Biblio

The new switching architecture of the neural networks was proposed. The switching neural networks consist of the neurons and the switchers. The goal is to reduce expenses on the artificial neural network design and training. For realization of complex models, algorithms and methods of management the neural networks of the big size are required. The number of the interconnection links “everyone with everyone” grows with the number of neurons. The training of big neural networks requires the resources of supercomputers. Time of training of neural networks also depends on the number of neurons in the network. Switching neural networks are divided into fragments connected by the switchers. Training of switcher neuron network is provided by fragments. On the basis of switching neural networks the devices of associative memory were designed with the number of neurons comparable to the human brain.

At present, our life is almost inseparable from the network, the network provides a lot of convenience for our life. However, a variety of network security incidents occur very frequently. In recent years, with the continuous development of neural network technology, more and more researchers have applied neural network to intrusion detection, which has developed into a new research direction in intrusion detection. As long as the neural network is provided with input data including network data packets, through the process of self-learning, the neural network can separate abnormal data features and effectively detect abnormal data. Therefore, the article innovatively proposes an intrusion detection method based on deep convolutional neural networks (CNN), which is used to test on public data sets. The results show that the model has a higher accuracy rate and a lower false negative rate than traditional intrusion detection methods.

False data injection (FDI) attacks could happen in decode-and-forward (DF) wireless cooperative relay networks. Although physical integrity check (PIC) can combat that by applying physical layer detection, the detector depends on the decoding results and low signal-to-noise ratio (SNR) further deteriorates the detecting results. In this paper, a physical layer detect-before-decode (DbD) method is proposed, which has low computational complexity with no sacrifice of false alarm and miss detection rates. One significant advantage of this method is the detector does not depend on the decoding results. In order to implement the proposed DbD method, a unified error sufficient statistic (UESS) containing the full information of FDI attacks is constructed. The proposed UESS simplifies the detector because it is applicable to all link conditions, which means there is no need to deal each link condition with a specialized sufficient statistic. Moreover, the source to destination outage probability (S2Dop) of the DF cooperative relay network utilizing the proposed DbD method is studied. Finally, numerical simulations verify the good performance of this DbD method.

With the development of 5G communication technology, the status of the Internet of Vehicles in people's lives is greatly improved in the general trend of intelligent transportation. The combination of vehicles and Radio Frequency Identification (RFID) makes the application prospects of vehicle networking gradually expand. However, the wireless network of the Internet of Vehicles is open and mobile, so it can be easily stolen or tampered with by attackers. Moreover, it will cause serious traffic security problems and even threat people's lives. In this paper, we propose a lightweight authentication protocol for the Internet of Vehicles based on a mobile RFID system and give corresponding security requirements for modeling potential attacks. The protocol is based on the three-party mutual authentication, and uses bit-operated left-cycle shift operations and hetero-oriented operations to generate encrypted data. The simultaneous inclusion of triparty shared key information and random numbers makes the protocol resistant to counterfeit attacks, violent attacks, replay attacks and desynchronization attacks. Finally, a simulation analysis of the security protocol using the ProVerif tool shows that the protocol secures is not accessible to attackers during the data transfer, and achieve the three-party authentication between sensor nodes (SN), vehicle nodes (Veh) and backend servers.

A rapid rise in cyber-attacks on Cyber Physical Systems (CPS) has been observed in the last decade. It becomes even more concerning that several of these attacks were on critical infrastructures that indeed succeeded and resulted into significant physical and financial damages. Experimental testbeds capable of providing flexible, scalable and interoperable platform for executing various cybersecurity experiments is highly in need by all stakeholders. A container-based SCADA testbed is presented in this work as a potential platform for executing cybersecurity experiments. Through this testbed, a network traffic containing ARP spoofing is generated that represents a Man in the middle (MITM) attack. While doing so, scanning of different systems within the network is performed which represents a reconnaissance attack. The network traffic generated by both ARP spoofing and network scanning are captured and further used for preparing a dataset. The dataset is utilized for training a network classification model through a machine learning algorithm. Performance of the trained model is evaluated through a series of tests where promising results are obtained.

