Biblio

This short paper argues that current conceptions in trust formation scholarship miss the context of zero trust, a practice growing in importance in cyber security. The contribution of this paper presents a novel approach to help conceptualize and operationalize zero trust and a call for a research agenda. Further work will expand this model and explore the implications of zero trust in future digital systems.

When a fault occurs in power grid, the analytic model for power grid fault diagnosis could generate multiple solutions under one or more protective relays (PRs) and/or circuit breakers (CBs) malfunctioning, and/or one or more their alarm information failing. Hence, this paper, calling the electrical quantities, presents an optimal solution discrimination method, which determines the optimal solution by constructing the electrical criteria of suspicious faulty components. Furthermore, combining the established electrical criteria with the existing analytic model, a hierarchical fault diagnosis mode is proposed. It uses the analytic model for the first level diagnosis based on the switching quantities. Thereafter, aiming at multiple solutions, it applies the electrical criteria for the second level diagnosis to determine the diagnostic result. Finally, the examples of fault diagnosis demonstrate the feasibility and effectiveness of the developed method.

The power grid is considered to be the most critical piece of infrastructure in the United States because each of the other fifteen critical infrastructures, as defined by the Cyberse-curity and Infrastructure Security Agency (CISA), require the energy sector to properly function. Due the critical nature of the power grid, the ability to detect anomalies in the power grid is of critical importance to prevent power outages, avoid damage to sensitive equipment and to maintain a working power grid. Over the past few decades, the modern power grid has evolved into a large Cyber Physical System (CPS) equipped with wide area monitoring systems (WAMS) and distributed control. As smart technology advances, the power grid continues to be upgraded with high fidelity sensors and measurement devices, such as phasor measurement units (PMUs), that can report the state of the system with a high temporal resolution. However, this influx of data can often become overwhelming to the legacy Supervisory Control and Data Acquisition (SCADA) system, as well as, the power system operator. In this paper, we propose using a deep learning (DL) convolutional neural network (CNN) as a module within the Automatic Network Guardian for ELectrical systems (ANGEL) Digital Twin environment to detect physical faults in a power system. The presented approach uses high fidelity measurement data from the IEEE 9-bus and IEEE 39-bus benchmark power systems to not only detect if there is a fault in the power system but also applies the algorithm to classify which bus contains the fault.

Recent scientific results show that audio feedback associated with body movements can be fundamental during the development to learn new spatial concepts [1], [2]. Within the weDraw project [3], [4], we have investigated how this link can be useful to learn mathematical concepts. Here we present a study investigating how mathematical skills changes after multisensory training based on human-computer interaction (RobotAngle and BodyFraction activities). We show that embodied angle and fractions exploration associated with audio and visual feedback can be used in typical children to improve cognition of spatial mathematical concepts. We finally present the exploitation of our results: an online, optimized version of one of the tested activity to be used at school. The training result suggests that audio and visual feedback associated with body movements is informative for spatial learning and reinforces the idea that spatial representation development is based on sensory-motor interactions.

Privacy preserving scheme for face verification is a biometric system embedded with template protection to protect the data in ensuring data integrity. This paper proposes a new method called Histogram of Oriented Gradient Random Template Protection (HOGRTP). The proposed method utilizes Histogram of Oriented Gradient approach as a feature extraction technique and is combined with Random Template Protection method. The proposed method acts as a multi-factor authentication technique and adds a layer of data protection to avoid the compromising biometric issue because biometric is irreplaceable. The performance accuracy of HOGRTP is tested on the unconstrained face images using the benchmarked dataset, Labeled Face in the Wild (LFW). A promising result is obtained to prove that HOGRTP achieves a higher verification rate in percentage than the pure biometric scheme.

