization problem. The solution of this optimization problem addresses eco-friendly enhancement of vehicle performance. Results indicate that this computational intelligent analysis scheme can find optimal operating regimes of an engine.

Earlier around in year 1880’s, Industry 2.0 marked as change to the society caused by the invention of electricity. In today’s era, artificial intelligence plays a crucial role in defining the period of Industry 4.0. In this research study, we have presented Computational Intelligence based Machine Condition Monitoring system architecture for determination of developing faults in industrial machines. The goal is to increase efficiency of machines and reduce the cost. The architecture is fusion of machine sensitive sensors, cloud computing, artificial intelligence and databases, to develop an autonomous fault diagnostic system. To explain CI-MCMs, we have used neural networks on sensor data obtained from hydraulic system. The results obtained by neural network were compared with those obtained from traditional methods.

Intelligent design has been an emerging important area in the design. Existing works related to intelligent design use objective indicators to measure the quality of AI design by comparing the differences between AI-generated data and real data. However, the level of quality and efficiency of intelligent design compared to human designers remains unclear. We conducted user experiments to compare the design quality and efficiency of advanced design methods with that of junior designers. The conclusion is advanced intelligent design methods are comparable with junior designers on painting. Besides, intelligent design uses only 10% of the time spent by the junior designer in the tasks of layout design, color matching, and video editing.

Analyzing safety and security requirements remains a difficult task in the development of real-life network protocols. Although numerous modeling and analyzing methods have been proposed in the past decades, most of them handle safety and security requirements separately without considering their interplay. In this work, we propose a collaborative modeling framework that enables co-analysis of safety and security requirements for network protocols. Our modeling framework is based on a well-defined type system and supports modeling of network topology, message flows, protocol behaviors and attacker behaviors. It also supports the specification of safety requirements as temporal logical formulae and typical security requirements as queries, and leverages on the existing verification tools for formal safety and security analysis via model transformations. We have implemented this framework in a prototype tool CMSS, and illustrated the capability of CMSS by using the 5G AKA initialization protocol as a case study.

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.

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 vulnerability assessment of the cyber-physical power system based on complex network theory is applied in this paper. The influence of the power system statistics upon the system vulnerability is studied based on complex network theory. The electrical betweenness is defined to suitably describe the power system characteristics. The real power systems are utilized as examples to analyze the distribution of the degree and betweenness of the power system as a complex network. The topology model of the cyber-physical power system is formed, and the static analysis is implemented to the study of the cyber-physical power system structural vulnerability. The IEEE 300 bus test system is selected to verify the model.

Session-based recommendation is the task of predicting user actions during short online sessions. The user is considered to be anonymous in this setting, with no past behavior history available. Predicting anonymous users' next actions and their preferences in the absence of historical user behavior information is valuable from a cybersecurity and aerospace perspective, as cybersecurity measures rely on the prompt classification of novel threats. Our offered solution builds upon the previous representation learning work originating from natural language processing, namely BERT, which stands for Bidirectional Encoder Representations from Transformers (Devlin et al., 2018). In this paper we propose CyberBERT, the first deep session-based recommender system to employ bidirectional transformers to model the intent of anonymous users within a session. The session-based setting lends itself to applications in threat recognition, through monitoring of real-time user behavior using the CyberBERT architecture. We evaluate the efficiency of this dynamic state method using the Windows PE Malware API sequence dataset (Catak and Yazi, 2019), which contains behavior for 7107 API call sequences executed by 8 classes of malware. We compare the proposed CyberBERT solution to two high-performing benchmark algorithms on the malware dataset: LSTM (Long Short-term Memory) and transformer encoder (Vaswani et al., 2017). We also evaluate the method using the YOOCHOOSE 1/64 dataset, which is a session-based recommendation dataset that contains 37,483 items, 719,470 sessions, and 31,637,239 clicks. Our experiments demonstrate the advantage of a bidirectional architecture over the unidirectional approach, as well as the flexibility of the CyberBERT solution in modelling the intent of anonymous users in a session. Our system achieves state-of-the-art measured by F1 score on the Windows PE Malware API sequence dataset, and state-of-the-art for P@20 and MRR@20 on YOOCHOOSE 1/64. As CyberBERT allows for user behavior monitoring in the absence of behavior history, it acts as a robust malware classification system that can recognize threats in aerospace systems, where malicious actors may be interacting with a system for the first time. This work provides the backbone for systems that aim to protect aviation and aerospace applications from prospective third-party applications and malware.

