The world’s most important industrial economy is particularly vulnerable to both external and internal threats, such as the one uncovered in Supervisory Control and Data Acquisition (SCADA) and Industrial Control Systems (ICS). Upon those systems, the success criteria for security are quite dynamic. Security flaws in these automated SCADA systems have already been discovered by infiltrating the entire network in addition to reducing production line hazards. The objective of our review article is to show various potential future research voids that recent studies have, as well as how many methods are available to concentrate on specific aspects of risk assessment of manufactured systems. The state-of-the-art methods in cyber security risk assessment of SCADA systems are reviewed and compared in this research. Multiple contemporary risk assessment approaches developed for or deployed in the settings of a SCADA system are considered and examined in detail. We outline the approaches’ main points before analyzing them in terms of risk assessment, conventional analytical procedures, and research challenges. The paper also examines possible risk regions or locations where breaches in such automated SCADA systems can emerge, as well as solutions as to how to safeguard and eliminate the hazards when they arise during production manufacturing.

The aim of this paper is to examine noteworthy cyberattacks that have taken place against ICS and SCADA systems and to analyse them. This paper also proposes a new classification scheme based on the severity of the attack. Since the information revolution, computers and associated technologies have impacted almost all aspects of daily life, and this is especially true of the industrial sector where one of the leading trends is that of automation. This widespread proliferation of computers and computer networks has also made it easier for malicious actors to gain access to these systems and networks and carry out harmful activities.

The SCADA (Supervisory Control And Data Acquisition) has become ubiquitous in industrial control systems. However, it may be exposed to cyber attack threats when it accesses the Internet. We propose a three-layer IDS (Intrusion Detection System) model, which integrates three main functions: access control, flow detection and password authentication. We use the reliability test system IEEE RTS-79 to evaluate the reliability. The experimental results provide insights into the establishment of the power SCADA system reliability enhancement strategies.

Software-Defined Networking (SDN) technique is presented in this paper to manage the Naval Supervisory Control and Data Acquisition (SCADA) network for equipping the network with the function of reconfiguration and scalability. The programmable nature of SDN enables a programmable Modular Topology Generator (MTG), which provides an extensive control over the network’s internal connectivity and traffic control. Specifically, two functions of MTG are developed and examined in this paper, namely linkHosts and linkSwitches. These functions are able to place the network into three different states, i.e., fully connected, fully disconnected, and partially connected. Therefore, it provides extensive security benefits and allows network administrators to dynamically reconfigure the network and adjust settings according to the network’s needs. Extensive tests on Mininet have demonstrated the effectiveness of SDN for enabling the reconfigurable and scalable Naval SCADA network. Therefore, it provides a potent tool to enhance the resiliency/survivability, scalability/compatibility, and security of naval SCADA networks.

Supervisory control and data acquisition (SCADA) systems play pivotal role in the operation of modern critical infrastructures (CIs). Technological advancements, innovations, economic trends, etc. have continued to improve SCADA systems effectiveness and overall CIs’ throughput. However, the trends have also continued to expose SCADA systems to security menaces. Intrusions and attacks on SCADA systems can cause service disruptions, equipment damage or/and even fatalities. The use of conventional intrusion detection models have shown trends of ineffectiveness due to the complexity and sophistication of modern day SCADA attacks and intrusions. Also, SCADA characteristics and requirement necessitate exceptional security considerations with regards to intrusive events’ mitigations. This paper explores the viability of supervised learning algorithms in detecting intrusions specific to SCADA systems and their communication protocols. Specifically, we examine four supervised learning algorithms: Random Forest, Naïve Bayes, J48 Decision Tree and Sequential Minimal Optimization-Support Vector Machines (SMO-SVM) for evaluating SCADA datasets. Two SCADA datasets were used for evaluating the performances of our approach. To improve the classification performances, feature selection using principal component analysis was used to preprocess the datasets. Using prominent classification metrics, the SVM-SMO presented the best overall results with regards to the two datasets. In summary, results showed that supervised learning algorithms were able to classify intrusions targeted against SCADA systems with satisfactory performances.

