Biblio

The rapid development of low-power integrated circuits, wireless communication, intelligent sensors and microelectronics has allowed the realization of wireless body area networks (WBANs), which can monitor patients' vital body parameters remotely in real time to offer timely treatment. These vital body parameters are related to patients' life and health; and these highly private data are subject to many security threats. To guarantee privacy, many secure communication protocols have been proposed. However, most of these protocols have a one-to-one structure in extra-body communication and cannot support multidisciplinary team (MDT). Hence, we propose a lightweight and certificateless multi-receiver secure data transmission protocol for WBANs to support MDT treatment in this paper. In particular, a novel multi-receiver certificateless generalized signcryption (MR-CLGSC) scheme is proposed that can adaptively use only one algorithm to implement one of three cryptographic primitives: signature, encryption or signcryption. Then, a multi-receiver secure data transmission protocol based on the MR-CLGSC scheme with many security properties, such as data integrity and confidentiality, non-repudiation, anonymity, forward and backward secrecy, unlinkability and data freshness, is designed. Both security analysis and performance analysis show that the proposed protocol for WBANs is secure, efficient and highly practical.

Trust network is widely considered to be one of the most important aspects of social networks. It has many applications in the field of recommender systems and opinion formation. Few researchers have addressed the problem of trust/distrust prediction and, it has not yet been established whether the similarity measures can do trust prediction. The present paper aims to validate that similar users have related trust relationships. To predict trust relations between two users, the LookLike algorithm was introduced. Then we used the LookLike algorithm results as new features for supervised classifiers to predict the trust/distrust label. We chose a list of similarity measures to examined our claim on four real-world trust network datasets. The results demonstrated that there is a strong correlation between users' similarity and their opinion on trust networks. Due to the tight relation between trust prediction and truth discovery, we believe that our similarity-based algorithm could be a promising solution in their challenging domains.

This article describes the process assembling of Encryption & Decryption In Trusted Platform Module of encoding information in NM640X \textregistered. Encoding of information, carried out in assembly language according to Gost 28147-89. It is a realisation of standard GOST 28147-89- Russian state symmetric key block cipher. GOST 28147-89 has 64-bit to access the kernel, trust, and allocated memory in the BlockSize and 256-bit KeySize.

Advancements in the AI field unfold tremendous opportunities for society. Simultaneously, it becomes increasingly important to address emerging ramifications. Thereby, the focus is often set on ethical and safe design forestalling unintentional failures. However, cybersecurity-oriented approaches to AI safety additionally consider instantiations of intentional malice – including unethical malevolent AI design. Recently, an analogous emphasis on malicious actors has been expressed regarding security and safety for virtual reality (VR). In this vein, while the intersection of AI and VR (AIVR) offers a wide array of beneficial cross-fertilization possibilities, it is responsible to anticipate future malicious AIVR design from the onset on given the potential socio-psycho-technological impacts. For a simplified illustration, this paper analyzes the conceivable use case of Generative AI (here deepfake techniques) utilized for disinformation in immersive journalism. In our view, defenses against such future AIVR safety risks related to falsehood in immersive settings should be transdisciplinarily conceived from an immersive co-creation stance. As a first step, we motivate a cybersecurity-oriented procedure to generate defenses via immersive design fictions. Overall, there may be no panacea but updatable transdisciplinary tools including AIVR itself could be used to incrementally defend against malicious actors in AIVR.

The increasing of malwares has presented a serious threat to the security of computer systems in recent years. Traditional signature-based anti-virus systems are not able to detect metamorphic and previously unseen malwares and it inspires people to use machine learning methods such as Naive Bayes and Decision Tree to identity malicious executables. Among these methods, detecting malwares by using Support Vector Machine (SVM) is one of the most effective approaches. However, the parameters of SVM have serious impacts on its classification performance. In order to find the optimal parameter combination and avoid the problem of falling into local optimal solution, many methods based on evolutionary algorithms are proposed, including Particle Swarm Optimization (PSO), Genetic Algorithm (GA), Differential Evolution (DE) and others. But these algorithms still face the problem of being trapped into local solution spaces in different degree. In this paper, an improved fireworks algorithm is presented and applied to search parameters of SVM: penalty factor c and kernel function parameter g. To research the performance of the proposed algorithm, numeric experiments are made and compared with some typical algorithms, the experimental results demonstrate it outperforms other algorithms.

