Quantum Computing Security 2022 - Geospatial fog computing system offers various benefits as a platform for geospatial computing services closer to the end users, including very low latency, good mobility, precise position awareness, and widespread distribution. In recent years, it has grown quickly. Fog nodes’ security is susceptible to a number of assaults, including denial of service and resource abuse, because to their widespread distribution, complex network environments, and restricted resource availability. This paper proposes a Quantum Key Distribution (QKD)-based geospatial quantum fog computing environment that offers a symmetric secret key negotiation protocol that can preserve informationtheoretic security. In QKD, after being negotiated between any two fog nodes, the secret keys can be given to several users in various locations to maintain forward secrecy and long-term protection. The new geospatial quantum fog computing environment proposed in this work is able to successfully withstand a variety of fog computing assaults and enhances information security.

Provenance 2022 - Connected vehicles (CVs) have facilitated the development of intelligent transportation system that supports critical safety information sharing with minimum latency. However, CVs are vulnerable to different external and internal attacks. Though cryptographic techniques can mitigate external attacks, preventing internal attacks imposes challenges due to authorized but malicious entities. Thwarting internal attacks require identifying the trustworthiness of the participating vehicles. This paper proposes a trust management framework for CVs using interaction provenance that ensures privacy, considers both in-vehicle and vehicular network security incidents, and supports flexible security policies. For this purpose, we present an interaction provenance recording and trust management protocol. Different events are extracted from interaction provenance, and trustworthiness is calculated using fuzzy policies based on the events.

Provable Security - Recent research has shown that hackers can efficiently infer sensitive user activities only by observing the network traffic of smart home devices. To protect users’ privacy, researchers have designed several traffic obfuscation methods. However, existing methods usually consume high bandwidth or provide weak privacy protection. In this paper, we conduct thorough research on smart home traffic obfuscation. We first propose a fixed-value obfuscation scheme and prove that it is perfectly secure by showing the indistinguishability of user activities. Yet, fixed-value obfuscation has high bandwidth consumption. To further reduce the bandwidth consumption, we propose combining fixed-value obfuscation with Multipath TCP transmission. The security and performance of the proposed multipath fixed-value obfuscation method are theoretically analyzed. We have implemented the proposed methods and tested them on public packet traces and simulated smart home networks. The experimental results match well with the theoretical analysis.

Operating Systems Security - Drive Backup is an application for backing up data, including creating copies of partitions for quick recovery in case of an accident, virus attack or, if necessary, replacing all data, including the operating system and installed ones. Software, plus a new hard drive. Reinstalling the operating system and applications after a hardware failure or virus attack does not take you much time and effort. The best way to protect your computer is to create a backup of the system partition with the operating system installed on it and all the necessary applications. In this paper, The commercial hard disk backup system for quick recovery operating system in cloud storage system. Copies can be made to hard drives and removable media as well as network-connected drives. If you need a disk management program, check out the corporate version of this package. A multicast function for transferring copies of an image to multiple computers at the same time, well suited to the needs of corporate offices (for example, to create or restore multiple workstations). But for home backup, you may need to think about other programs - simpler and faster.

Object Oriented Security - Smart distribution grids have new protection concepts known as fault self-healing whereby Intelligent Electronic Devices (IEDs) can automatically reconfigure the power circuits to isolate faults and restore power to the relevant sections. This is typically implemented with IEDs exchanging IEC 61850 Generic Object Oriented Substation Event (GOOSE) messages in a peer-to-peer communication network. However, a selfhealing application may be faced by challenges of emerging cyber-physical security threats. These can result in disruption to the applications’ operations thereby affecting the power system reliability. Blockchain is one technology that has been deployed in several applications to offer security and bookkeeping. In this paper, we propose a novel concept using blockchain as a second-tier security mechanism to support time-critical selfhealing operations in smart distribution grids. We show through a simulation study the impact of our proposed architecture when compared with a normal self healing architecture. The results show that our proposed architecture can achieve significant savings in time spent in no-power state by portions of the grid during cyber-physical attacks.

Object Oriented Security - In Production System Engineering (PSE), domain experts aim at effectively and efficiently analyzing and mitigating information security risks to product and process qualities for manufacturing. However, traditional security standards do not connect security analysis to the value stream of the production system nor to production quality requirements. This paper aims at facilitating security analysis for production quality already in the design phase of PSE. In this paper, we (i) identify the connection between security and production quality, and (ii) introduce the Production Security Network (PSN) to efficiently derive reusable security requirements and design patterns for PSE. We evaluate the PSN with threat scenarios in a feasibility study. The study results indicate that the PSN satisfies the requirements for systematic security analysis. The design patterns provide a good foundation for improving the communication of domain experts by connecting security and quality concerns.

