Biblio

In order to identify potential weakness in communication and data in transit, a microgrid testbed is being developed at Boise State University. This testbed will be used to verify microgrid models and communication methods in an effort to increase the resiliency of these systems to cyber-attacks. If vulnerabilities are found in these communication methods, then risk mitigation techniques will be developed to address them.

Renewed focus on spacecraft networking by government and private industry promises to establish interoperable communications infrastructures and enable distributed computing in multi-nodal systems. Planned near-Earth and cislunar missions by NASA and others evidence the start of building this networking vision. Working with space agencies, academia, and industry, NASA has developed a suite of communications protocols and algorithms collectively referred to as Delay-Tolerant Networking (DTN) to support an interoperable space network. Included in the DTN protocol suite is a security protocol - the Bundle Protocol Security Protocol - which provides the kind of delay-tolerant, transport-layer security needed for cislunar and deep-space trusted networking. We present an analysis of the lifecycle of security operations inherent in a space network with a focus on the DTN-enabled space networking paradigm. This analysis defines three security-related roles for spacecraft (Security Sources, verifiers, and acceptors) and associates a series of critical processing events with each of these roles. We then define the set of required and optional actions associated with these security events. Finally, we present a series of best practices associated with policy configurations that are unique to the space-network security problem. Framing space network security policy as a mapping of security actions to security events provides the details necessary for making trusted networks semantically interoperable. Finally, this method is flexible enough to allow for customization even while providing a unifying core set of mandatory security actions.

Connecting anything to the Internet is one of the main objectives of the Internet of Things (IoT). It enabled to access any device from anywhere at any time without any human intervention. There are endless applications of IoT involving controlling home applications to industry. This rapid growth of this technology and innovations of its application results due to improved technology of developing these tiny devices with its back-end software. On the other side, internal resources such as memory, processing power, battery life are the significant constraints of these devices. Introducing lightweight cryptography helped secure data transmission across various devices while protecting these devices from getting attacked for DDoS attack is still a significant concern. This paper primarily focuses on elaborating on DDoS attack and the malware used to initiate a DDoS attack on IoT devices. Further, this paper mainly focuses on providing solutions that would help to prevent DDoS attack from IoT network.

Industrial Internet is widely used in the production field. As the openness of networks increases, industrial networks facing increasing security risks. Information and communication technologies are now available for most industrial manufacturing. This industry-oriented evolution has driven the emergence of cloud systems, the Internet of Things (IoT), Big Data, and Industry 4.0. However, new technologies are always accompanied by security vulnerabilities, which often expose unpredictable risks. Industrial safety has become one of the most essential and challenging requirements. In this article, we highlight the serious challenges facing Industry 4.0, introduce industrial security issues and present the current awareness of security within the industry. In this paper, we propose solutions for the anomaly detection and defense of the industrial Internet based on the demand characteristics of network security, the main types of intrusions and their vulnerability characteristics. The main work is as follows: This paper first analyzes the basic network security issues, including the network security needs, the security threats and the solutions. Secondly, the security requirements of the industrial Internet are analyzed with the characteristics of industrial sites. Then, the threats and attacks on the network are analyzed, i.e., system-related threats and process-related threats; finally, the current research status is introduced from the perspective of network protection, and the research angle of this paper, i.e., network anomaly detection and network defense, is proposed in conjunction with relevant standards. This paper proposes a software-defined network (SDN)-based industrial Internet security gateway for the security protection of the industrial Internet. Since there are some known types of attacks in the industrial network, in order to fully exploit the effective information, we combine the ExtratreesClassifier to enhance the detection rate of anomaly detection. In order to verify the effectiveness of the algorithm, this paper simulates an industrial network attack, using the acquired training data for testing. The test data are industrial network traffic datasets, and the experimental results show that the algorithm is suitable for anomaly detection in industrial networks.

With the continuous emergence of cyber attacks, the security of industrial control system (ICS) has become a hot issue in academia and industry. Intrusion detection technology plays an irreplaceable role in protecting industrial system from attacks. However, the imbalance between normal samples and attack samples seriously affects the performance of intrusion detection algorithms. This paper proposes SE-IDS, which uses generative adversarial networks (GAN) to expand the minority to make the number of normal samples and attack samples relatively balanced, adopts particle swarm optimization (PSO) to optimize the parameters of LightGBM. Finally, we evaluated the performance of the proposed model on the industrial network dataset.

