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2022-01-10
Schrenk, Bernhard.  2021.  Simplified Synaptic Receptor for Coherent Optical Neural Networks. 2021 IEEE Photonics Society Summer Topicals Meeting Series (SUM). :1–2.
Advancing artificial neural networks to the coherent optical domain offers several advantages, such as a filterless synaptic interconnect with increased routing flexibility. Towards this direction, a coherent synaptic receptor with integrated multiplication function will be experimentally evaluated for a 1-GHz train of 130-ps spikes.
Viktoriia, Hrechko, Hnatienko, Hrygorii, Babenko, Tetiana.  2021.  An Intelligent Model to Assess Information Systems Security Level. 2021 Fifth World Conference on Smart Trends in Systems Security and Sustainability (WorldS4). :128–133.
This research presents a model for assessing information systems cybersecurity maturity level. The main purpose of the model is to provide comprehensive support for information security specialists and auditors in checking information systems security level, checking security policy implementation, and compliance with security standards. The model synthesized based on controls and practices present in ISO 27001 and ISO 27002 and the neural network of direct signal propagation. The methodology described in this paper can also be extended to synthesis a model for different security control sets and, consequently, to verify compliance with another security standard or policy. The resulting model describes a real non-automated process of assessing the maturity of an IS at an acceptable level and it can be recommended to be used in the process of real audit of Information Security Management Systems.
Paul, Avishek, Islam, Md Rabiul.  2021.  An Artificial Neural Network Based Anomaly Detection Method in CAN Bus Messages in Vehicles. 2021 International Conference on Automation, Control and Mechatronics for Industry 4.0 (ACMI). :1–5.
Controller Area Network is the bus standard that works as a central system inside the vehicles for communicating in-vehicle messages. Despite having many advantages, attackers may hack into a car system through CAN bus, take control of it and cause serious damage. For, CAN bus lacks security services like authentication, encryption etc. Therefore, an anomaly detection system must be integrated with CAN bus in vehicles. In this paper, we proposed an Artificial Neural Network based anomaly detection method to identify illicit messages in CAN bus. We trained our model with two types of attacks so that it can efficiently identify the attacks. When tested, the proposed algorithm showed high performance in detecting Denial of Service attacks (with accuracy 100%) and Fuzzy attacks (with accuracy 99.98%).
Sallam, Youssef F., Ahmed, Hossam El-din H., Saleeb, Adel, El-Bahnasawy, Nirmeen A., El-Samie, Fathi E. Abd.  2021.  Implementation of Network Attack Detection Using Convolutional Neural Network. 2021 International Conference on Electronic Engineering (ICEEM). :1–6.
The Internet obviously has a major impact on the global economy and human life every day. This boundless use pushes the attack programmers to attack the data frameworks on the Internet. Web attacks influence the reliability of the Internet and its administrations. These attacks are classified as User-to-Root (U2R), Remote-to-Local (R2L), Denial-of-Service (DoS) and Probing (Probe). Subsequently, making sure about web framework security and protecting data are pivotal. The conventional layers of safeguards like antivirus scanners, firewalls and proxies, which are applied to treat the security weaknesses are insufficient. So, Intrusion Detection Systems (IDSs) are utilized to screen PC and data frameworks for security shortcomings. IDS adds more effectiveness in securing networks against attacks. This paper presents an IDS model based on Deep Learning (DL) with Convolutional Neural Network (CNN) hypothesis. The model has been evaluated on the NSLKDD dataset. It has been trained by Kddtrain+ and tested twice, once using kddtrain+ and the other using kddtest+. The achieved test accuracies are 99.7% and 98.43% with 0.002 and 0.02 wrong alert rates for the two test scenarios, respectively.
Jianhua, Xing, Jing, Si, Yongjing, Zhang, Wei, Li, Yuning, Zheng.  2021.  Research on Malware Variant Detection Method Based on Deep Neural Network. 2021 IEEE 5th International Conference on Cryptography, Security and Privacy (CSP). :144–147.
To deal with the increasingly serious threat of industrial information malicious code, the simulations and characteristics of the domestic security and controllable operating system and office software were implemented in the virtual sandbox environment based on virtualization technology in this study. Firstly, the serialization detection scheme based on the convolution neural network algorithm was improved. Then, the API sequence was modeled and analyzed by the improved convolution neural network algorithm to excavate more local related information of variant sequences. Finally the variant detection of malicious code was realized. Results showed that this improved method had higher efficiency and accuracy for a large number of malicious code detection, and could be applied to the malicious code detection in security and controllable operating system.