Object Oriented Security - A growing number of attacks and the introduction of new security standards, e.g. ISO 21434, are increasingly shifting the focus of industry and research to the cybersecurity of vehicles. Being cyber-physical systems, compromised vehicles can pose a safety risk to occupants and the environment. Updates over the air and monitoring of the vehicle fleet over its entire lifespan are therefore established in current and future vehicles. Elementary components of such a strategy are security sensors in the form of firewalls and intrusion detection systems, for example, and an operations center where monitoring and response activities are coordinated. A critical step in defending against, detecting, and remediating attacks is providing knowledge about the vehicle and fleet context. Whether a vehicle is driving on the highway or parked at home, what software version is installed, or what security incidents have occurred affect the legitimacy of data and network traffic. However, current security measures lack an understanding of how to operate in an adjusted manner in different contexts. This work is therefore dedicated to a concept to make security measures for vehicles context-aware. We present our approach, which consists of an object-oriented model of relevant context information within the vehicle and a Knowledge Graph for the fleet. With this approach, various use cases can be addressed, according to the different requirements for the use of context knowledge in the vehicle and operations center.

Neural Style Transfer - With the development of economical society, the problem of product piracy security is becoming more and more serious. In order to protect the copyright of brands, based on the image neural style transfer, this paper proposes an automatic generation algorithm of anti-counterfeiting logo with security shading, which increases the difficulty of illegal copying and packaging production. VGG19 deep neural network is used to extract image features and calculate content response loss and style response loss. Based on the original neural style transfer algorithm, the content loss is added, and the generated security shading is fused with the original binary logo image to generate the anti-counterfeiting logo image with higher recognition rate. In this paper, the global loss function is composed of content loss, content response loss and style response loss. The L-BFGS optimization algorithm is used to iteratively reduce the global loss function, and the relationship between the weight adjustment, the number of iterations and the generated anti-counterfeiting logo among the three losses is studied. The secret keeping of shading style image used in this method increases the anti-attack ability of the algorithm. The experimental results show that, compared with the original logo, this method can generate the distinguishable logo content, complex security shading, and has convergence and withstand the attacks.

Neural Network Security - Trust is an essential concept in ad hoc network security. Creating and maintaining trusted relationships between nodes is a challenging task. This paper proposes a decentralized method for evaluating trust in ad hoc networks. The method uses neural networks and local information to predict the trust of neighboring nodes. The method was compared with the original centralized version, showing that even without global information knowledge, the method has, on average, 97\% accuracy in classification and 94\% in regression problem. An important contribution of this paper is overcoming the main limitation of the original method, which is the centralized evaluation of trust. Moreover, the decentralized method output is a perfect fit to use as an input to enhance routing in ad hoc networks.

Neural Network Security - Software-Defined Network (SDN) is a new networking paradigm that adopts centralized control logic and provides more control to the network operators over the network infrastructure to meet future network requirements. SDN controller known as operation system, which is responsible for running network applications and maintaining the different network services and functionalities. Despite all its great capabilities, SDN is facing different security threats due to its various architectural entities and centralized nature. Distributed Denial of Service (DDoS) is a promptly growing attack and becomes a major threat for the SDN. To date, most of the studies focus on detecting high-rate DDoS attacks at the control layer of SDN and low-rate DDoS attacks are high concealed because they are difficult to detect. Furthermore, the existing methods are useful for the detection of high-rate DDoS, so need to focus on low-rate DDoS attacks separately. Hence, the use of machine learning algorithms is growing for the detection of low-rate DDoS attacks in the SDN, but they achieved low accuracy against this attack. To improve the detection accuracy, this paper first describes the attack s mechanism and then proposes a Recurrent Neural Network (RNN) based method. The extracted features from the flow rules are used by the RNN for the detection of low-rate attacks. The experimental results show that the proposed method intelligently detects the attack, and its detection accuracy reaches 98.59\%. The proposed method achieves good detection accuracy as compared to existing studies.