Supervisory Control and Data Acquisition (SCADA) systems are utilized extensively in critical power grid infrastructures. Modern SCADA systems have been proven to be susceptible to cyber-security attacks and require improved security primitives in order to prevent unwanted influence from an adversarial party. One section of weakness in the SCADA system is the integrity of field level sensors providing essential data for control decisions at a master station. In this paper we propose a lightweight hardware scheme providing inferred authentication for SCADA sensors by combining an analog to digital converter and a permutation generator as a single integrated circuit. Through this method we encode critical sensor data at the time of sensing, so that unencoded data is never stored in memory, increasing the difficulty of software attacks. We show through experimentation how our design stops both software and hardware false data injection attacks occurring at the field level of SCADA systems.

With the advent of cloud storage services many users tend to store their data in the cloud to save storage cost. However, this has lead to many security concerns, and one of the most important ones is ensuring data integrity. Public verification schemes are able to employ a third party auditor to perform data auditing on behalf of the user. But most public verification schemes are vulnerable to procrastinating auditors who may not perform auditing on time. These schemes do not have fair arbitration also, i.e. they lack a way to punish the malicious Cloud Service Provider (CSP) and compensate user whose data has been corrupted. On the other hand, CSP might be storing redundant data that could increase the storage cost for the CSP and computational cost of data auditing for the user. In this paper, we propose a Blockchain-based public auditing and deduplication scheme with a fair arbitration system against procrastinating auditors. The key idea requires auditors to record each verification using smart contract and store the result into a Blockchain as a transaction. Our scheme can detect and punish the procrastinating auditors and compensate users in the case of any data loss. Additionally, our scheme can detect and delete duplicate data that improve storage utilization and reduce the computational cost of data verification. Experimental evaluation demonstrates that our scheme is provably secure and does not incur overhead compared to the existing public auditing techniques while offering an additional feature of verifying the auditor’s performance.

With the development of Internet of Things (IoT) technology, the transaction behavior of IoT devices has gradually increased, which also brings the problem of transaction data security and transaction processing efficiency. As one of the research hotspots in the field of data security, blockchain technology has been widely applied in the maintenance of transaction records and the construction of financial payment systems. However, the proportion of microtransactions in the Internet of Things poses challenges to the coupling of blockchain and IoT devices. This paper proposes a three-party scalable architecture based on “IoT device-edge server-blockchain”. In view of the characteristics of micropayment, the verification mechanism of the execution results of the off-chain transaction is designed, and the bridge node is designed in the off-chain architecture, which ensures the finality of the blockchain to the transaction. According to system evaluation, this scalable architecture improves the processing efficiency of micropayments on blockchain, while ensuring its decentration equal to that of blockchain. Compared with other blockchain-based IoT device payment schemes, our architecture is more excellent in activity.

Robustness verification of neural networks (NNs) is a challenging and significant problem, which draws great attention in recent years. Existing researches have shown that bound propagation is a scalable and effective method for robustness verification, and it can be implemented on GPUs and TPUs to get parallelized. However, the bound propagation methods naturally produce weak bound due to linear relaxations on the neurons, which may cause failure in verification. Although tightening techniques for simple ReLU networks have been explored, they are not applicable for NNs with general activation functions such as Sigmoid and Tanh. Improving robustness verification on these NNs is still challenging. In this paper, we propose a Branch-and-Bound (BaB) style method to address this problem. The proposed BaB procedure improves the weak bound by splitting the input domain of neurons into sub-domains and solving the corresponding sub-problems. We propose a generic heuristic function to determine the priority of neuron splitting by scoring the relaxation and impact of neurons. Moreover, we combine bound optimization with the BaB procedure to improve the weak bound. Experimental results demonstrate that the proposed method gains up to 35% improvement compared to the state-of-art CROWN method on Sigmoid and Tanh networks.

As the COVID-19 continues to spread globally, more and more companies are transforming into remote online offices, leading to the expansion of electronic signatures. However, the existing electronic signatures platform has the problem of data-centered management. The system is subject to data loss, tampering, and leakage when an attack from outside or inside occurs. In response to the above problems, this paper designs an electronic signature solution and implements a prototype system based on the consortium blockchain. The solution divides the contract signing process into four states: contract upload, initiation signing, verification signing, and confirm signing. The signing process is mapped with the blockchain-linked data. Users initiate the signature transaction by signing the uploaded contract's hash. The sign state transition is triggered when the transaction is uploaded to the blockchain under the consensus mechanism and the smart contract control, which effectively ensures the integrity of the electronic contract and the non-repudiation of the electronic signature. Finally, the blockchain performance test shows that the system can be applied to the business scenario of contract signing.

