Biblio

A “tripartite and bilateral” dynamic game model was constructed to study the impact of space deterrence on the challenger's military strategy in a military conflict. Based on the signal game theory, the payment matrices and optimal strategies of the sheltering side and challenging side were analyzed. In a theoretical framework, the indicators of the effectiveness of the challenger's response to space deterrence and the influencing factors of the sheltering's space deterrence were examined. The feasibility and effective means for the challenger to respond to the space deterrent in a “tripartite and bilateral” military conflict were concluded.

The security control problem of cyber-physical system (CPS) under actuator attacks is studied in the paper. Considering the strict-feedback cyber-physical systems with external disturbance, a security control scheme is proposed by combining backstepping method and super-twisting sliding mode technology when the transmission control input signal of network layer is under false data injection(FDI) attack. Firstly, the unknown nonlinear function of the CPS is identified by Radial Basis Function Neural Network. Secondly, the backstepping method and super-twisting sliding mode algorithm are combined to eliminate the influence of actuator attack and ensure the robustness of the control system. Then, by Lyapunov stability theory, it is proved that the proposed control scheme can ensure that all signals in the closed-loop system are semi-global and ultimately uniformly bounded. Finally, the effectiveness of the proposed control scheme is verified by the inverted pendulum simulation.

The latest, modern security camera systems record numerous data at once. With the utilization of artificial intelligence, these systems can even compose an online attendance register of students present during the lectures. Data is primarily recorded on the hard disk of the NVR (Network Video Recorder), and in the long term, it is recommended to save the data in the blockchain. The purpose of the research is to demonstrate how university security cameras can be securely connected to the blockchain. This would be important for universities as this is sensitive student data that needs to be protected from unauthorized access. In my research, as part of the practical implementation, I therefore also use encryption methods and data fragmentation, which are saved at the nodes of the blockchain. Thus, even a DDoS (Distributed Denial of Service) type attack may be easily repelled, as data is not concentrated on a single, central server. To further increase security, it is useful to constitute a blockchain capable of its own data storage at the faculty itself, rather than renting data storage space, so we, ourselves may regulate the conditions of operation, and the policy of data protection. As a practical part of my research, therefore, I created a blockchain called UEDSC (Universities Data Storage Chain) where I saved the student's data.

With the rapid development of information science and technology, the role of the Internet in daily life is becoming more and more important, but while bringing speed and convenience to the experience, network security issues are endless, and fighting cybercrime will be an eternal topic. In recent years, new types of cyberattacks have made defense and analysis difficult. For example, the memory of network attacks makes some key array evidence only temporarily exist in physical memory, which puts forward higher requirements for attack detection. The traditional memory forensic analysis method for persistent data is no longer suitable for a new type of network attack analysis. The continuous development of memory forensics gives people hope. This paper proposes a network attack detection model based on memory forensic analysis to detect whether the system is under attack. Through experimental analysis, this model can effectively detect network attacks with low overhead and easy deployment, providing a new idea for network attack detection.

This paper examines audio-based social networking platforms and how their environments can affect the persistence of fake news and mis/disinformation in the whole information ecosystem. This is performed through an exploration of their features and how they compare to that of general-purpose multimodal platforms. A case study on Spotify and its recent issue on free speech and misinformation is the application area of this paper. As a supplementary, a demographic analysis of the current statistics of podcast streamers is outlined to give an overview of the target audience of possible deception attacks in the future. As for the conclusion, this paper confers a recommendation to policymakers and experts in preparing for future mis-affordance of the features in social environments that may unintentionally give the agents of mis/disinformation prowess to create and sow discord and deception.

This paper offers a comparative vector assessment of DDoS and disinformation attacks. The assessed dimensions are as follows: (1) the threat agent, (2) attack vector, (3) target, (4) impact, and (5) defense. The results revealed that disinformation attacks, anchoring on astroturfs, resemble DDoS’s zombie computers in their method of amplification. Although DDoS affects several layers of the OSI model, disinformation attacks exclusively affect the application layer. Furthermore, even though their payloads and objectives are different, their vector paths and network designs are very similar. This paper, as its conclusion, strongly recommends the classification of disinformation as an actual cybersecurity threat to eliminate the inconsistencies in policies in social networking platforms. The intended target audiences of this paper are IT and cybersecurity experts, computer and information scientists, policymakers, legal and judicial scholars, and other professionals seeking references on this matter.