Autonomous systems have the potential to accomplish missions more quickly and effectively, while reducing risks to human operators and costs. However, since the use of autonomous systems is still relatively new, there are still a lot of challenges associated with trusting these systems. Without operators in direct control of all actions, there are significant concerns associated with endangering human lives or damaging equipment. For this reason, NATO has issued a challenge seeking to identify ways to improve decision-maker and operator trust when deploying autonomous systems, and de-risk their adoption. This paper presents the proposal of the winning solution to this NATO challenge. It approaches trust as a multi-dimensional concept, by incorporating the four dimensions of human-agent trust establishment in a digital twin context.

SMS (Short Message Service)-based authentication is widely used as a simple and secure multi-factor authentication, where OTP (One Time Password) is sent to user’s mobile phone via SMS. However, SMS authentication is vulnerable to Password Reset Man in the Middle Attack (PRMitM). In this attack, the attacker makes a victim perform password reset OTP for sign-up verification OTP. If the victim enters OTP to a malicious man-in-the-middle site, the attacker can overtake the victim’s account.We find new smartphone useful functions may increase PR-MitM attack risks. SMS push notification informs us an arrival of message by showing only beginning of the message. Hence, those who received SMS OTP do not notice the cautionary notes and the name of the sender that are supposed to show below the code, which may lead to be compromised. Auto-fill function, which allow us to input authentication code with one touch, is also vulnerable for the same reason.In this study, we conduct a user study to investigate the effect of new smartphone functions incurring PRMitM attack.

With the rapid development and application of emerging information technology, the traditional network security architecture is more and more difficult to support flexible dynamic and a wider range of business data access requirements. Zero trust technology can truly realize the aggregation of security and business by building an end-to-end dynamic new boundary based on identity, which puts forward a new direction for the upgrade and evolution of enterprise network security architecture. This paper mainly includes access control and identity authentication management functions. The goal of access control system is to ensure that legitimate and secure users can use the system normally, and then protect the security of enterprise network and server. The functions of the access control system include identifying the user's identity (legitimacy), evaluating the security characteristics (Security) of the user's machine, and taking corresponding response strategies.

In recent years, blockchain-based cryptocurren-cies have attracted much attention. Attacks targeting cryptocurrencies and related services directly profit an attacker if successful. Related studies have reported attacks targeting configuration-vulnerable nodes in Ethereum using a method called honeypots to observe malicious user attacks. They have analyzed 380 million observed requests and showed that attacks had to that point taken at least 4193 Ether. However, long-term observations using honeypots are difficult because the cost of maintaining honeypots is high. In this study, we analyze the behavior of malicious users using our honeypot system. More precisely, we clarify the pre-investigation that a malicious user performs before attacks. We show that the cost of maintaining a honeypot can be reduced. For example, honeypots need to belong in Ethereum's P2P network but not to the mainnet. Further, if they belong to the testnet, the cost of storage space can be reduced.

STAMP (System Theoretic Accident Model and Processes) is one of the theories that has been attracting attention as a new safety analysis method for complex systems. CAST (Causal Analysis using System Theory) is a causal analysis method based on STAMP theory. The authors investigated an information security incident case, “AIST (National Institute of Advanced Industrial Science and Technology) report on unauthorized access to information systems,” and attempted accident analysis using CAST. We investigated whether CAST could be applied to the cyber security analysis. Since CAST is a safety accident analysis technique, this study was the first to apply CAST to cyber security incidents. Its effectiveness was confirmed from the viewpoint of the following three research questions. Q1:Features of CAST as an accident analysis method Q2:Applicability and impact on security accident analysis Q3:Understanding cyber security incidents with a five-layer model.