Moving target defense technology is one of the revolutionary techniques that is “changing the rules of the game” in the field of network technology, according to recent propositions from the US Science and Technology Commission. Building upon a recently-developed approach called Self Cleansing Intrusion Tolerance (SCIT), this paper proposes a moving target defense system that is based on server switching and cleaning. A protected object is maneuvered to improve its safety by exploiting software diversity and thereby introducing randomness and unpredictability into the system. Experimental results show that the improved system increases the difficulty of attack and significantly reduces the likelihood of a system being invaded, thus serving to enhance system security.

In this paper, we present the concept of boosting the resiliency of optimization-based observers for cyber-physical systems (CPS) using auxiliary sources of information. Due to the tight coupling of physics, communication and computation, a malicious agent can exploit multiple inherent vulnerabilities in order to inject stealthy signals into the measurement process. The problem setting considers the scenario in which an attacker strategically corrupts portions of the data in order to force wrong state estimates which could have catastrophic consequences. The goal of the proposed observer is to compute the true states in-spite of the adversarial corruption. In the formulation, we use a measurement prior distribution generated by the auxiliary model to refine the feasible region of a traditional compressive sensing-based regression problem. A constrained optimization-based observer is developed using l1-minimization scheme. Numerical experiments show that the solution of the resulting problem recovers the true states of the system. The developed algorithm is evaluated through a numerical simulation example of the IEEE 14-bus system.

Intrusion detection system based on representation learning is the main research direction in the field of anomaly detection. Malicious traffic detection system can distinguish normal and malicious traffic by learning representations between normal and malicious traffic. However, under the context of big data, there are many types of malicious traffic, and the features are also changing constantly. It is still a urgent problem to design a detection model that can effectively learn and summarize the feature of normal traffic and accurately identify the features of new kinds of malicious traffic.in this paper, a malicious traffic detection method based on Deep Support Vector Data Description is proposed, which is called Deep - SVDD. We combine convolutional neural network (CNN) with support vector data description, and train the model with normal traffic. The normal traffic features are mapped to high-dimensional space through neural networks, and a compact hypersphere is trained by unsupervised learning, which includes the normal features of the highdimensional space. Malicious traffic fall outside the hypersphere, thus distinguishing between normal and malicious traffic. Experiments show that the model has a high detection rate and a low false alarm rate, and it can effectively identify new malicious traffic.

The existing network intrusion detection methods have less label samples in the training process, and the detection accuracy is not high. In order to solve this problem, this paper designs a network intrusion detection method based on the GAN model by using the adversarial idea contained in the GAN. The model enhances the original training set by continuously generating samples, which expanding the label sample set. In order to realize the multi-classification of samples, this paper transforms the previous binary classification model of the generated adversarial network into a supervised learning multi-classification model. The loss function of training is redefined, so that the corresponding training method and parameter setting are obtained. Under the same experimental conditions, several performance indicators are used to compare the detection ability of the proposed method, the original classification model and other models. The experimental results show that the method proposed in this paper is more stable, robust, accurate detection rate, has good generalization ability, and can effectively realize network intrusion detection.

The insurance business plays a quite significant role in people's lives, but in the process of claim settlement, there are still various frauds such that the insurance companies' refusal to compensate or customers' malicious fraud to obtain compensation. Therefore, it is very important to ensure fair and just claims. In this paper, by combining the blockchain technology and the ciphertext-policy attribute-based encryption system, we build a scheme for secure storage and update for insurance records under the InterPlanetary File System (IPFS) storage environment in the insurance system. In this scheme, we use the fog node to outsource encryption of insurance records to improve the efficiency of the staff; In addition, we store encrypted insurance records on IPFS to ensure the security of the storage platform and avoid the single point failure of the centralized mechanism. In addition, we use the immutability of the blockchain to achieve the non-repudiation of both insurance companies and the client. The security proof shows that the proposed scheme can achieve selective security against selected keyword attacks. Our scheme is efficient and feasible under performance analysis and real data set experiments.