Abdullah, Rezhna M., Abdullah, Syamnd M., Abdullah, Saman M..  2021.  Neighborhood Component Analysis and Artificial Neural Network for DDoS Attack Detection over IoT Networks. 2021 7th International Engineering Conference ``Research Innovation amid Global Pandemic" (IEC). :1–6.
Recently, modern networks have been made up of connections of small devices that have less memory, small CPU capability, and limited resources. Such networks apparently known as Internet of Things networks. Devices in such network promising high standards of live for human, however, they increase the size of threats lead to bring more risks to network security. One of the most popular threats against such networks is known as Distributed Denial of Service (DDoS). Reports from security solution providers show that number of such attacks are in increase considerably. Therefore, more researches on detecting the DDoS attacks are necessary. Such works need monitoring network packets that move over Internet and networks and, through some intelligent techniques, monitored packets could be classified as benign or as DDoS attack. This work focuses on combining Neighborhood Component Analysis and Artificial Neural Network-Backpropagation to classify and identify packets as forward by attackers or as come from authorized and illegible users. This work utilized the activities of four type of the network protocols to distinguish five types of attacks from benign packets. The proposed model shows the ability of classifying packets to normal or to attack classes with an accuracy of 99.4%.
Gong, Jianhu.  2021.  Network Information Security Pipeline Based on Grey Relational Cluster and Neural Networks. 2021 5th International Conference on Computing Methodologies and Communication (ICCMC). :971–975.
Network information security pipeline based on the grey relational cluster and neural networks is designed and implemented in this paper. This method is based on the principle that the optimal selected feature set must contain the feature with the highest information entropy gain to the data set category. First, the feature with the largest information gain is selected from all features as the search starting point, and then the sample data set classification mark is fully considered. For the better performance, the neural networks are considered. The network learning ability is directly determined by its complexity. The learning of general complex problems and large sample data will bring about a core dramatic increase in network scale. The proposed model is validated through the simulation.
Agarwal, Shivam, Khatter, Kiran, Relan, Devanjali.  2021.  Security Threat Sounds Classification Using Neural Network. 2021 8th International Conference on Computing for Sustainable Global Development (INDIACom). :690–694.
Sound plays a key role in human life and therefore sound recognition system has a great future ahead. Sound classification and identification system has many applications such as system for personal security, critical surveillance, etc. The main aim of this paper is to detect and classify the security sound event using the surveillance camera systems with integrated microphone based on the generated spectrograms of the sounds. This will enable to track security events in cases of emergencies. The goal is to propose a security system to accurately detect sound events and make a better security sound event detection system. We propose to use a convolutional neural network (CNN) to design the security sound detection system to detect a security event with minimal sound. We used the spectrogram images to train the CNN. The neural network was trained using different security sounds data which was then used to detect security sound events during testing phase. We used two datasets for our experiment training and testing datasets. Both the datasets contain 3 different sound events (glass break, gun shots and smoke alarms) to train and test the model, respectively. The proposed system yields the good accuracy for the sound event detection even with minimum available sound data. The designed system achieved accuracy was 92% and 90% using CNN on training dataset and testing dataset. We conclude that the proposed sound classification framework which using the spectrogram images of sounds can be used efficiently to develop the sound classification and recognition systems.
Zheng, Shiji.  2021.  Network Intrusion Detection Model Based on Convolutional Neural Network. 2021 IEEE 5th Advanced Information Technology, Electronic and Automation Control Conference (IAEAC). 5:634–637.
Network intrusion detection is an important research direction of network security. The diversification of network intrusion mode and the increasing amount of network data make the traditional detection methods can not meet the requirements of the current network environment. The development of deep learning technology and its successful application in the field of artificial intelligence provide a new solution for network intrusion detection. In this paper, the convolutional neural network in deep learning is applied to network intrusion detection, and an intelligent detection model which can actively learn is established. The experiment on KDD99 data set shows that it can effectively improve the accuracy and adaptive ability of intrusion detection, and has certain effectiveness and advancement.
Wang, Xiaoyu, Han, Zhongshou, Yu, Rui.  2021.  Security Situation Prediction Method of Industrial Control Network Based on Ant Colony-RBF Neural Network. 2021 IEEE 2nd International Conference on Big Data, Artificial Intelligence and Internet of Things Engineering (ICBAIE). :834–837.