Wireless communications networks are an integral part of intelligent systems that enhance the automation of various activities and operations embarked by humans. For example, the development of intelligent devices imbued with sensors leverages emerging technologies such as machine learning (ML) and artificial intelligence (AI), which have proven to enhance military operations through communication, control, intelligence gathering, and situational awareness. However, growing concerns in cybersecurity imply that attackers are always seeking to take advantage of the widened attack surface to launch adversarial attacks which compromise the activities of legitimate users. To address this challenge, we leverage on deep learning (DL) and the principle of cyber-deception to propose a method for defending wireless networks from the activities of jammers. Specifically, we use DL to regulate the power allocated to users and the channel they use to communicate, thereby luring jammers into attacking designated channels that are considered to guarantee maximum damage when attacked. Furthermore, by directing its energy towards the attack on a specific channel, other channels are freed up for actual transmission, ensuring secure communication. Through simulations and experiments carried out, we conclude that this approach enhances security in wireless communication systems.

The user's access history can be used as an important reference factor in determining whether to allow the current access request or not. And it is often ignored by the existing access control models. To make up for this defect, a Dynamic Trust - game theoretic Access Control model is proposed based on the previous work. This paper proposes a method to quantify the user's trust in the cloud environment, which uses identity trust, behavior trust, and reputation trust as metrics. By modeling the access process as a game and introducing the user's trust value into the pay-off matrix, the mixed strategy Nash equilibrium of cloud user and service provider is calculated respectively. Further, a calculation method for the threshold predefined by the service provider is proposed. Authorization of the access request depends on the comparison of the calculated probability of the user's adopting a malicious access policy with the threshold. Finally, we summarize this paper and make a prospect for future work.

Cloud Computing (CC) has emerged as an on-demand accessible tool in different practical applications such as digital industry, academics, manufacturing, health sector and others. In this paper different security threats faced by CC are discussed with suitable examples. Moreover, an artificial intelligence based security enabled CC is also discussed based on suitable empirical data. It is found that an artificial neural network (ANN) is an effective system to detect the level of risk factors associated with CC along with mitigating those risk issues with appropriate algorithms. Hence, it provides a desired level of protection against cyber attacks, internal confidential threats and external threat of data theft from a cloud computing system. Levenberg–Marquardt (LMBP) algorithms are also found as a significant tool to estimate the level of security performance around a cloud computing system. ANN is used to improve the performance level of data security across a cloud computing network and make it security enabled to ensure a protected data transmission to clients associated with the system.

The most widely used method to prevent adversaries from eavesdropping on sensitive sensor, robot, and war fighter communications is mathematically strong cryptographic algorithms. However, prevailing cryptographic protocol mandates are often made without consideration of resource constraints of devices in the internet of Battlefield Things (IoBT). In this article, we address the challenges of IoBT sensor data exchange in contested environments. Battlefield IoT (Internet of Things) devices need to exchange data and receive feedback from other devices such as tanks and command and control infrastructure for analysis, tracking, and real-time engagement. Since data in IoBT systems may be massive or sparse, we introduced a machine learning classifier to determine what type of data to transmit under what conditions. We compared Support Vector Machine, Bayes Point Match, Boosted Decision Trees, Decision Forests, and Decision Jungles on their abilities to recommend the optimal confidentiality preserving data and transmission path considering dynamic threats. We created a synthesized dataset that simulates platoon maneuvers and IED detection components. We found Decision Jungles to produce the most accurate results while requiring the least resources during training to produce those results. We also introduced the JointField blockchain network for joint and allied force data sharing. With our classifier, strategists, and system designers will be able to enable adaptive responses to threats while engaged in real-time field conflict.

Disinformation, fake news, and unverified rumors spread quickly in online social networks (OSNs) and manipulate people's opinions and decisions about life events. The solid mathematical solutions of the strategic decisions in OSNs have been provided under game theory models, including multiple roles and features. This work proposes a game-theoretic opinion framework to model subjective opinions and behavioral strategies of attackers, users, and a defender. The attackers use information deception models to disseminate disinformation. We investigate how different game-theoretic opinion models of updating people's subject opinions can influence a way for people to handle disinformation. We compare the opinion dynamics of the five different opinion models (i.e., uncertainty, homophily, assertion, herding, and encounter-based) where an opinion is formulated based on Subjective Logic that offers the capability to deal with uncertain opinions. Via our extensive experiments, we observe that the uncertainty-based opinion model shows the best performance in combating disinformation among all in that uncertainty-based decisions can significantly help users believe true information more than disinformation.