With the popularity of Internet applications and rapid development, data using and sharing process may lead to the sensitive information divulgence. To deal with the privacy protection issue more effectively, in this paper, we propose the associated data sets protection model based on game theory from the point of view of realizing benefits from the access of privacy is about happen, quantify the extent to which visitors gain sensitive information, then compares the tolerance of the sensitive information owner and finally decides whether to allow the visitor to make an access request.

TEE(Trusted Execution Environment) has became one of the most popular security features for mobile platforms. Current TEE solutions usually implement secure functions in Trusted applications (TA) running over a trusted OS in the secure world. Host App may access these secure functions through the TEE driver. Unfortunately, such architecture is not very secure. A trusted OS has to be loaded in secure world to support TA running. Thus, the code size in secure world became large. As more and more TA is installed, the secure code size will be further larger and larger. Lots of real attack case have been reported [1]. In this paper, we present a novel TEE constructing method named ALTEE. Different from existing TEE solutions, ALTEE includes secure code in host app, and constructs a trustworthy execution environment for it dynamically whenever the code needs to be run.

Internet of Things devices have spread to near ubiquity this decade. All around us now lies an invisible mesh of communication from devices embedded in seemingly everything. Inevitably some of that communication flying around our heads will contain data that must be protected or otherwise shielded from tampering. The responsibility to protect this sensitive information from malicious actors as it travels through the air then falls upon the standards used to communicate this data. Bluetooth Low Energy (BLE) is one of these standards, the aim of this paper is to put its security standards to test. By attempting to exploit its vulnerabilities we can see how secure this standard really is. In this paper, we present steps for analyzing the security of BLE devices using open-source hardware and software.

Occurrence of faults in power systems is unavoidable but their timely recognition and location enhances the reliability and security of supply; thereby resulting in economic gain to consumers and power utility alike. Distribution Network (DN) is made smarter by the introduction of sensors and computers into the system. In this paper, detection and classification of faults in DN using Artificial Neural Network (ANN) is emphasized. This is achieved through the employment of Back Propagation Algorithm (BPA) of the Feed Forward Neural Network (FFNN) using three phase voltages and currents as inputs. The simulations were carried out using the MATLAB® 2017a. ANN with various hidden layers were analyzed and the results authenticate the effectiveness of the method.

With the transformation of connected vehicles into the Internet of Vehicles (IoV), the time is now ripe for paving the way for the next generation of connected vehicles with novel applications and innovative security measures. The connected vehicles are experiencing prenominal growth in the auto industry, but are still studded with many security and privacy vulnerabilities. Today's IoV applications are part of cyber physical communication systems that collect useful information from thousands of smart sensors associated with the connected vehicles. The technology advancement has paved the way for connected vehicles to share significant information among drivers, auto manufacturers, auto insurance companies and operational and maintenance service providers for various applications. The critical issues in engineering the IoV applications are effective to use of the available spectrum and effective allocation of good channels an opportunistic manner to establish connectivity among vehicles, and the effective utilization of the infrastructure under various traffic conditions. Security and privacy in information sharing are the main concerns in a connected vehicle communication network. Blockchain technology facilitates secured communication among users in a connected vehicles network. Originally, blockchain technology was developed and employed with the cryptocurrency. Bitcoin, to provide increased trust, reliability, and security among users based on peer-to-peer networks for transaction sharing. In this paper, we propose to integrate blockchain technology into ad hoc vehicular networking so that the vehicles can share network resources with increased trust, reliability, and security using distributed access control system and can benefit a wider scope of scalable IoV applications scenarios for decision making. The proposed architecture is the faithful environment for information sharing among connected vehicles. Blockchain technology allows multiple copies of data storage at the distribution cloud. Distributed access control system is significantly more secure than a traditional centralized system. This paper also describes how important of ad hoc vehicular networking in human life, possibilities in real-world implementation and its future trends. The ad hoc vehicular networking may become one of the most trendy networking concepts in the future that has the perspective to bring out much ease human beneficial and secured applications.