By 2025, it is estimated that installed data storage in the U.S. will be 2.2 Zettabytes, generating about 50 million units of end-of-life hard-disk drives (HDDs) per year. The circular economy (CE) tackles waste issues by maximizing value retention in the economy, for instance, through reuse and recycling. However, the reuse of hard disk drives is hindered by the lack of trust organizations have toward other means of data removal than physically destroying HDDs. Here, an agent-based approach explores how organizations' decisions to adopt other data removal means affect HDDs' circularity. The model applies the theory of planned behavior to model the decisions of HDDs end-users. Results demonstrate that the attitude (which is affected by trust) of end-users toward data-wiping technologies acts as a barrier to reuse. Moreover, social pressure can play a significant role as organizations that adopt CE behaviors can set an example for others.

Cloud computing has included an essential part of its industry and statistics garage is the main service provided, where a huge amount of data can be stored in a virtual server. Storing data in public platforms may be vulnerable to threats. Consequently, the obligation of secure usage and holistic backup of statistics falls upon the corporation providers. Subsequently, an affordable and compliant mechanism of records auditing that permits groups to audit the facts stored in shared clouds whilst acting quick and trouble- unfastened healing might be a fairly sought-after cloud computing task concept. There is a lot of advantage in growing this domain and there is considerable precedence to follow from the examples of dropbox, google power among others.

To improve the security and reliability of the system in case of sensor failure, a fault diagnosis algorithm based on neural network is proposed to locate the fault quickly and reconstruct the control system in this paper. Firstly, the typical airborne sensors are introduced and their common failure modes are analyzed. Then, a new method of complex feature extraction using wavelet packet is put forward to extract the fault characteristics of UAV sensors. Finally, the observer method based on BP neural network is adopted to train and acquire data offline, and to detect and process single or multiple sensor faults online. Matlab simulation results show that the algorithm has good diagnostic accuracy and strong generalization ability, which also has certain practicability in engineering.

Advancement in network technology provides an opportunity for e-commerce industries to sell digital content. However, multimedia content has the drawback of easy copy and redistribution, which causes rampant piracy. Digital rights management (DRM) systems are developed to address content piracy. Basically, DRM focuses to control content consumption and distribution. In general, to provide copyright protection, DRM system loses flexibility and creates a severe threat to users’ privacy. Moreover, traditional DRM systems are client-server architecture, which cannot handle strategies geographically. These disadvantages discourage the adoption of DRM systems. At the same time, multi-distributor DRM (MD-DRM) system provides a way to facilitate content distribution more effectively. Most of the existing multi-distributor DRM systems are privacy encroaching and do not discuss the useful content distribution framework. To overcome the drawbacks of existing schemes, we propose a privacy-preserving MD-DRM system, which is flexible enough to support location-based content distribution. The proposed scheme maintains a flexible and transparent content distribution without breaching consumer privacy. Besides, the proposed scheme does not violate accountability parameters. This mechanism makes traitor identification possible without violating the privacy rights of authorized consumers.

In order to protect user privacy and provide better access control in Internet of Things (IoT) environments, designing an appropriate two-party authentication and key exchange protocol is a prominent challenge. In this paper, we propose a lightweight certificateless non-interactive authentication and key exchange (CNAKE) protocol for mutual authentication between remote users and smart devices. Based on elliptic curves, our lightweight protocol provides high security performance, realizes non-interactive authentication between the two entities, and effectively reduces communication overhead. Under the random oracle model, the proposed protocol is provably secure based on the Computational Diffie-Hellman and Bilinear Diffie-Hellman hardness assumption. Finally, through a series of experiments and comprehensive performance analysis, we demonstrate that our scheme is fast and secure.