The network attack such as Distributed Denial-of-Service (DDoS) attack could be critical to latency-critical systems such as Mobile Edge Computing (MEC) as such attacks significantly increase the response delay of the victim service. Intrusion prevention system (IPS) is a promising solution to defend against such attacks, but there will be a trade-off between IPS deployment and application resource reservation as the deployment of IPS will reduce the number of computation resources for MEC applications. In this paper, we proposed a game-theoretic framework to study the joint computation resource allocation and IPS deployment in the MEC architecture. We study the pricing strategy of the MEC platform operator and purchase strategy of the application service provider, given the expected attack strength and end user demands. The best responses of both MPO and ASPs are derived theoretically to identify the Stackelberg equilibrium. The simulation results confirm that the proposed solutions significantly increase the social welfare of the system.

To meet the application need of dynamic visualization VR display of 3D time-varying field, this paper designed an immersive visualization VR system of 3D time-varying field based on the Unity 3D framework. To reduce visual confusion caused by 3D time-varying field flow line drawing and improve the quality and efficiency of visualization rendering drawing, deep learning was used to extract features from the mesoscale vortex of the 3D time-varying field. Moreover, the 3D flow line dynamic visualization drawing was implemented through the Unity Visual Effect Graph particle system.

We study a game-theoretic model of the interactions between a Cyber-Physical System’s (CPS) operator (the defender) against an attacker who launches stepping-stone attacks to reach critical assets within the CPS. We consider that, in addition to optimally allocating its security budget to protect the assets, the defender may choose to modify the CPS through network design interventions. In particular, we propose and motivate four ways in which the defender can introduce additional nodes in the CPS: these nodes may be intended as additional safeguards, be added for functional or structural redundancies, or introduce additional functionalities in the system. We analyze the security implications of each of these design interventions, and evaluate their impacts on the security of an automotive network as our case study. We motivate the choice of the attack graph for this case study and elaborate how the parameters in the resulting security game are selected using the CVSS metrics and the ISO-26262 ASIL ratings as guidance. We then use numerical experiments to verify and evaluate how our proposed network interventions may be used to guide improvements in automotive security.

Vehicles play an integral part in the life of a human being by facilitating in everyday tasks. The major concern that arises with this fact is that the rate of vehicle thefts have increased exponentially and retrieving them becomes almost impossible as the responsible party completely alters the stolen vehicles, leaving them untraceable. Ultimately, tracking and monitoring of vehicles using on-vehicle sensors is a promising and an efficient solution. The Internet of Things (IoT) is expected to play a vital role in revolutionizing the Security and Safety industry through a system of sensor networks by periodically sending the data from the sensors to the cloud for storage, from where it can be accessed to view or take any necessary actions (if required). The main contributions of this paper are the implementation and results of the prototype of a vehicle tracking and monitoring system. The system comprises of an Arduino UNO board connected to the Global Positioning System (GPS) module, Neo-6M, which senses the exact location of the vehicle in the form of latitude and longitude, and the ESP8266 Wi-Fi module, which sends the data to the Application Programming Interface (API) Cloud service, ThingSpeak, for storage and analyzing. An Android based mobile application is developed that utilizes the stored data from the Cloud and presents the user with the findings. Results show that the prototype is not only simple and cost effective, but also efficient and can be readily used by everyone from all walks of life to protect their vehicles.