Embedded memory are important components in system-on-chip, which may be crippled by aging and wear faults or Hardware Trojan attacks to compromise run-time security. The current built-in self-test and pre-silicon verification lack efficiency and flexibility to solve this problem. To this end, we address such vulnerabilities by proposing a run-time memory security detecting framework in this paper. The solution builds mainly upon a centralized security detection controller for partially reconfigurable inspection content, and a static memory wrapper to handle access conflicts and buffering testing cells. We show that a field programmable gate array prototype of the proposed framework can pursue 16 memory faults and 3 types Hardware Trojans detection with one reconfigurable partition, whereas saves 12.7% area and 2.9% power overhead compared to a static implementation. This architecture has more scalable capability with little impact on the memory accessing throughput of the original chip system in run-time detection.

Efficiency is essential to support responsiveness w.r.t. ever-growing datasets, especially for Deep Learning (DL) systems. DL frameworks have traditionally embraced deferred execution-style DL code that supports symbolic, graph-based Deep Neural Network (DNN) computation. While scalable, such development tends to produce DL code that is error-prone, non-intuitive, and difficult to debug. Consequently, more natural, less error-prone imperative DL frameworks encouraging eager execution have emerged at the expense of run-time performance. While hybrid approaches aim for the “best of both worlds,” the challenges in applying them in the real world are largely unknown. We conduct a data-driven analysis of challenges-and resultant bugs-involved in writing reliable yet performant imperative DL code by studying 250 open-source projects, consisting of 19.7 MLOC, along with 470 and 446 manually examined code patches and bug reports, respectively. The results indicate that hybridization: (i) is prone to API misuse, (ii) can result in performance degradation-the opposite of its intention, and (iii) has limited application due to execution mode incompatibility. We put forth several recommendations, best practices, and anti-patterns for effectively hybridizing imperative DL code, potentially benefiting DL practitioners, API designers, tool developers, and educators.

The secure Internet of Things (loT) increasingly relies on digital cryptographic signatures which require a private signature and public verification key. By their intrinsic nature, public keys are meant to be accessible to any interested party willing to verify a given signature. Thus, the storing of such keys is of great concern, since an adversary shall not be able to tamper with the public keys, e.g., on a local filesystem. Commonly used public-key infrastructures (PKIs), which handle the key distribution and storage, are not feasible in most use-cases, due to their resource intensity and high complexity. Thus, the general storing of the public verification keys is of notable interest for low-resource loT networks. By using the Distributed Ledger Technology (DLT), this paper proposes a decentralized concept for storing public signature verification keys in a tamper-resistant, secure, and resilient manner. By combining lightweight public-key exchange protocols with the proposed approach, the storing of verification keys becomes scalable and especially suitable for low-resource loT devices. This paper provides a Proof-of-Concept implementation of the DLT public-key store by extending our previously proposed NFC-Key Exchange (NFC-KE) protocol with a decentralized Hyperledger Fabric public-key store. The provided performance analysis shows that by using the decentralized keystore, the NFC- KE protocol gains an increased tamper resistance and overall system resilience while also showing expected performance degradations with a low real-world impact.

Web-based Application Programming Interfaces (APIs) are often described using SOAP, OpenAPI, and GraphQL specifications. These specifications provide a consistent way to define web services and enable automated fuzz testing. As such, many fuzzers take advantage of these specifications. However, in an enterprise setting, the tools are usually installed and scaled by individual teams, leading to duplication of efforts. There is a need for an enterprise-wide fuzz testing solution to provide shared, cost efficient, off-nominal testing at scale where fuzzers can be plugged-in as needed. Internet cloud-based fuzz testing-as-a-service solutions mitigate scalability concerns but are not always feasible as they require artifacts to be uploaded to external infrastructure. Typically, corporate policies prevent sharing artifacts with third parties due to cost, intellectual property, and security concerns. We utilize API specifications and combine them with cluster computing elasticity to build an automated, scalable framework that can fuzz multiple apps at once and retain the trust boundary of the enterprise.