Mobile payment protocols have attracted widespread attention over the past decade, due to advancements in digital technology. The use of these protocols in online industries can dramatically improve the quality of online services. However, the central issue of concern when utilizing these types of systems is their accountability, which ensures trust between the parties involved in payment transactions. It is, therefore, vital for researchers to investigate how to handle the accountability of mobile payment protocols. In this research, we introduce a secure mobile payment protocol to overcome this problem. Our payment protocol combines all the necessary security features, such as confidentiality, integrity, authentication, and authorization that are required to build trust among parties. In other words, is the properties of mutual authentication and non-repudiation are ensured, thus providing accountability. Our approach can resolve any conflicts that may arise in payment transactions between parties. To prove that the proposed protocol is correct and complete, we use the Scyther and AVISPA tools to verify our approach formally.

Unforeseen situations on the shopfloor cause the assembly process to divert from its expected progress. To be able to overcome these deviations in a timely manner, assembly process monitoring and early deviation detection are necessary. However, legal regulations and union policies often limit the direct monitoring of human-intensive assembly processes. Grounded in an industry use case, this paper outlines a novel approach that, based on indirect privacy-respecting monitored data from the shopfloor, enables the near real-time detection of multiple types of process deviations. In doing so, this paper specifically addresses uncertainties stemming from indirect shopfloor observations and how to reason in their presence.

In recent years, the commodities industry has grown rapidly under the stimulus of domestic demand and the expansion of cross-border trade. It has also been combined with the rapid development of e-commerce technology in the same period to form a flexible and efficient e-commerce system for bulk commodities. However, the hasty combination of both has inspired a lack of effective regulatory measures in the bulk industry, leading to constant industry chaos. Among them, the problem of lagging evidence in regulatory platforms is particularly prominent. Based on this, we design a blockchain-based evidential and secure bulk-commodity supervisory system (abbr. BeBus). Setting different privacy protection policies for each participant in the system, the solution ensures effective forensics and tamper-proof evidence to meet the needs of the bulk business scenario.

Telecommunications and data are pervasive in almost each aspect of our every-day life and new concerns progressively arise as a result of stakes related to privacy and data protection [1]. Indeed, systems development becomes data-centric leading to an ecosystem where a variety of players intervene (citizens, industry, regulators) and where the policies regarding data usage and utilization are far from consensual. The new General Data Protection Regulation (GDPR) enacted by the European Commission in 2018 has introduced new provisions including principles for lawfulness, fairness, transparency, etc. thus endorsing data subjects with new rights in regards to their personal data. In this context, a growing need for approaches that conceptualize and help engineers to integrate GDPR and privacy provisions at design time becomes paramount. This paper presents a comprehensive approach to support different phases of the design process with special attention to the integration of privacy and data protection principles. Among others, it is a generic model-based approach that can be specialized according to the specifics of different application domains.

Blockchain technology (BCT) has paved a way for new potentials of handling serious data privacy, integrity and security issues in healthcare. To curb the increasing challenges in healthcare industry, healthcare organizations need to apply blockchain technology to better improve patient safety and protect patients records from counterfeiting and fraud. The purpose of this research paper was to define BCT can assist in improving pharmaceutical industries in Saudi Arabia upon utilization of its application. This study adopted quantitative methods to gather the study data. Based on healthcare leaders perception and Internet connection, lack of cooperation, and economic inequality were found to be leading factors hindering the application of blockchain technology in the pharmaceutical industries, Saudi Arabia. Factors facilitating the application of blockchain technology in the pharmaceutical industries, Saudi Arabia were found as system robustness of BCT, increased data safety and decentralization, need for enhanced supply chain management and interoperability, and government laws and policies. Adopting interventions that are targeted to specific patient population medications, effective delivery systems, transit provider reimbursement far from intensity and volume of services towards value and quality was found to compromise the pre-existent challenges and real capacity in healthcare system. Although the relationship between implementation of blockchain technology and cost spending is negative in the short-term, in the long run, the relationship is positive Blockchain helps in managing multiple levels in a more secure way, reduces paper work and amplifies verification inefficiency.