To understand the future trend of network security, the field of network security began to introduce the concept of NSSA(Network Security Situation Awareness). This paper implements the situation assessment model by using game theory algorithms to calculate the situation value of attack and defense behavior. After analyzing the ant colony algorithm and the RBF neural network, the defects of the RBF neural network are improved through the advantages of the ant colony algorithm, and the situation prediction model based on the ant colony-RBF neural network is realized. Finally, the model was verified experimentally.
2021-11-08
Karode, Tanakorn, Werapun, Warodom.  2020.  Performance Analysis of Trustworthy Online Review System Using Blockchain. 2020 17th International Conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology (ECTI-CON). :510–513.
Today, the online review system cannot fully support the business since there are fraudulent activities inside. The companies that get low score reviews are induced to raise their score for the market competition capability by paying to the platform for deleting or editing the posted reviews. Moreover, the automatic filtration system of a platform removes some reviews without the awareness of the users. The low transparency platform causes low credibility toward the reviews. Blockchain technology provides exceptionally high transparency since every action can be traced publicly. However, there are some tradeoffs that need to be considered, such as cost and response time. This work tends to find the potential of using Blockchain technology in the online review system by testing four implementation approaches of the Ethereum Smart Contract. The result illustrates that using IPFS to store the data is a practical way of reducing transaction costs. Besides, preventing using Smart Contract states can significantly reduce costs too. The response time for using the Blockchain and IPFS system is slower than the centralized system. However, posting a review does not need a fast response. Thus, it is worthy of trading response time with transparency and cost. In the business view, the review posting with cost causes more difficulty to generate fake reviews. Moreover, there are other advantages over the centralized system, such as the reward system, bogus review voting, and global database. Thus, credibility improvement for a consumer online review system is a potential application of Blockchain technology.
Ganguli, Subhankar, Thakur, Sanjeev.  2020.  Machine Learning Based Recommendation System. 2020 10th International Conference on Cloud Computing, Data Science Engineering (Confluence). :660–664.
Recommender system helps people in decision making by asking their preferences about various items and recommends other items that have not been rated yet and are similar to their taste. A traditional recommendation system aims at generating a set of recommendations based on inter-user similarity that will satisfy the target user. Positive preferences as well as negative preferences of the users are taken into account so as to find strongly related users. Weighted entropy is usedz as a similarity measure to determine the similar taste users. The target user is asked to fill in the ratings so as to identify the closely related users from the knowledge base and top N recommendations are produced accordingly. Results show a considerable amount of improvement in accuracy after using weighted entropy and opposite preferences as a similarity measure.
Varshney, Kush R..  2020.  On Mismatched Detection and Safe, Trustworthy Machine Learning. 2020 54th Annual Conference on Information Sciences and Systems (CISS). :1–4.
Instilling trust in high-stakes applications of machine learning is becoming essential. Trust may be decomposed into four dimensions: basic accuracy, reliability, human interaction, and aligned purpose. The first two of these also constitute the properties of safe machine learning systems. The second dimension, reliability, is mainly concerned with being robust to epistemic uncertainty and model mismatch. It arises in the machine learning paradigms of distribution shift, data poisoning attacks, and algorithmic fairness. All of these problems can be abstractly modeled using the theory of mismatched hypothesis testing from statistical signal processing. By doing so, we can take advantage of performance characterizations in that literature to better understand the various machine learning issues.
Marino, Daniel L., Grandio, Javier, Wickramasinghe, Chathurika S., Schroeder, Kyle, Bourne, Keith, Filippas, Afroditi V., Manic, Milos.  2020.  AI Augmentation for Trustworthy AI: Augmented Robot Teleoperation. 2020 13th International Conference on Human System Interaction (HSI). :155–161.
Despite the performance of state-of-the-art Artificial Intelligence (AI) systems, some sectors hesitate to adopt AI because of a lack of trust in these systems. This attitude is prevalent among high-risk areas, where there is a reluctance to remove humans entirely from the loop. In these scenarios, Augmentation provides a preferred alternative over complete Automation. Instead of replacing humans, AI Augmentation uses AI to improve and support human operations, creating an environment where humans work side by side with AI systems. In this paper, we discuss how AI Augmentation can provide a path for building Trustworthy AI. We exemplify this approach using Robot Teleoperation. We lay out design guidelines and motivations for the development of AI Augmentation for Robot Teleoperation. Finally, we discuss the design of a Robot Teleoperation testbed for the development of AI Augmentation systems.