This paper establishes a vector autoregressive model based on the current development status of the digital economy and studies the correlation between the digital economy and economic growth MIS from a dynamic perspective, and found that the digital economy has a strong supporting role in the growth of the total economic volume. The coordination degree model calculates the scientific and technological innovation capabilities of China's 30 provinces (except Tibet) from 2018 to 2022, and the coordination, green, open, and shared level of high-quality economic development. The basic principles of the composition of the security measurement are expounded, and the measurement information model can be used as a logic model. The analysis of security measure composition summarizes the selection principle and selection process of security measurement, and analyzes and compares the measure composition methods in several typical security measurement methods.

A huge number of cloud users and cloud providers are threatened of security issues by cloud computing adoption. Cloud computing is a hub of virtualization that provides virtualization-based infrastructure over physically connected systems. With the rapid advancement of cloud computing technology, data protection is becoming increasingly necessary. It's important to weigh the advantages and disadvantages of moving to cloud computing when deciding whether to do so. As a result of security and other problems in the cloud, cloud clients need more time to consider transitioning to cloud environments. Cloud computing, like any other technology, faces numerous challenges, especially in terms of cloud security. Many future customers are wary of cloud adoption because of this. Virtualization Technologies facilitates the sharing of recourses among multiple users. Cloud services are protected using various models such as type-I and type-II hypervisors, OS-level, and unikernel virtualization but also offer a variety of security issues. Unfortunately, several attacks have been built in recent years to compromise the hypervisor and take control of all virtual machines running above it. It is extremely difficult to reduce the size of a hypervisor due to the functions it offers. It is not acceptable for a safe device design to include a large hypervisor in the Trusted Computing Base (TCB). Virtualization is used by cloud computing service providers to provide services. However, using these methods entails handing over complete ownership of data to a third party. This paper covers a variety of topics related to virtualization protection, including a summary of various solutions and risk mitigation in VMM (virtual machine monitor). In this paper, we will discuss issues possible with a malicious virtual machine. We will also discuss security precautions that are required to handle malicious behaviors. We notice the issues of investigating malicious behaviors in cloud computing, give the scientific categorization and demonstrate the future headings. We've identified: i) security specifications for virtualization in Cloud computing, which can be used as a starting point for securing Cloud virtual infrastructure, ii) attacks that can be conducted against Cloud virtual infrastructure, and iii) security solutions to protect the virtualization environment from DDOS attacks.

With the continuous development of computer technology, the coverage of informatization solutions covers all walks of life and all fields of society. For colleges and universities, teaching and scientific research are the basic tasks of the school. The scientific research ability of the school will affect the level of teachers and the training of students. The establishment of a good scientific research environment has become a more important link in the development of universities. SR(Scientific research) data is a prerequisite for SR activities. High-quality SR management data services are conducive to ensuring the quality and safety of SRdata, and further assisting the smooth development of SR projects. Therefore, this article mainly conducts research and practice on cloud computing-based scientific research management data services in colleges and universities. First, analyze the current situation of SR data management in colleges and universities, and the results show that the popularity of SR data management in domestic universities is much lower than that of universities in Europe and the United States, and the data storage awareness of domestic researchers is relatively weak. Only 46% of schools have developed SR data management services, which is much lower than that of European and American schools. Second, analyze the effect of CC(cloud computing )on the management of SR data in colleges and universities. The results show that 47% of SR believe that CC is beneficial to the management of SR data in colleges and universities to reduce scientific research costs and improve efficiency, the rest believe that CC can speed up data storage and improve security by acting on SR data management in colleges and universities.

With the development of cloud computing technology, more and more scientific researchers choose to deliver scientific workflow tasks to public cloud platforms for execution. This mode effectively reduces scientific research costs while also bringing serious security risks. In response to this problem, this article summarizes the current security issues facing cloud scientific workflows, and analyzes the importance of studying cloud scientific workflow security issues. Then this article analyzes, summarizes and compares the current cloud scientific workflow security methods from three perspectives: system architecture, security model, and security strategy. Finally made a prospect for the future development direction.

Cooperative secure computing based on the relationship between numerical value and numerical interval is not only the basic problems of secure multiparty computing but also the core problems of cooperative secure computing. It is of substantial theoretical and practical significance for information security in relation to scientific computing to continuously investigate and construct solutions to such problems. Based on the Goldwasser-Micali homomorphic encryption scheme, this paper propose the Morton rule, according to the characteristics of the interval, a double-length vector is constructed to participate in the exclusive-or operation, and an efficient cooperative decision-making solution for integer and integer interval security is designed. This solution can solve more basic problems in cooperative security computation after suitable transformations. A theoretical analysis shows that this solution is safe and efficient. Finally, applications that are based on these protocols are presented.