Most anti-collusion audio fingerprinting schemes are aiming at finding colluders from the illegal redistributed audio copies. However, the loss caused by the redistributed versions is inevitable. In this letter, a novel fingerprinting scheme is proposed to eliminate the motivation of collusion attack. The audio signal is transformed to the frequency domain by the Fourier transform, and the coefficients in frequency domain are reversed in different degrees according to the fingerprint sequence. Different from other fingerprinting schemes, the coefficients of the host media are excessively modified by the proposed method in order to reduce the quality of the colluded version significantly, but the imperceptibility is well preserved. Experiments show that the colluded audio cannot be reused because of the poor quality. In addition, the proposed method can also resist other common attacks. Various kinds of copyright risks and losses caused by the illegal redistribution are effectively avoided, which is significant for protecting the copyright of audio.

The increased power capacity and networking requirements in Extremely Fast Charging (XFC) systems for battery electric vehicles (BEVs) and the resulting increase in the adversarial attack surface call for security measures to be taken in the involved cyber-physical system (CPS). Within this system, the security of the BEV's battery management system (BMS) is of critical importance as the BMS is the first line of defense between the vehicle and the charge station. This study proposes an optimal control and moving-target defense (MTD) based novel approach for the security of the vehicle BMS) focusing on the charging process, during which a compromised vehicle may contaminate the XFC station and the whole grid. This paper is part of our ongoing research, which is one of the few, if not the first, reported studies in the literature on security-hardened BMS, aiming to increase the security and performance of operations between the charging station, the BMS and the battery system of electric vehicles. The developed MTD based switching strategy makes use of redundancies in the controller and feedback design. The performed simulations demonstrate an increased unpredictability and acceptable charging performance under adversarial attacks.

Increased penetration of distributed energy resources (DERs) creates challenges in formulating the security constrained optimal power flow (SCOPF) problem as the number of models for these resources proliferate. Specifically, the number of devices with different mathematical models is large and their integration into the SCOPF becomes tedious. Henceforth, a process that seamlessly models and integrates such new devices into the SCOPF problem is needed. We propose an object-oriented modeling approach that leads to the autonomous formation of the SCOPF problem. All device models in the system are cast into a universal syntax. We have also introduced a quadratization method which makes the models consisting of linear and quadratic equations, if nonlinear. We refer to this model as the State and Control Quadratized Device Model (SCQDM). The SCQDM includes a number of equations and a number of inequalities expressing the operating limits of the device. The SCOPF problem is then formed in a seamless manner by operating only on the SCQDM device objects. The SCOPF problem, formed this way, is also quadratic (i.e. consists of linear and quadratic equations), and of the same form and syntax as the SCQDM for an individual device. For this reason, we named it security constrained quadratic optimal power flow (SCQOPF). We solve the SCQOPF problem using a sequential linear programming (SLP) algorithm and compare the results with those obtained from the commercial solver Knitro on the IEEE 57 bus system.

Research on the design of data center infrastructure is increasing, both from academia and industry, due to the rapid development of cloud-based applications such as search engines, social networks, and large-scale computing. On a large scale, data centers can consist of hundreds to thousands of servers that require systems with high-performance requirements and low downtime. To meet the network's needs in a dynamic data center, infrastructure of applications and services are growing. It takes a process of designing a network topology so that it can guarantee availability and security. One way to surmount this is by implementing the zero trust security model based on micro-segmentation. Zero trust is a security idea based on the principle of "never trust, always verify" in which no concepts of trust and untrust in network traffic. The zero trust security model implemented network traffic in the form of untrust. Micro-segmentation is a way to achieve zero trust by dividing a network into smaller logical segments to restrict the traffic. In this research, data center network performance based on software-defined networking with zero trust security model using micro-segmentation has been evaluated using a testbed simulation of Cisco Application Centric Infrastructure by measuring the round trip time, jitter, and packet loss during experiments. Performance evaluation results show that micro-segmentation adds an average round trip time of 4 μs and jitter of 11 μs without packet loss so that the security can be improved without significantly affecting network performance on the data center.