With the development of various technologies, the modern industry has been promoted to a new era known as Industry 4.0. Within such paradigm, smart factories are becoming widely recognized as the fundamental concept. These systems generate and exchange vast amounts of privacy-sensitive data, which makes them attractive targets of attacks and unauthorized access. To improve privacy and security within such environments, a more decentralized approach is seen as the solution to allow their longterm growth. Currently, the blockchain technology represents one of the most suitable candidate technologies able to support distributed and secure ecosystem for Industry 4.0 while ensuring reliability, information integrity and access authorization. Blockchain based access control frameworks address encountered challenges regarding the confidentiality, traceability and notarization of access demands and procedures. However significant additional fears are raised about entities' privacy regarding access history and shared policies. In this paper, our main focus is to ensure strong privacy guarantees over the access control related procedures regarding access requester sensitive attributes and shared access control policies. The proposed scheme called PDAMF based on ring signatures adds a privacy layer for hiding sensitive attributes while keeping the verification process transparent and public. Results from a real implementation plus performance evaluation prove the proposed concept and demonstrate its feasibility.

Fuzzing is a promising method for discovering vulnerabilities. Recently, various techniques are developed to improve the efficiency of fuzzing, and impressive gains are observed in evaluation results. However, evaluation is complex, as many factors affect the results, for example, test suites, baseline and metrics. Even more, most experiment setups are lab-oriented, lacking industrial settings such as large code-base and parallel runs. The correlation between the academic evaluation results and the bug-finding ability in real industrial settings has not been sufficiently studied. In this paper, we test representative fuzzing techniques to reveal their efficiency in industrial settings. First, we apply typical fuzzers on academic widely used small projects from LAVAM suite. We also apply the same fuzzers on large practical projects from Google's fuzzer-test-suite, which is rarely used in academic settings. Both experiments are performed in both single and parallel run. By analyzing the results, we found that most optimizations working well on LAVA-M suite fail to achieve satisfying results on Google's fuzzer-test-suite (e.g. compared to AFL, QSYM detects 82x more synthesized bugs in LAVA-M, but only detects 26% real bugs in Google's fuzzer-test-suite), and the original AFL even outperforms most academic optimization variants in industry widely used parallel runs (e.g. AFL covers 13% more paths than AFLFast). Then, we summarize common pitfalls of those optimizations, analyze the corresponding root causes, and propose potential directions such as orchestrations and synchronization to overcome the problems. For example, when running in parallel on those large practical projects, the proposed horizontal orchestration could cover 36%-82% more paths, and discover 46%-150% more unique crashes or bugs, compared to fuzzers such as AFL, FairFuzz and QSYM.

This advanced research survey aims to perform intrusion detection and routing in ad hoc networks in wireless MANET networks using machine learning techniques. The MANETs are composed of several ad-hoc nodes that are randomly or deterministically distributed for communication and acquisition and to forward the data to the gateway for enhanced communication securely. MANETs are used in many applications such as in health care for communication; in utilities such as industries to monitor equipment and detect any malfunction during regular production activity. In general, MANETs take measurements of the desired application and send this information to a gateway, whereby the user can interpret the information to achieve the desired purpose. The main importance of MANETs in intrusion detection is that they can be trained to detect intrusion and real-time attacks in the CIC-IDS 2019 dataset. MANETs routing protocols are designed to establish routes between the source and destination nodes. What these routing protocols do is that they decompose the network into more manageable pieces and provide ways of sharing information among its neighbors first and then throughout the whole network. The landscape of exciting libraries and techniques is constantly evolving, and so are the possibilities and options for experiments. Implementing the framework in python helps in reducing syntactic complexity, increases performance compared to implementations in scripting languages, and provides memory safety.

In recent years, the Internet-of-Things (IoT)-based traffic management system (ITMS) has attracted the attention of researchers from different fields, such as the automotive industry, academia and traffic management, due to its ability to enhance road safety and improve traffic efficiency. ITMS uses the Vehicle Ad-hoc Network (VANET) to communicate messages about traffic conditions or the event on the route to ensure the safety of the commuter. ITMS uses wireless communication technology for communication between different devices. Wireless communication has challenges to privacy and security. Challenges such as confidentiality, authentication, integrity, non-repudiation, identity, trust are major concerns of either security or privacy or both. This paper discusses the features of the traffic system, the features of the traffic management system (TMS) and the features of IoT that can be used in TMS with its challenges. Further, this paper analyses the work done in the last few years with the future scope of IoT in the TMS.