With the development of Industrial Internet identification analysis, various encryption methods have been widely used in identification analysis to ensure the security of identification encoding and data. However, the past encryption methods failed to consider the problem of encryption efficiency in the case of high concurrency, so it will reduce the identification resolution efficiency and increase the computational pressure of secondary nodes when applying these methods to the identification analysis. In this paper, in order to improve the efficiency of identification analysis under the premise of ensuring information security, a safe and efficient analytical encryption method for industrial Internet identification based on Secure Hash Algorithm 256 (SHA-256), and Rivest-Shamir-Adleman (RSA) is presented. Firstly, by replacing the secret key in the identification encoding encryption with the SHA-256 function, the number of secret keys is reduced, which is beneficial to improve the efficiency of identification analysis. Secondly, by replacing the large prime number of the RSA encryption algorithm with multiple small prime numbers, the generation speed of RSA key pair is improved, which is conducive to reduce the computation of secondary nodes. Finally, by assigning a unique RSA private key to the identification code during the identification registration phase, SHA-256 and RSA are associated, the number of key exchanges is reduced during the encryption process, which is conducive to improve the security of encryption. The experiment verifies that the proposed method can improve security of encryption and efficiency of identification analysis, by comparing the complexity of ciphertext cracking and the identification security analysis time between the traditional encryption method and this method.

Aiming at the specificity and complexity of the power IoT terminal, a method of power IoT terminal firmware vulnerability detection based on memory fuzzing is proposed. Use the method of bypassing the execution to simulate and run the firmware program, dynamically monitor and control the execution of the firmware program, realize the memory fuzzing test of the firmware program, design an automatic vulnerability exploitability judgment plug-in for rules and procedures, and provide power on this basis The method and specific process of the firmware vulnerability detection of the IoT terminal. The effectiveness of the method is verified by an example.

The power industrial control system is an important part of the national critical Information infrastructure. Its security is related to the national strategic security and has become an important target of cyber attacks. In order to solve the problem that the vulnerability detection technology of power industrial control system cannot meet the requirement of non-destructive, this paper proposes an industrial control vulnerability analysis technology combined with dynamic and static analysis technology. On this basis, an industrial control non-destructive vulnerability detection system is designed, and a simulation verification platform is built to verify the effectiveness of the industrial control non-destructive vulnerability detection system. These provide technical support for the safety protection research of the power industrial control system.

Under the situation of regular epidemic prevention and control, teleworking has gradually become a normal working mode. With the development of modern information technologies such as big data, cloud computing and mobile Internet, it's become a problem that how to build an effective security defense system to ensure the information security of teleworking in complex network environment while ensuring the availability, collaboration and efficiency of teleworking. One of the solutions is Zero Trust Network(ZTN), most enterprise infrastructures will operate in a hybrid zero trust/perimeter-based mode while continuing to invest in IT modernization initiatives and improve organization business processes. In this paper, we have systematically studied the zero trust principles, the logical components of zero trust architecture and the key technology of zero trust network. Based on the abstract model of zero trust architecture and information security technologies, a prototype has been realized which suitable for iOS terminals to access enterprise resources safely in teleworking mode.

Cyber Physical Systems (CPS) are widely deployed and employed in many recent real applications such as automobiles with sensing technology for crashes to protect passengers, automated homes with various smart appliances and control units, and medical instruments with sensing capability of glucose levels in blood to keep track of normal body function. In spite of their significance, CPS infrastructures are vulnerable to cyberattacks due to the limitations in the computing, processing, memory, power, and transmission capabilities for their endpoint/edge appliances. In this paper, we consider a short systematic investigation for the models and techniques of cyberattacks and threats rate against Cyber Physical Systems with multiple subsystems and redundant elements such as, network of computing devices or storage modules. The cyberattacks are assumed to be externally launched against the Cyber Physical System during a prescribed operational time unit following stochastic distribution models such as Poisson probability distribution, negative-binomial probability distribution and other that have been extensively employed in the literature and proved their efficiency in modeling system attacks and threats.