In recent years, the application of Bluetooth has always been the highly debated topic among the researches. Through the Bluetooth protocol, Bluetooth can implement the data switching in short distance between various devices. Nevertheless, BlueBorne Attack makes the seemingly stable Bluetooth protocols full of vulnerabilities. Our research will concentrate on predicting the BlueBorne Attack with the following directions: the working mechanism, the working methods and effective range of BlueBorne. Based on the comprehensive review of recent peer-reviewed researches, this project provides a new model based on application layer to solve the security problem of BlueBorne. The paper asserts that compared with the previous research, the unique model has better consequence with highly stability.

IoT devices introduce unprecedented threats into home and professional networks. As they fail to adhere to security best practices, they are broadly exploited by malicious actors to build botnets or steal sensitive information. Their adoption challenges established security standard as classic security measures are often inappropriate to secure them. This is even more problematic in sensitive environments where the presence of insecure IoTs can be exploited to bypass strict security policies. In this paper, we demonstrate an attack against a highly secured network using a Bluetooth smart bulb. This attack allows a malicious actor to take advantage of a smart bulb to exfiltrate data from an air gapped network.

In signed directed graph (SDG) fault diagnosis model, only single fault can be diagnosed. In order to meet the requirements of multiple faults diagnosis, in this paper, improved signed directed graph (ISDG) fault diagnosis model was proposed. The logic and influence between nodes were included in ISDG model. With ISDG model, complex logic can be shown, multiple faults can be diagnosed and the optimal sequence can be determined. Two algorithms are proposed in this paper. One algorithm can obtain the multiple faults combine logic, and the other algorithm can obtain the optimal path of fault diagnosis. According to these two algorithms, the efficiency was improved and the cost was reduced in the multiple fault diagnosis process. Finally, the faults of an aircraft engine bleed system were diagnosed with the interactive algorithm. The proposed algorithms can obtain a diagnosis result effectively. The results of two cases prove that these algorithms can be used for multiple fault diagnosis.

Internet of Things (IoT) is becoming increasingly important to intelligent transportation system stakeholders, including cloud-based vehicular cloud (VC) and internet of vehicles (IoV) paradigms. This new trend involves communication and data exchange between several objects within different layers of control. Security in such a deployment is pivotal to realize the general IoT-based smart city. However, the evaluation of the degree of security regarding these paradigms remains a challenge. This study aims to discover and identify common security criteria (CSC) from a context-based analysis pattern and later to discuss, compare, and aggregate a conceptual model of CSC impartially. A privacy granularity classification that maintains data confidentiality is proposed alongside the security selection criteria.

Internet of Vehicles are the important part of Intelligence Traffic Systems (ITS), which are essential for the national security and economy development. The impact assessment for cyberattacks in the IoV protection is of great theoretical and practical significance. Most of the researchers in this field pay attention on the attack impact on a vehicle, and the seldom investigate the impact on the whole system which combines all the vehicles as a whole integration. To tackle this problem, a cyber-physical interaction model based impact assessment of cyberattacks is presented. In this approach, the operation of IoV is modeled from the cyberphysical interaction perspective, and then the propagating process from cyber layer to physical layer is investigated. Based on above model, the impact assessment of cyberattacks on IoV is realized quantitatively. Finally, a simulation on an IoV is conducted to verify the effectiveness of this approach.

This paper presents a data-driven and physics-based method for detection of false data injection (FDI) in Smart Grids (SG). As the power grid transitions to the use of SG technology, it becomes more vulnerable to cyber-attacks like FDI. Current strategies for the detection of bad data in the grid rely on the physics based State Estimation (SE) process and statistical tests. This strategy is naturally vulnerable to undetected bad data as well as false positive scenarios, which means it can be exploited by an intelligent FDI attack. In order to enhance the robustness of bad data detection, the paper proposes the use of data-driven Machine Intelligence (MI) working together with current bad data detection via a combined Chi-squared test. Since MI learns over time and uses past data, it provides a different perspective on the data than the SE, which analyzes only the current data and relies on the physics based model of the system. This combined bad data detection strategy is tested on the IEEE 118 bus system.