The Robot Operation System (ROS) is widely used in academia as well as the industry to build custom robot applications. Successful cyberattacks on robots can result in a loss of control for the legitimate operator and thus have a severe impact on safety if the robot is moving uncontrollably. A high level of security thus needs to be mandatory. Neither ROS 1 nor 2 in their default configuration provide protection against network based attackers. Multiple protection mechanisms have been proposed that can be used to overcome this. Unfortunately, it is unclear how effective and usable each of them are. We provide a structured analysis of the requirements these protection mechanisms need to fulfill by identifying realistic, network based attacker models and using those to derive relevant security requirements and other evaluation criteria. Based on these criteria, we analyze the protection mechanisms available and compare them to each other. We find that none of the existing protection mechanisms fulfill all of the security requirements. For both ROS 1 and 2, we discuss which protection mechanism are most relevant and give hints on how to decide on one. We hope that the requirements we identify simplify the development or enhancement of protection mechanisms that cover all aspects of ROS and that our comparison helps robot operators to choose an adequate protection mechanism for their use case.

Convergence of operation technology (OT) and information technology (IT) in industrial automation is currently being adopted as an accelerating trend. The Industrial Internet of Things (IIoT) consists of heterogeneous sensing, computing and actuation nodes that are meshed through a layer of communication protocols, and represents a key enabler for this convergence. Experimental test beds are required to validate complex system designs in terms of scalability, latency, real-time operation and security. We use the open source Coaty - distributed industrial systems framework to present a smart industry application integrating field devices and controllers over the OPCUA and MQTT protocols. The experimental evaluation, using both proprietary automation components and open software modules, serves as a reference tool for building robust systems and provides practical insights for interoperability.

The fourth industrial revolution envisions industry manufacturing systems to be software driven where mundane manufacturing tasks can be automated. As software is perceived as an integral part of this vision, discovering vulnerabilities is of paramount of importance so that manufacturing systems are secure. A categorization of vulnerability discovery strategies can inform practitioners on how to identify undiscovered vulnerabilities in software. Recently researchers have investigated and identified vulnerability discovery strategies used in open source software (OSS) projects. The efficacy of the derived strategy needs to be validated by obtaining feedback from practitioners. Such feedback can be helpful to assess if identified strategies are useful for practitioners and possible directions the derived vulnerability discovery strategies can be improvised. We survey 51 practitioners to assess if four vulnerability discovery strategies: diagnostics, malicious payload construction, misconfiguration, and pernicious execution can be used to identify undiscovered vulnerabilities. Practitioners perceive the strategies to be useful: for example, we observe 88% of the surveyed practitioners to agree that diagnostics could be used to discover vulnerabilities. Our work provides evidence of usefulness for the identified strategies.

The Internet of Things (IoT) provides significant benefits for industry due to connect the devices together through the internet. Attribute-Based Encryption (ABE) is a technique can enforce an access control over data to guarantee the data security. In this paper, we propose an ABE scheme for data in industrial IoT. The scheme achieves both security and high performance. When there is a shared subpolicy among the access policies of a sensor, the scheme optimizes the encryption of the messages. Through analysis and simulation, we show that our solution is security and efficient.

In a collaboration with the Georgia Peanut Commission’s Education Center and museum in Georgia, USA, we developed an augmented reality app to guide visitors through the museum and offer immersive educational information about the artifacts, exhibits, and artwork displayed therein. Notably, our augmented reality system applies the First Order Motion Model for Image Animation to several portraits of individuals influential to the Georgia peanut industry to provide immersive animated narration and monologue regarding their contributions to the peanut industry. [4]

Advanced persistent threats targeting sensitive corporations, are becoming today stealthier and more complex, coordinating different attacks steps and lateral movements, and trying to stay undetected for long time. Classical security solutions that rely on signature-based detection can be easily thwarted by malware using obfuscation and encryption techniques. More recent solutions are using machine learning approaches for detecting outliers. Nevertheless, the majority of them reason on tabular unstructured data which can lead to missing obvious conclusions. We propose in this paper a novel approach that leverages a combination of both knowledge graphs and machine learning techniques to detect and predict attacks. Using Cyber Threat Intelligence (CTI), we built a knowledge graph that processes event logs in order to not only detect attack techniques, but also learn how to predict them.