Distributed Denial-of-Service (DDoS) attacks pose a huge risk to the network and threaten its stability. A game theoretic approach for intrusion detection and prevention is proposed to avoid DDoS attacks in the internet. Game theory provides a control mechanism that automates the intrusion detection and prevention process within a network. In the proposed system, system-subject interaction is modeled as a 2-player Bayesian signaling zero sum game. The game's Nash Equilibrium gives a strategy for the attacker and the system such that neither can increase their payoff by changing their strategy unilaterally. Moreover, the Intent Objective and Strategy (IOS) of the attacker and the system are modeled and quantified using the concept of incentives. In the proposed system, the prevention subsystem consists of three important components namely a game engine, database and a search engine for computing the Nash equilibrium, to store and search the database for providing the optimum defense strategy. The framework proposed is validated via simulations using ns3 network simulator and has acquired over 80% detection rate, 90% prevention rate and 6% false positive alarms.

The problem of buffer overflow in the information system is not threatening, and the system's own defense mechanism can detect and terminate code injection attacks. However, as countermeasures compete with each other, advanced stack overflow attacks have emerged: Return Oriented-Programming (ROP) technology, which has become a hot spot in the field of system security research in recent years. First, this article explains the reason for the existence of this technology and the attack principle. Secondly, it systematically expounds the realization of the return-oriented programming technology at home and abroad in recent years from the common architecture platform, the research of attack load construction, and the research of variants based on ROP attacks. Finally, we summarize the paper.

Big data exchange is an emerging problem in the context of big data management and analytics. In big data exchange, multiple entities exchange big datasets beyond the common data integration or data sharing paradigms, mostly in the context of data federation architectures. How to make big data exchange while ensuring privacy preservation constraintsƒ The latter is a critical research challenge that is gaining momentum on the research community, especially due to the wide family of application scenarios where it plays a critical role (e.g., social networks, bio-informatics tools, smart cities systems and applications, and so forth). Inspired by these considerations, in this paper we provide an overview of models and issues in the context of privacy-preserving big data exchange research, along with a selection of future research directions that will play a critical role in next-generation research.

With the increasing number of electric vehicles, the scale of the market also increases. In the past, the electric vehicle market had problems such as opaque information, numerous levels and data leakage, which were criticized for the impact of the overall development and policies of the electric vehicle industry. In view of the problems existing in the transparency and security of big data management transactions of the Internet of vehicles, this paper combs the commercial operation framework of the Internet of Vehicles Platform, analyses the feasibility and necessity of establishing the token system of the Internet of Vehicles Platform, and constructs the token economic system architecture of the Internet of Vehicles Platform and its development path.

With the development of information technology, extracting important data that people need from the vast information has become the key to a successful era. Therefore, big data technology is increasingly recognized by the public. While creating a lot of commercial value for enterprises, it also brings huge challenges to information security and privacy. In the big data environment, data has become an important medium for corporate decision-making, and information security and privacy protection have become the “army battleground” in corporate competition. Therefore, information security and privacy protection are getting more and more attention from enterprises, which also determines whether enterprises can occupy a place in the fiercely competitive market. This article analyzes the information security and privacy protection issues of enterprises in the big data environment from three aspects. Starting from the importance and significance of big data protection, it analyzes the security and privacy issues of big data in enterprise applications, and finally conducts information security and privacy protection for enterprises. Privacy protection puts forward relevant suggestions.

The importance of the security of industrial control network has become increasingly prominent. Aiming at the defects of main security protection system in the intelligent manufacturing industrial control network, we propose a security attack risk detection and defense, and emergency processing capability synchronization technology system suitable for the intelligent manufacturing industrial control system. Integrating system control and network security theories, a flexible and reconfigurable system-wide security architecture method is proposed. On the basis of considering the high availability and strong real-time of the system, our research centers on key technologies supporting system-wide security analysis, defense strategy deployment and synchronization, including weak supervision system reinforcement and pattern matching, etc.. Our research is helpful to solve the problem of industrial control network of “old but full of loopholes” caused by the long-term closed development of the production network of important parts, and alleviate the contradiction between the high availability of the production system and the relatively backward security defense measures.