He, Hongmei, Gray, John, Cangelosi, Angelo, Meng, Qinggang, McGinnity, T. M., Mehnen, Jörn.  2020.  The Challenges and Opportunities of Artificial Intelligence for Trustworthy Robots and Autonomous Systems. 2020 3rd International Conference on Intelligent Robotic and Control Engineering (IRCE). :68–74.
Trust is essential in designing autonomous and semiautonomous Robots and Autonomous Systems (RAS), because of the ``No trust, no use'' concept. RAS should provide high quality services, with four key properties that make them trustworthy: they must be (i) robust with regards to any system health related issues, (ii) safe for any matters in their surrounding environments, (iii) secure against any threats from cyber spaces, and (iv) trusted for human-machine interaction. This article thoroughly analyses the challenges in implementing the trustworthy RAS in respects of the four properties, and addresses the power of AI in improving the trustworthiness of RAS. While we focus on the benefits that AI brings to human, we should realize the potential risks that could be caused by AI. This article introduces for the first time the set of key aspects of human-centered AI for RAS, which can serve as a cornerstone for implementing trustworthy RAS by design in the future.
Zhao, Zhiming, Rong, Chunming, Jaatun, Martin Gilje.  2020.  A Trustworthy Blockchain-Based Decentralised Resource Management System in the Cloud. 2020 IEEE 26th International Conference on Parallel and Distributed Systems (ICPADS). :617–624.
Quality Critical Decentralised Applications (QC-DApp) have high requirements for system performance and service quality, involve heterogeneous infrastructures (Clouds, Fogs, Edges and IoT), and rely on the trustworthy collaborations among participants of data sources and infrastructure providers to deliver their business value. The development of the QCDApp has to tackle the low-performance challenge of the current blockchain technologies due to the low collaboration efficiency among distributed peers for consensus. On the other hand, the resilience of the Cloud has enabled significant advances in software-defined storage, networking, infrastructure, and every technology; however, those rich programmabilities of infrastructure (in particular, the advances of new hardware accelerators in the infrastructures) can still not be effectively utilised for QCDApp due to lack of suitable architecture and programming model.
Liu, Qian, de Simone, Robert, Chen, Xiaohong, Kang, Jiexiang, Liu, Jing, Yin, Wei, Wang, Hui.  2020.  Multiform Logical Time Amp; Space for Mobile Cyber-Physical System With Automated Driving Assistance System. 2020 27th Asia-Pacific Software Engineering Conference (APSEC). :415–424.
We study the use of Multiform Logical Time, as embodied in Esterel/SyncCharts and Clock Constraint Specification Language (CCSL), for the specification of assume-guarantee constraints providing safe driving rules related to time and space, in the context of Automated Driving Assistance Systems (ADAS). The main novelty lies in the use of logical clocks to represent the epochs of specific area encounters (when particular area trajectories just start overlapping for instance), thereby combining time and space constraints by CCSL to build safe driving rules specification. We propose the safe specification pattern at high-level that provide the required expressiveness for safe driving rules specification. In the pattern, multiform logical time provides the power of parameterization to express safe driving rules, before instantiation in further simulation contexts. We present an efficient way to irregularly update the constraints in the specification due to the context changes, where elements (other cars, road sections, traffic signs) may dynamically enter and exit the scene. In this way, we add constraints for the new elements and remove the constraints related to the disappearing elements rather than rebuild everything. The multi-lane highway scenario is used to illustrate how to irregularly and efficiently update the constraints in the specification while receiving a fresh scene.
Martin, Robert Alan.  2020.  Assurance for CyberPhysical Systems: Addressing Supply Chain Challenges to Trustworthy Software-Enabled Things. 2020 IEEE Systems Security Symposium (SSS). :1–5.