The evolution of the Internet of Vehicles (IoV) paradigm has recently attracted a lot of researchers and industries. Vehicular Ad Hoc Networks (VANET) is the networking model that lies at the heart of this technology. It enables the vehicles to exchange relevant information concerning road conditions and safety. However, ensuring communication security has been and still is one of the main challenges to vehicles' interconnection. To secure the interconnected vehicular system, many cryptography techniques, communication protocols, and certification and reputation-based security approaches were proposed. Nonetheless, some limitations are still present, preventing the practical implementation of such approaches. In this paper, we first define a set of locally-perceived behavioral reputation parameters that enable a distributed evaluation of vehicles' reputation. Then, we integrate these parameters into the design of a reputation management system to exclude malicious or faulty vehicles from the IoV network. Our system can help in the prevention of several attacks on the VANET environment such as Sybil and Denial of Service attacks, and can be implemented in a fully decentralized fashion.

Cyber-event-triggered power grid blackout compels utility operators to intensify cyber-aware and physics-constrained recovery and restoration process. Recently, coordinated cyber attacks on the Ukrainian grid witnessed such a cyber-event-triggered power system blackout. Various cyber-physical system (CPS) testbeds have attempted with multitude designs to analyze such interdependent events and evaluate remedy measures. However, resource constraints and modular integration designs have been significant barriers while modeling large-scale grid models (scalability) and multi-grid isolated models (concurrency) under a single real-time execution environment for the hardware-in-the-loop (HIL) CPS security testbeds. This paper proposes a meticulous design and effective modeling for simulating large-scale grid models and multi-grid isolated models in a HIL realtime digital simulator environment integrated with industry-grade hardware and software systems. We have used our existing HIL CPS security testbed to demonstrate scalability by the realtime performance of a Texas-2000 bus US synthetic grid model and concurrency by the real-time performance of simultaneous ten IEEE-39 bus grid models and an IEEE-118 bus grid model. The experiments demonstrated significant results by 100% realtime performance with zero overruns, low latency while receiving and executing control signals from SEL Relays via IEC-61850 protocol and low latency while computing and transmitting grid data streams including stability measures via IEEE C37.118 synchrophasor data protocol to SEL Phasor Data Concentrators.

To increase security and to offer user experiences according to the requirements of a hyper-connected world, modern vehicles are integrating complex electronic systems, being transformed into systems of Cyber-Physical Systems (CPS). While a great diversity of heterogeneous hardware and software components must work together and control in real-time crucial functionalities, cybersecurity for the automotive sector is still in its infancy. This paper provides an analysis of the most common vulnerabilities and risks of connected vehicles, using a real example based on industrial and market-ready technologies. Several components have been implemented to inject and simulate multiple attacks, which enable security services and mitigation actions to be developed and validated.

Intelligent software applications are becoming ubiquitous and pervasive affecting various aspects of our lives and livelihoods. At the same time, the risks to which these systems expose the organizations and end users are growing dramatically. Trustworthiness of software applications is becoming a paramount necessity. Trust is to be regarded as a first-class citizen in the total product life cycle and should be addressed across all stages of software development. Trust can be looked at from two facets: one at an algorithmic level (e.g., bias-free, discrimination-aware, explainable and interpretable techniques) and the other at a process level by making development processes more transparent, auditable, and adhering to regulations and best practices. In this paper, we address the latter and propose a blockchain enabled governance framework for building trustworthy software. Our framework supports the recording, monitoring, and analysis of various activities throughout the application development life cycle thereby bringing in transparency and auditability. It facilitates the specification of regulations and best practices and verifies for its adherence raising alerts of non-compliance and prescribes remedial measures.