Recently, several cross-layer protocols have been designed for vehicular networks to optimize data dissemination by ensuring internal communications between routing and MAC layers. In this context, a cross-layer protocol, called TDMA-aware Routing Protocol for Multi-hop communications (TRPM), was proposed in order to efficiently select a relay node based on time slot scheduling information obtained from the MAC layer. However, due to the constant evolution of cyber-attacks on the routing and MAC layers, data dissemination in vehicular networks is vulnerable to several types of attack. In this paper, we identify the different attack models that can disrupt the cross-layer operation of the TRPM protocol and assess their impact on performance through simulation. Several new vulnerabilities related to the MAC slot scheduling process are identified. Exploiting of these vulnerabilities would lead to severe channel capacity wastage where up to half of the free slots could not be reserved.

The article is devoted to the choice of personal means of the 5G defense in dependence of hard- and software available to the user. The universal module MS 127.04 and its software compatible unit can be universally configured for use. An intelligent hardware and software platform is proposed for multi-core setting of policies for the automatic encryption of confidential data and selective blocking related to the implementation of computing security and confidentiality of data transfer, using such additional specially. A platform that resists the external influences is described. The platform is based on a universal module MS 127.05 (produced in Russia), that is a heterogeneous multiprocessor system on a chip), the system features 16 processor cores (NeuroMatrix Core 4) and five ARM Cortex-A5 units (ULSI 1879VM8Ya.

To ensure quality of service and user experience, large Internet companies often monitor various Key Performance Indicators (KPIs) of their systems so that they can detect anomalies and identify failure in real time. However, due to a large number of various KPIs and the lack of high-quality labels, existing KPI anomaly detection approaches either perform well only on certain types of KPIs or consume excessive resources. Therefore, to realize generic and practical KPI anomaly detection in the real world, we propose a KPI anomaly detection framework named iRRCF-Active, which contains an unsupervised and white-box anomaly detector based on Robust Random Cut Forest (RRCF), and an active learning component. Specifically, we novelly propose an improved RRCF (iRRCF) algorithm to overcome the drawbacks of applying original RRCF in KPI anomaly detection. Besides, we also incorporate the idea of active learning to make our model benefit from high-quality labels given by experienced operators. We conduct extensive experiments on a large-scale public dataset and a private dataset collected from a large commercial bank. The experimental resulta demonstrate that iRRCF-Active performs better than existing traditional statistical methods, unsupervised learning methods and supervised learning methods. Besides, each component in iRRCF-Active has also been demonstrated to be effective and indispensable.

This article presents the modeling results of the ability to improve the accuracy of predicting the state of information security in the space of parameters of its threats. Information security of the protected object is considered as a dynamic system. Security threats to the protected object are used as the security system parameters most qualitatively and fully describing its behavior. The number of threats considered determines the dimension of the security state space. Based on the dynamic properties of changes in information security threats, the space region of the security system possible position at the moments of subsequent measurements of its state (a comprehensive security audit) is predicted. The corrected state of the information security system is considered to be the intersection of the area of subsequent measurement of the state of the system (integrated security audit) with the previously predicted area of the parameter space. Such a way to increase the accuracy of determining the state of a dynamic system in the space of its parameters can be called dynamic recurrent correction method. It is possible to use this method if the comprehensive security audit frequency is significantly higher than the frequency of monitoring changes in the dynamics of specific threats to information security. In addition, the data of the audit results and the errors of their receipt must be statistically independent with the results of monitoring changes in the dynamics of specific threats to information security. Improving the accuracy of the state of information security assessment in the space of the parameters of its threats can be used for various applications, including clarification of the communication channels characteristics, increasing the availability and efficiency of the telecommunications network, if it is an object of protection.