Software is playing a pivotal role in most enterprises, whether they realize it or not, and with the proliferation of Industrial Internet of Things (IoT) and other CyberPhysical systems across our society and critical infrastructure and our collective love affair with automation, optimization, and ``smart'' devices, the role of these types of systems is only going to increase. This talk addresses the myriad of issues that underlie unsafe, insecure, and unreliable software and provides the insights of the Industrial Internet Consortium and other government and industry efforts on how to conquer them and pave the way to a marketplace of trustworthy software-enabled connected things. As the experience of several sectors has shown, the dependence on connected software needs to be met with a strong understanding of the risks to the overall trustworthiness of our software-based capabilities that we, our enterprises, and our world utilize. In many of these new connected systems issues of safety, reliability, and resilience rival or dominate concerns for security and privacy, the long-time focus of many in the IT world. Without a scalable and efficient method for managing these risks so our enterprises can continue to benefit from these advancements that powers our military, commercial industries, cities, and homes to new levels of efficiency, versatility, and cost effectiveness we face the potential for harm, death, and destructiveness. In such a marketplace, creating, exchanging, and integrating components that are trustworthy as well as entering into value-chain relationships with trustworthy partners and service suppliers will be common if we can provide a method for explicitly defining what is meant by the word trustworthy. The approach being pursued by these groups for applying Software Assurance to these systems and their Supply Chains by leveraging Structured Assurance Cases (the focus of this paper), Software Bill of Materials, and secure development practices applied to the evolving Agile and DevSecOps methodologies, is to explicitly identify the detailed requirements ``about what we need to know about something for it to be worthy of our trust'' and to do that in a way that we can convey that basis of trust to others that: can scale; is consistent within different workflows; is flexible to differing sets of hazards and environments; and is applicable to all sectors, domains, and industries.
Martin, Robert Alan.  2020.  Visibility Amp; Control: Addressing Supply Chain Challenges to Trustworthy Software-Enabled Things. 2020 IEEE Systems Security Symposium (SSS). :1–4.
Software is playing a pivotal role in most enterprises, whether they realize it or not, and with the proliferation of Industrial Internet of Things (IoT) and other cyber/physical systems across our society and critical infrastructure and our collective love affair with automation, optimization, and ``smart'' devices, the role of these types of systems is only going to increase. This talk addresses the myriad of issues that underlie unsafe, insecure, and unreliable software and provides the insights of the Industrial Internet Consortium and other government and industry efforts on how to conquer them and pave the way to a marketplace of trustworthy software-enabled connected things.As the experience of several sectors has shown, the dependence on connected software needs to be met with a strong understanding of the risks to the overall trustworthiness of our software-based capabilities that we, our enterprises, and our world utilize. In many of these new connected systems issues of safety, reliability, and resilience rival or dominate concerns for security and privacy, the long-time focus of many in the IT world. Without a scalable and efficient method for managing these risks so our enterprises can continue to benefit from these advancements that powers our military, commercial industries, cities, and homes to new levels of efficiency, versatility, and cost effectiveness we face the potential for harm, death, and destructiveness.In such a marketplace, creating, exchanging, and integrating components that are trustworthy as well as entering into value-chain relationships with trustworthy partners and service suppliers will be common if we can provide a method for explicitly defining what is meant by the word trustworthy. The approach being pursued by these groups for applying Software Assurance to these systems and their Supply Chains by leveraging Structured Assurance Cases, Software Bill of Materials (the focus of this paper), and secure development practices applied to the evolving Agile and DevSecOps methodologies, is to explicitly identify the detailed requirements ``about what we need to know about something for it to be worthy of our trust'' and to do that in a way that we can convey that basis of trust to others that: can scale; is consistent within different workflows; is flexible to differing sets of hazards and environments; and is applicable to all sectors, domains, and industries.
Aitchison, Callum, Buckle, Roman, Ch'ng, Alvin, Clarke, Christian, Malley, Jacob, Halak, Basel.  2020.  On the Integration of Physically Unclonable Functions into ARM TrustZone Security Technology. 2020 European Conference on Circuit Theory and Design (ECCTD). :1–4.
As Internet of Things (IoT) devices are increasingly used in industry and become further integrated into our daily lives the security of such devices is of paramount concern. Ensuring that the large amount of information that these devices collect is protected and only accessible to authenticated users is a critical requirement of the industry. One potentially inexpensive way to improve device security utilises a Physically Unclonable Function (PUF) to generate a unique random response per device. This random response can be generated in such a way that it can be regenerated reliably and repeatably allowing the response to be considered a signature for each device. This signature could then be used for authentication or key generation purposes, improving trust in IoT devices. The advantage of a PUF based system is that the response does not need to be stored in nonvolatile memory as it is regenerated on demand, hardening the system against physical attacks. With SoC FPGAs being inexpensive and widely available there is potential for their use in both industrial and consumer applications as an additional layer of hardware security. In this paper we investigate and implement a Trusted Execution Environment (TEE) based around a PUF solely implemented in the FPGA fabric on a Xilinx Zynq-7000 SoC FPGA. The PUF response is used to seed a generic entropy maximisation function or Pseudorandom Number Generator (PRNG) with a system controller capable of encrypting data to be useful only to the device. This system interacts with a software platform running in the ARM TrustZone on the ARM Cortex core in the SoC, which handles requests between user programs and the FPGA. The proposed PUF-based security module can generate unique random keys able to pass all NIST tests and protects against physical attacks on buses and nonvolatile memories. These improvements are achieved at a cost of fewer than half the resources on the Zynq-7000 SoC FPGA.