Quick response (QR) codes are currently used ubiq-uitously. Their interaction protocol design is initially unsecured. It forces users to scan QR codes, which makes it harder to differentiate a genuine code from a malicious one. Intruders can change the original QR code and make it fake, which can lead to phishing websites that collect sensitive data. The interaction model can be improved and made more secure by adding some modifications to the backend side of the application. This paper addresses the vulnerabilities of QR codes and recommends improvements in security design. Furthermore, two state-of-the-art algorithms, Digital Signature (DS) and Elliptic Curve Digital Signature (ECDS), are analytically compared to determine their strengths in QR code security.

The paradigm of quantum computation has led to the development of new algorithms as well variations on existing algorithms. In particular, novel cryptographic techniques based upon quantum computation are of great interest. Many classical encryption techniques naturally translate into the quantum paradigm because of their well-structured factorizations and the fact that they can be phased in the form of unitary operators. In this work, we demonstrate a quantum approach to data encryption and decryption based upon the McEliece cryptosystem using Reed-Muller codes. This example is of particular interest given that post-quantum analyses have highlighted this system as being robust against quantum attacks. Finally, in anticipation of quantum computation operating over binary fields, we discuss alternative operator factorizations for the proposed cryptosystem.

Cyber-security training has evolved into an imperative need, aiming to provide cyber-security professionals with the knowledge and skills required to confront cyber-attacks that are increasing in number and sophistication. Training activities are typically associated with evaluation means, aimed to assess the extent to which the trainee has acquired the knowledge and skills whose development is targeted by the training programme, while cyber-security awareness and skill level evaluation means may be used to support additional security-related aspects of organizations. In this paper, we review trainee performance assessment metrics in cyber-security training, aiming to assist designers of cyber-security training activities to identify the most prominent trainee performance assessment means for their training programmes, while additional research directions involving cyber-security training evaluation metrics are also identified.

Professional training is essential for organizations to successfully defend their assets against cyber-attacks. Successful detection and prevention of security incidents demands that personnel is not just aware about the potential threats, but its security expertise goes far beyond the necessary background knowledge. To fill-in the gap for competent security professionals, platforms offering realistic training environments and scenarios are designed that are referred to as cyber-ranges. Multiple cyber-ranges listed under a common platform can simulate more complex environments, referred as cyber-range federations. Security education approaches often implement gamification mechanics to increase trainees’ engagement and maximize the outcome of the training process. Scoring is an integral part of a gamification scheme, allowing both the trainee and the trainer to monitor the former’s performance and progress. In this article, a novel scoring model is presented that is designed to be agnostic with respect to the source of information: either a CR or a variety of different CRs being part of a federated environment.

Pairings on elliptic curves which are carried out by the Miller loop and final exponentiation are used for innovative protocols such as ID-based encryption and group signature authentication. As the recent progress of attacks for finite fields in which pairings are defined, the importance of the use of the curves with prime embedding degrees \$k\$ has been increased. In this manuscript, the authors provide a method for providing efficient final exponentiation algorithms for a specific cyclotomic family of curves with arbitrary prime \$k\$ of \$k\textbackslashtextbackslashequiv 1(\textbackslashtextbackslashtextmod\textbackslashtextbackslash 6)\$. Applying the proposed method for several curves such as \$k=7\$, 13, and 19, it is found that the proposed method gives rise to the same algorithms as the previous state-of-the-art ones by the lattice-based method.

Nowadays, cyber physical systems are playing an important role in human life in which they provide features that make interactions between human and machine easier. To design and analysis such systems, the main problem is their complexity. In this paper, we propose a description language for cyber physical systems based on stochastic processes. The proposed language is called SPDL (Stochastic Description Process Language). For designing SPDL, two main parts are considered for Cyber Physical Systems (CSP): embedded systems and physical environment. Then these parts are defined as stochastic processes and CPS is defined as a tuple. Syntax and semantics of SPDL are stated based on the proposed definition. Also, the semantics are defined as by set theory. For implementation of SPDL, dependencies between words of a requirements are extracted as a tree data structure. Based on the dependencies, SPDL is used for describing the CPS. Also, a lexical analyzer and a parser based on a defined BNF grammar for SPDL is designed and implemented. Finally, SPDL of CPS is transformed to NuSMV which is a symbolic model checker. The Experimental results show that SPDL is capable of describing cyber physical systems by natural language.