A considerable portion of computing power is always required to perform symbolic calculations. The reliability and effectiveness of algorithms are two of the most significant challenges observed in the field of scientific computing. The terms “feasible calculations” and “feasible computations” refer to the same idea: the algorithms that are reliable and effective despite practical constraints. This research study intends to investigate different types of computing and modelling challenges, as well as the development of efficient integration methods by considering the challenges before generating the accurate results. Further, this study investigates various forms of errors that occur in the process of data integration. The proposed framework is based on automata, which provides the ability to investigate a wide-variety of distinct distance-bounding protocols. The proposed framework is not only possible to produce computational (in)security proofs, but also includes an extensive investigation on different issues such as optimal space complexity trade-offs. The proposed framework in embedded with the already established symbolic framework in order to get a deeper understanding of distance-bound security. It is now possible to guarantee a certain level of physical proximity without having to continually mimic either time or distance.

Cloud computing is a model of service provisioning in heterogeneous distributed systems that encourages many researchers to explore its benefits and drawbacks in executing workflow applications. Recently, high-quality security protection has been a new challenge in workflow allocation. Different tasks may and may not have varied security demands, security overhead may vary for different virtual machines (VMs) at which the task is assigned. This paper proposes a Security Oriented Deadline-Aware workflow allocation (SODA) strategy in an IaaS cloud environment to minimize the risk probability of the workflow tasks while considering the deadline met in a deterministic environment. SODA picks out the task based on the highest security upward rank and assigns the selected task to the trustworthy VMs. SODA tries to simultaneously satisfy each task’s security demand and deadline at the maximum possible level. The simulation studies show that SODA outperforms the HEFT strategy on account of the risk probability of the cloud system on scientific workflow, namely CyberShake.

Network security is a problem that is of great concern to all countries at this stage. How to ensure that the network provides effective services to people without being exposed to potential security threats has become a major concern for network security researchers. In order to better understand the network security situation, researchers have studied a variety of quantitative assessment methods, and the most scientific and effective one is the hierarchical quantitative assessment method of the network security threat situation. This method allows the staff to have a very clear understanding of the security of the network system and make correct judgments. This article mainly analyzes the quantitative assessment of the hierarchical network security threat situation from the current situation and methods, which is only for reference.

In this paper we present techniques for enhancing the security of south bound infrastructure in SDN which includes OpenFlow switches and end hosts. In particular, the proposed security techniques have three main goals: (i) validation and secure configuration of flow rules in the OpenFlow switches by trusted SDN controller in the domain; (ii) securing the flows from the end hosts; and (iii) detecting attacks on the switches by malicious entities in the SDN domain. We have implemented the proposed security techniques as an application for ONOS SDN controller. We have also validated our application by detecting various OpenFlow switch specific attacks such as malicious flow rule insertions and modifications in the switches over a mininet emulated network.

SDN represents a significant advance for the telecom world, since the decoupling of the control and data planes offers numerous advantages in terms of management dynamism and programmability, mainly due to its software-based centralized control. Unfortunately, these features can be exploited by malicious entities, who take advantage of the centralized control to extend the scope and consequences of their attacks. When this happens, both the legal and network technical fields are concerned with gathering information that will lead them to the root cause of the problem. Although forensics and incident response processes share their interest in the event information, both operate in isolation due to the conceptual and pragmatic challenges of integrating them into SDN environments, which impacts on the resources and time required for information analysis. Given these limitations, the current work focuses on proposing a framework for SDNs that combines the above approaches to optimize the resources to deliver evidence, incorporate incident response activation mechanisms, and generate assumptions about the possible origin of the security problem.

In case of deploying additional network security equipment in a new location, network service providers face difficulties such as precise management of large number of network security equipment and expensive network operation costs. Accordingly, there is a need for a method for security-aware network service provisioning using the existing network security equipment. In order to solve this problem, there is an existing reinforcement learning-based routing decision method fixed for each node. This method performs repeatedly until a routing decision satisfying end-to-end security constraints is achieved. This generates a disadvantage of longer network service provisioning time. In this paper, we propose security constraints reinforcement learning based routing (SCRR) algorithm that generates routing decisions, which satisfies end-to-end security constraints by giving conditional reward values according to the agent state-action pairs when performing reinforcement learning.