Hedabou, Mustapha, Abdulsalam, Yunusa Simpa.  2020.  Efficient and Secure Implementation of BLS Multisignature Scheme on TPM. 2020 IEEE International Conference on Intelligence and Security Informatics (ISI). :1–6.
In many applications, software protection can not be sufficient to provide high security needed by some critical applications. A noteworthy example are the bitcoin wallets. Designed the most secure piece of software, their security can be compromised by a simple piece of malware infecting the device storing keys used for signing transactions. Secure hardware devices such as Trusted Platform Module (TPM) offers the ability to create a piece of code that can run unmolested by the rest of software applications hosted in the same machine. This has turned out to be a valuable approach for preventing several malware threats. Unfortunately, their restricted functionalities make them inconsistent with the use of multi and threshold signature mechanisms which are in the heart of real world cryptocurrency wallets implementation. This paper proposes an efficient multi-signature scheme that fits the requirement of the TPM. Based on discrete logarithm and pairings, our scheme does not require any interaction between signers and provide the same benefits as the well established BLS signature scheme. Furthermore, we proposed a formal model of our design and proved it security in a semi-honest model. Finally, we implemented a prototype of our design and studied its performance. From our experimental analysis, the proposed design is highly efficient and can serve as a groundwork for using TPM in future cryptocurrency wallets.
Qian, Dazan, Guo, Songhui, Sun, Lei, Liu, Haidong, Hao, Qianfang, Zhang, Jing.  2020.  Trusted Virtual Network Function Based on vTPM. 2020 7th International Conference on Information Science and Control Engineering (ICISCE). :1484–1488.
Mobile communication technology is developing rapidly, and this is integrated with technologies such as Software Defined Network (SDN), cloud computing, and Network Function Virtualization (NFV). Network Functions (NFs) are no longer deployed on dedicated hardware devices, while deployed in Virtual Machines (VMs) or containers as Virtual Network Functions (VNFs). If VNFs are tampered with or replaced, the communication system will not function properly. Our research is to enhance the security of VNFs using trusted computing technology. By adding Virtual Trusted Platform Module (vTPM) to the virtualization platform, the chain of trust extends from the VM operating system to VNFs within the VM. Experimental results prove that the solution can effectively protect the integrity of VNFs from being attacked.
Khalfaoui, Chaima, Ayed, Samiha, Esseghir, Moez.  2020.  A Stochastic Approach for an Enhanced Trust Management in a Decentralized Healthcare Environment. 2020 16th International Conference on Wireless and Mobile Computing, Networking and Communications (WiMob). :26–31.
Medical institutions are increasingly adopting IoT platforms to share data, communicate rapidly and improve healthcare treatment abilities. However, this trend is also raising the risk of potential data manipulation attacks. In decentralized networks, defense mechanisms against external entities have been widely enabled while protection against insider attackers is still the weakest link of the chain. Most of the platforms are based on the assumption that all the insider nodes are trustworthy. However, these nodes are exploiting of this assumption to lead manipulation attacks and violate data integrity and reliability without being detected. To address this problem, we propose a secure decentralized management system able to detect insider malicious nodes. Our proposal is based on a three layer architecture: storage layer, blockchain based network layer and IoT devices layer. In this paper, we mainly focus on the network layer where we propose to integrate a decentralized trust based authorization module. This latter allows updating dynamically the nodes access rights by observing and evaluating their behavior. To this aim, we combine probabilistic modelling and stochastic modelling to classify and predict the nodes behavior. Conducted performance evaluation and security analysis show that our proposition provides efficient detection of malicious nodes compared to other trust based management approaches.
Shang, Wenli, Zhang, Xiule, Chen, Xin, Liu, Xianda, Chen, Chunyu, Wang, Xiaopeng.  2020.  The Research and Application of Trusted Startup of Embedded TPM. 2020 39th Chinese Control Conference (CCC). :7669–7676.
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.
Ruchkin, Vladimir, Fulin, Vladimir, Romanchuk, Vitaly, Koryachko, Alexei, Ruchkina, Ekaterina.  2020.  Personal Trusted Platform Module for the Multi-Core System of 5G Security and Privacy. 2020 ELEKTRO. :1–4.
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.