This paper presents conceptual modelling of the criticality of critical infrastructure (CI) nth order dependency effect using neural networks. Incidentally, critical infrastructures are usually not stand-alone, they are mostly interconnected in some way thereby creating a complex network of infrastructures that depend on each other. The relationships between these infrastructures can be either unidirectional or bidirectional with possible cascading or escalating effect. Moreover, the dependency relationships can take an nth order, meaning that a failure or disruption in one infrastructure can cascade to nth interconnected infrastructure. The nth-order dependency and criticality problems depict a sequential characteristic, which can result in chronological cyber effects. Consequently, quantifying the criticality of infrastructure demands that the impact of its failure or disruption on other interconnected infrastructures be measured effectively. To understand the complex relational behaviour of nth order relationships between infrastructures, we model the behaviour of nth order dependency using Neural Network (NN) to analyse the degree of dependency and criticality of the dependent infrastructure. The outcome, which is to quantify the Criticality Index Factor (CIF) of a particular infrastructure as a measure of its risk factor can facilitate a collective response in the event of failure or disruption. Using our novel NN approach, a comparative view of CIFs of infrastructures or organisations can provide an efficient mechanism for Critical Information Infrastructure Protection and resilience (CIIPR) in a more coordinated and harmonised way nationally. Our model demonstrates the capability to measure and establish the degree of dependency (or interdependency) and criticality of CIs as a criterion for a proactive CIIPR.

In this paper is devoted a creation cryptographic protocol for the division of mutual authentication and session key. For secure protocols, suitable cryptographic algorithms were monitored.

There is an increasing trend in cloud adoption of enterprise applications in, for example, manufacturing, healthcare, and finance. Such applications are routinely subject to targeted cyberattacks, which result in significant loss of sensitive data (e.g., due to data exfiltration in advanced persistent threats) or valuable utilities (e.g., due to resource the exfiltration of power in cryptojacking). There is a critical need to train highly skilled cybersecurity professionals, who are capable of defending against such targeted attacks. In this article, we present the design, development, and evaluation of the Mizzou Cyber Range, an online platform to learn basic/advanced cyber defense concepts and perform training exercises to engender the next-generation cybersecurity workforce. Mizzou Cyber Range features flexibility, scalability, portability, and extendability in delivering cyberattack/defense learning modules to students. We detail our “research-inspired learning” and “learn-apply-create” three-phase pedagogy methodologies in the development of four learning modules that include laboratory exercises and self-study activities using realistic cloud-based application testbeds. The learning modules allow students to gain skills in using latest technologies (e.g., elastic capacity provisioning, software-defined everything infrastructure) to implement sophisticated “attack defense by pretense” techniques. Students can also use the learning modules to understand the attacker-defender game in order to create disincentives (i.e., pretense initiation) that make the attacker's tasks more difficult, costly, time consuming, and uncertain. Lastly, we show the benefits of our Mizzou Cyber Range through the evaluation of student learning using auto-grading, rank assessments with peer standing, and monitoring of students' performance via feedback from prelab evaluation surveys and postlab technical assessments.

Physical layer security has a paramount importance in tactical wireless networks. Traditional approaches may not fulfill all requirements, demanding additional sophisticated techniques. Thus, Combined Interference and Communications (CIC) emerges as a strategy against message interception in Cognitive Radio Military Networks (CRMN). Since CIC adopts an interference approach under specific CRMN requirements and characteristics, it saves great energy and reduces the receiver detection factor when compared to previous proposals in the literature. However, previous CIC analyses were conducted under vaguely realistic channel models. Thus, the focus of this paper is two-fold. Firstly, we identify more realistic channel models to achieve tactical network scenario channel parameters. Additionally, we use such parameters to evaluate CIC suitability to increase CRMN physical layer security. Numerical experiments and emulations illustrate potential impairments on previous work due to the adoption of unrealistic channel models, concluding that CIC technique remains as an upper limit to increase physical layer security in CRMN.

Cognitive radio primary network secure communication strategy based on secondary user energy harvesting and primary user destination assistance is investigated to guarantee primary user secure communication in cognitive radio network. In the proposed strategy, the primary network selects the best secondary user to forward the traffic from a primary transmitter (PT) to a primary receiver (PR). The best secondary user implements beamforming technique to assist primary network for secure communication. The remaining secondary transmitters harvest energy and transmit information to secondary receiver over the licensed primary spectrum. In order to further enhance the security of primary network and increase the harvested energy for the remaining secondary users, a destination-assisted jamming signal transmission strategy is proposed. In this strategy, artificial noise jamming signal transmitted by PR not only confuses eavesdropper, but also be used to power the remaining secondary users. Simulation results demonstrate that, the proposed strategy allows secondary users to communicate in the licensed primary spectrum. It enhances primary network secure communication performance dramatically with the joint design of secondary user transmission power and beamforming vectors. Furthermore, physical layer security of primary and secondary network can also be guaranteed via the proposed cognitive radio primary network secure communication strategy.