The paper discusses a prototype of a database, which can be used for operation in a decentralized mode for an information system. In this project, the focus is on creation of a data structure model that provides flexibility of business processes. The research is based on the development of a model for decentralized access rights distribution by including users in groups where they are assigned similar roles using consensus of other group members. This paper summarizes the main technologies that were used to ensure information security of the decentralized storage, the mechanisms for fixing access rights to an object access (the minimum entity of the system), describes a process of the data access control at the role level and an algorithm for managing the consensus for applying changes.

In view of the security threats caused by the code execution vulnerability of the industrial control system, design the trusted security architecture of the industrial control system based on the embedded system. From the trusted startup of industrial control equipment, the safety protection for industrial control system is completed. The scheme is based on TPM and Xilinx Zynq-7030 to build an industrial trusted computing environment and complete the trusted startup process. Experiment shows that this method can effectively prevent the destruction of malicious code during the startup process of embedded system and provide technical support for the construction of trusted computing environment of industrial control system.

Compared with traditional databases such as Oracle database, SQL Server database and MySQL database, Dameng database is a domestic database with independent intellectual property rights. Combined with the security management of Dameng database and the requirement of database audit, this paper designs the security configuration baseline of Dameng database. By designing the security configuration baseline of Dameng database, the audit work of Dameng database can be carried out efficiently, and by analyzing the audit results, the security configuration baseline of Dameng database can be improved.

In view of the problem that the overall security of the network is difficult to evaluate quantitatively, we propose the edge authority attack graph model, which aims to make up for the traditional dependence attack graph to describe the relationship between vulnerability behaviors. This paper proposed a network security metrics based on probability, and proposes a network vulnerability algorithm based on vulnerability exploit probability and attack target asset value. Finally, a network security reinforcement algorithm with network vulnerability index as the optimization target is proposed based on this metric algorithm.

Energy Internet is a typical cyber-physical system (CPS), in which the disturbance on cyber part may result in the operation risks on the physical part. In order to perform CPS assessment and research the interactive influence between cyber part and physical part, an integrated energy internet CPS model which adopts information flow matrix, energy control flow matrix and information energy hybrid flow matrix is proposed in this paper. The proposed model has a higher computational efficacy compared with simulation based approaches. Then, based on the proposed model, the influence of cyber disturbances such as data dislocation, data delay and data error on the physical part are studied. Finally, a 3 MW PET based energy internet CPS is built using PSCAD/EMTDC software. The simulation results prove the validity of the proposed model and the correctness of the interactive influence analysis.

ARP-attack often occurs in LAN network [1], which directly affects the user's online experience. The common type of ARP-attack is MITM-Attack (Man-in-the-Middle Attack) with two-types, disguising a host or a gateway. Common means of ARP-attack prevention is by deploying network-security equipment or binding IP-MAC in LAN manually[10]. This paper studies an automatic ARP-attack prevention technology for multi-object, based on the domain-control technology and batch-processing technology. Compared with the common ARP-attack-prevention measure, this study has advantages of low-cost, wide-application, and maintenance-free. By experimentally researching, this paper demonstrates the research correctness and technical feasibility. This research result, multi-object-oriented automatic defense technology for ARP-attacking, can apply to enterprise network.

Software-Defined Networks (SDN) are being adopted widely and are also likely to be deployed as the infrastructure of systems with critical real-time properties such as Industrial Control Systems (ICS). This raises the question of what security and performance guarantees can be given for the data plane of such critical systems and whether any control plane actions will adversely affect these guarantees, particularly for quality of service in real-time systems. In this paper we study the existing literature on the analysis of SDN using queueing networks and show ways in which models need to be extended to study attacks that are based on arrival rates and service time distributions of flows in SDN.