Advancement in communication technologies and the Internet of Things (IoT) is driving adoption in smart cities that aims to increase operational efficiency and improve the quality of services and citizen welfare, among other potential benefits. The privacy, reliability, and integrity of communications must be ensured so that actions can be appropriate, safe, accurate, and implemented promptly after receiving actionable information. In this work, we present a multi-tier methodology consisting of an authentication and trust-building/distribution framework designed to ensure the safety and validity of the information exchanged in the system. Blockchain protocols and Radio Frequency-Distinct Native Attributes (RF-DNA) combine to provide a hardware-software codesigned system for enhanced device identity and overall system trustworthiness. Our threat model accounts for counterfeiting, breakout fraud, and bad mouthing of one entity by others. Entity trust (e.g., IoT devices) depends on quality and level of participation, quality of messages, lifetime of a given entity in the system, and the number of known "bad" (non-consensus) messages sent by that entity. Based on this approach to trust, we are able to adjust trust upward and downward as a function of real-time and past behavior, providing other participants with a trust value upon which to judge information from and interactions with the given entity. This approach thereby reduces the potential for manipulation of an IoT system by a bad or byzantine actor.

Based on the Indonesian of Statistics the level of society people in 2019 is grow up. Based on data, the bank conducted a community to simple transaction payment in the market. Bank just used a debit card or credit card for the transaction, but the banks need more investment for infrastructure and very expensive. Based on that cause the bank needs another solution for low-cost infrastructure. Obtained from solutions that, the bank implementation QR Code Biometric authentication Payment Online is one solution that fulfills. This application used for payment in online merchant. The transaction permits in this study lie in the biometric encryption, or decryption transaction permission and QR Code Scan to improve communication security and transaction data. The test results of implementation Biometric Cloud Authentication Platform show that AES 256 agents can be implemented for face biometric encryption and decryption. Code Scan QR to carry out transaction permits with Face verification transaction permits gets the accuracy rate of 95% for 10 sample people and transaction process gets time speed of 53.21 seconds per transaction with a transaction sample of 100 times.

This paper deals with electromagnetic vibration excitation of rotating machine blades in a purpose of dynamic measurement and testing. A novel method for increasing the excitation force is presented. At the same time, the suggested method allows to reduce blade remanent induction. Examples of measurements are shown, and obtained results are discussed.

With the rapid deployment of Cyber-Physical Systems (CPS), security has become a more critical problem than ever before, as such devices are interconnected and have access to a broad range of critical data. A well-known attack is ReturnOriented Programming (ROP) which can diverge the control flow of a program by exploiting the buffer overflow vulnerability. To protect a program from ROP attacks, a useful method is to instrument code into the protected program to do runtime control flow checking (known as Control Flow Integrity, CFI). However, instrumented code brings extra execution time, which has to be properly handled, as most CPS systems need to behave in a real-time manner. In this paper, we present a technique to efficiently compute an execution plan, which maximizes the number of executions of instrumented code to achieve maximal defense effect, and at the same time guarantees real-time schedulability of the protected task system with a new response time analysis. Simulation-based experimental results show that the proposed method can yield good quality execution plans, but performs orders of magnitude faster than exhaustive search. We also built a prototype in which a small auto-drive car is defended against ROP attacks by the proposed method implemented in FreeRTOS. The prototype demonstrates the effectiveness of our method in real-life scenarios.

{In the last few decades, Internet of things (IOT) is one of the key elements in industrial revolution 4.0 that used mart phones as one of the best technological advances' intelligent device. It allows us to have power over devices without people intervention, either remote or voice control. Therefore, the “Smart Radar Door “system uses a microcontroller and mobile Bluetooth module as an automation of smart door lock system. It is describing the improvement of a security system integrated with an Android mobile phone that uses Bluetooth as a wireless connection protocol and processing software as a tool in order to detect any object near to the door. The mob ile device is required a password as authentication method by using microcontroller to control lock and unlock door remotely. The Bluetooth protocol was chosen as a method of communication between microcontroller and mobile devices which integrated with many Android devices in secured protocol}.