Neural Network Security - Aiming at the network security problem caused by the rapid development of network, this paper uses a network traffic anomaly detection method of industrial control system based on convolutional neural network. In the traditional machine learning algorithm, the processing of features has a high impact on the performance of the model, and the model is highly dependent on features. This method uses the characteristics of convolutional neural network to autonomously learn features, which avoids this problem. In order to verify the superiority of the model, this paper takes accuracy as the evaluation index, and compares it with the traditional machine learning algorithm. The results show that the overall accuracy of the method is 99.88 \%, which has higher accuracy than traditional machine learning algorithms such as decision tree algorithm (ID3), adaptive boosting tree (Adboost) and naive Bayesian model. Therefore, this method can be better applied to the anomaly detection of network traffic in industrial control system, and has practical application value.

Neural Network Security - With the development of computer and network technology, industrial control systems are connecting with the Internet and other public networks in various ways, viruses, trojans and other threats are spreading to industrial control systems, industrial control system information security issues are becoming increasingly prominent. Under this background, it is necessary to construct the network security evaluation model of industrial control system based on the safety evaluation criteria and methods, and complete the safety evaluation of the industrial control system network according to the design scheme. Based on back propagation (BP) neural network’s evaluation of the network security status of industrial control system, this paper determines the number of neurons in BP neural network input layer, hidden layer and output layer by analyzing the actual demand, empirical equation calculation and experimental comparison, and designs the network security evaluation index system of industrial control system according to factors affecting industrial control safety, and constructs a safety rating table. Finally, by comparing the performance of BP neural network and multilinear regression to the evaluation of the network security status of industrial control system through experimental simulation, it can be found that BP neural network has higher accuracy for the evaluation of network security status of industrial control system.

Neural Network Security - With the continuous development of network technology and the continuous expansion of network scale, the security of the network has suffered more threats, and the attacks faced are becoming more and more extensive. The frequent occurrence of network security incidents has caused huge losses, facing more and more severe situation, it is necessary to adopt various network security technologies to solve the problem. In network security, the most commonly used technology is firewall. The firewall has a certain blocking effect on attacks from outside the network, but it has a weak defense effect on the attacks in the internal network, and it is easy to be bypassed. Intrusion detection technology can detect both internal and external network attacks. Responses are generated before the intrusion behavior occurs, and alarm information is issued for timely and effective processing. In recent years, China s campus security incidents are still happening, seriously threatening the lives of students and disrupting the normal teaching order of schools. At present, there are still many loopholes in campus security operations. Campus security management system has become an important task in campus security construction. On this basis, relevant personnel are required to analyze the existing problems of campus safety and the needs of the safety management system, and find the main technology of a more advanced intelligent safety management system.

Neural Network Security - With the rapid development of computer networks and information technology today, people are more inclined to use network systems to achieve various data exchanges. Alibaba, Tencent and other companies virtual payment has become the mainstream payment method. Due to the globalization and openness of the network, anyone can freely enter and exit, which brings huge hidden dangers to NS(network security). NS has become an important issue that we have to face. Once important information is stolen, it is likely to cause very large losses to individuals and even the society. This article mainly studies the computer NS encryption technology of neural network. First of all, the current situation of computer NS is comprehensively reflected from the two aspects of domestic Internet users and NS penetration rate in recent years. By 2020, the number of Chinese residents using the Internet has reached 1.034 billion, and 77.3\% of Internet users are generally aware of NS. Secondly, it analyzes the effect of NN(neural network) on computer NS encryption technology. The results show that the use of NN in computer encryption technology not only helps to improve security and convenience, but also prevents the secondary transmission of data and prevents related information leakage.

Neural Network Security - With the development of computing technology, data security and privacy protection have also become the focus of researchers; along with this comes the issue of network link security and reliability, and these issues have become the focus of discussion when studying network security. Intrusion detection is an effective means to assist in network malicious traffic detection and maintain network stability; to meet the ever-changing demand for network traffic identification, intrusion detection models have undergone a transformation from traditional intrusion detection models to machine learning intrusion detection models to deep intrusion detection models. The efficiency and superiority of deep learning have been proven in fields such as image processing, but there are still some problems in the field of network security intrusion detection: the models are not targeted when processing data, the models have poor generalization ability, etc. The combinatorial neural network proposed in this paper can effectively propose a solution to the problems of existing models, and the CL-IDS model proposed in this paper has a better performance on the KDDCUP99 dataset as demonstrated by relevant experiments.

Neural Network Resiliency - Over the past few years, deep neural networks (DNNs) have been used to solve a wide range of real-life problems. However, DNNs are vulnerable to adversarial attacks where carefully crafted input perturbations can mislead a well-trained DNN to produce false results. As DNNs are being deployed into security-sensitive applications such as autonomous driving, adversarial attacks may lead to catastrophic consequences.

Neural Network Resiliency - The globalization of the Integrated Circuit (IC) market is attracting an ever-growing number of partners, while remarkably lengthening the supply chain. Thereby, security concerns, such as those imposed by functional Reverse Engineering (RE), have become quintessential. RE leads to disclosure of confidential information to competitors, potentially enabling the theft of intellectual property. Traditional functional RE methods analyze a given gate-level netlist through employing pattern matching towards reconstructing the underlying basic blocks, and hence, reverse engineer the circuit’s function.

Network Security Resiliency - Trending towards autonomous transportation systems, modern vehicles are equipped with hundreds of sensors and actuators that increase the intelligence of the vehicles with a higher level of autonomy, as well as facilitate increased communication with entities outside the in-vehicle network.However, increase in a contact point with the outside world has exposed the controller area network (CAN) of a vehicle to remote security vulnerabilities. In particular, an attacker can inject fake high priority messages within the CAN through the contact points, while preventing legitimate messages from controlling the CAN (Denial-of-Service (DoS) attack). In this paper, we propose a Moving Target Defense (MTD) based mechanism to provide resiliency against DoS attack, where we shuffle the message priorities at different communication cycles, opposed to the state-of-the-art message priority setup, to nullify the attacker’s knowledge of message priorities for a given time. The performance and efficacy of the proposed shuffling algorithm has been analyzed under different configuration, and compared against the state-of-the-art solutions. It is observed that the proposed mechanism is successful in denying DoS attack when the attacker is able to bypass preemptive strategies and inject messages within the in-vehicle network.

Network Security Resiliency - Distributed cyber-infrastructures and Artificial Intelligence (AI) are transformative technologies that will play a pivotal role in the future of society and the scientific community. Internet of Things (IoT) applications harbor vast quantities of connected devices that collect a massive amount of sensitive information (e.g., medical, financial), which is usually analyzed either at the edge or federated cloud systems via AI/Machine Learning (ML) algorithms to make critical decisions (e.g., diagnosis). It is of paramount importance to ensure the security, privacy, and trustworthiness of data collection, analysis, and decision-making processes. However, system complexity and increased attack surfaces make these applications vulnerable to system breaches, single-point of failures, and various cyber-attacks. Moreover, the advances in quantum computing exacerbate the security and privacy challenges. That is, emerging quantum computers can break conventional cryptographic systems that offer cyber-security services, public key infrastructures, and privacy-enhancing technologies. Therefore, there is a vital need for new cyber-security paradigms that can address the resiliency, long-term security, and efficiency requirements of distributed cyber infrastructures.

Network Security Resiliency - The 5G ecosystem is designed as a highly sophisticated and modularized architecture that decouples the radio access network (RAN), the multi-access edge computing (MEC) and the mobile core to enable different and scalable deployments. It leverages modern principles of virtualized network functions, microservices-based service chaining, and cloud-native software stacks. Moreover, it provides built-in security and mechanisms for slicing. Despite all these capabilities, there remain many gaps and opportunities for additional capabilities to support end-toend secure operations for applications across many domains. Although 5G supports mechanisms for network slicing and tunneling, new algorithms and mechanisms that can adapt network slice configurations dynamically to accommodate urgent and mission-critical traffic are needed. Such slices must be secure, interference-aware, and free of side channel attacks. Resilience of the 5G ecosystem itself requires an effective means for observability and (semi-)autonomous self-healing capabilities. To address this plethora of challenges, this paper presents the SECurity and REsiliency TEchniques for Differentiated 5G OPerationS (SECRETED 5G OPS) project, which is investigating fundamental new solutions that center on the zero trust, network slicing, and network augmentation dimensions, which together will achieve secure and differentiated operations in 5G networks. SECRETED 5G OPS solutions are designed to be easily deployable, minimally invasive to the existing infrastructure, not require modifications to user equipment other than possibly firmware upgrades, economically viable, standards compliant, and compliant to regulations.

Network Security Resiliency - The 5G ecosystem is designed as a highly sophisticated and modularized architecture that decouples the radio access network (RAN), the multi-access edge computing (MEC) and the mobile core to enable different and scalable deployments. It leverages modern principles of virtualized network functions, microservices-based service chaining, and cloud-native software stacks. Moreover, it provides built-in security and mechanisms for slicing. Despite all these capabilities, there remain many gaps and opportunities for additional capabilities to support end-toend secure operations for applications across many domains. Although 5G supports mechanisms for network slicing and tunneling, new algorithms and mechanisms that can adapt network slice configurations dynamically to accommodate urgent and mission-critical traffic are needed. Such slices must be secure, interference-aware, and free of side channel attacks. Resilience of the 5G ecosystem itself requires an effective means for observability and (semi-)autonomous self-healing capabilities. To address this plethora of challenges, this paper presents the SECurity and REsiliency TEchniques for Differentiated 5G OPerationS (SECRETED 5G OPS) project, which is investigating fundamental new solutions that center on the zero trust, network slicing, and network augmentation dimensions, which together will achieve secure and differentiated operations in 5G networks. SECRETED 5G OPS solutions are designed to be easily deployable, minimally invasive to the existing infrastructure, not require modifications to user equipment other than possibly firmware upgrades, economically viable, standards compliant, and compliant to regulations.

Network Security Resiliency - The renewable energy proliferation calls upon the grid operators and planners to systematically evaluate the potential impacts of distributed energy resources (DERs). Considering the significant differences between various inverter-based resources (IBRs), especially the different capabilities between grid-forming inverters and grid-following inverters, it is crucial to develop an efficient and effective assessment procedure besides available co-simulation framework with high computation burdens. This paper presents a streamlined graph-based topology assessment for the integrated power system transmission and distribution networks. Graph analyses were performed based on the integrated graph of modified miniWECC grid model and IEEE 8500-node test feeder model, high performance computing platform with 40 nodes and total 2400 CPUs has been utilized to process this integrated graph, which has 100,000+ nodes and 10,000+ IBRs. The node ranking results not only verified the applicability of the proposed method, but also revealed the potential of distributed grid forming (GFM) and grid following (GFL) inverters interacting with the centralized power plants.

Network Security Resiliency - 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.

Network Security Resiliency - An often overlooked but equally important aspect of unmanned aerial system (UAS) design is the security of their networking protocols and how they deal with cyberattacks. In this context, cyberattacks are malicious attempts to monitor or modify incoming and outgoing data from the system. These attacks could target anywhere in the system where a transfer of data occurs but are most common in the transfer of data between the control station and the UAS. A compromise in the networking system of a UAS could result in a variety of issues including increased network latency between the control station and the UAS, temporary loss of control over the UAS, or a complete loss of the UAS. A complete loss of the system could result in the UAS being disabled, crashing, or the attacker overtaking command and control of the platform, all of which would be done with little to no alert to the operator. Fortunately, the majority of higher-end, enterprise, and government UAS platforms are aware of these threats and take actions to mitigate them. However, as the consumer market continues to grow and prices continue to drop, network security may be overlooked or ignored in favor of producing the lowest cost product possible. Additionally, these commercial off-the-shelf UAS often use uniform, standardized frequency bands, autopilots, and security measures, meaning a cyberattack could be developed to affect a wide variety of models with minimal changes. This paper will focus on a low-cost educational-use UAS and test its resilience to a variety of cyberattack methods, including man-in-the-middle attacks, spoofing of data, and distributed denial-of-service attacks. Following this experiment will be a discussion of current cybersecurity practices for counteracting these attacks and how they can be applied onboard a UAS. Although in this case the cyberattacks were tested against a simpler platform, the methods discussed are applicable to any UAS platform attempting to defend against such cyberattack methods.

Network Security Resiliency - A reliable synchrophasor network of phasor measurement units (PMUs) is essential for modern power system operations and management with rapidly increasing levels of renewable energy sources. Cyber-physical system vulnerabilities such as side-channel based denial of service (DoS) attacks can compromise PMU communications even when using an encrypted virtual private network. To overcome these vulnerabilities, countermeasures to DoS attacks needs to be developed. One such countermeasure is the development and deployment of a virtual synchrophasor network (VSN) to improve the reliability of a synchrophasor network to DoS attacks. A cellular computational networks (CCN) is a distributed artificial intelligence framework suitable for complex system modeling and estimation. CCNs have been proved to mitigate the effects of DoS attacks on single PMUs successfully. In this study, the robustness of a VSN is further investigated and proven to exhibit resiliency under concurrent DoS attacks. Typical results for VSN applications in multi-area power systems with utility-scale photovoltaic solar plants are presented.