In the Internet of Things, it is more important than ever to effectively address the problem of secure transmission based on steganographic substitution by synthesizing digital sensor data. In this case, the degree to which the grayscale message is obscured is a necessary issue. To ensure information security in IoT systems, various methods are used and information security problems are solved to one degree or another. The article proposes a method and algorithm for a computer image in grayscale, in which the value of each pixel is one sample, representing the amount of light, carrying only information about the intensity. The proposed method in grayscale using steganographic coding provides a secure implementation of data transmission in the IoT system. Study results were analyzed using PSNR (Peak Signal to Noise Ratio).

We consider that in order to improve the utilization rate of spectrum resources and the security rate of unmanned aerial vehicle (UAV) Communication system, a secure transmission scheme of UAV relay assisted cognitive radio network (CRN) is proposed. In the presence of primary users and eavesdroppers, the UAV acts as the decoding and forwarding mobile relay to assist the secure transmission from the source node to the legitimate destination node. This paper optimizes the flight trajectory and transmission power of the UAV relay to maximize the security rate. Since the design problem is nonconvex, the original problem is approximated to a convex constraint by constructing a surrogate function with nonconvex constraints, and an iterative algorithm based on continuous convex approximation is used to solve the problem. The simulation results show that the algorithm can effectively improve the average security rate of the secondary system and successfully optimize the UAV trajectory.

Completely Automated Public Turing Tests (CAPTCHA) have been used to differentiate between computers and humans for quite some time now. There are many different varieties of CAPTCHAs - text-based, image-based, audio, video, arithmetic, etc. However, not all varieties are suitable for the visually impaired. As time goes by and Spambots and APIs grow more accurate, the CAPTCHA tests have been constantly updated to stay relevant, but that has not happened with the audio CAPTCHA. There exists an audio CAPTCHA intended for the blind/visually impaired but many blind/visually impaired find it difficult to solve. We propose an alternative to the existing system, which would make use of unique sound samples layered with music generated through GANs (Generative Adversarial Networks) along with noise and other layers of sounds to make it difficult to dissect. The user has to count the number of times the unique sound was heard in the sample and then input that number. Since there are no letters or numbers involved in the samples, speech-to-text bots/APIs cannot be used directly to decipher this system. Also, any user regardless of their native language can comfortably use this system.

Affected by the inherent ideas like "Focus on Function Realization, Despise Security Protection", there are lots of hidden threats in the industrial control system of wind farms (ICS-WF), such as unreasonable IP configuration, failure in virus detection and killing, which are prone to illegal invasion and attack from the cyberspace. Those unexpected unauthorized accesses are quite harmful for the stable operation of the wind farms and regional power grid. Therefore, by investigating the current security situation and needs of ICS-WF, analyzing the characteristics of ICS-WF’s architecture and internal communication, and integrating the ideas of the classified protection of cybersecurity, this paper proposes a new customized cybersecurity strategy for ICS-WF based on the barrel theory. We also introduce an new anomalous intrusion detection technology for ICS-WF, which is developed based on statistical models of wind farm network characteristics. Finally, combined all these work with the network security offense and defense drill in the industrial control safety simulation laboratory of wind farms, this research formulates a three-dimensional comprehensive protection solution for ICS-WF, which significantly improves the cybersecurity level of ICS-WF.

In order to solve the problem of quantitative assessment of information security risks in industrial control systems, this paper proposes a method of information security risk assessment for industrial control systems based on modular hybrid genetic algorithm. Combining with the characteristics of industrial control systems, the use of hybrid genetic algorithm evidence theory to identify, evaluate and assess assets and threats, and ultimately come to the order of the size of the impact of security threats on the specific industrial control system information security. This method can provide basis for making decisions to reduce information security risks in the control system from qualitative and quantitative aspects.

This paper summarizes the types and contents of enterprise big data information, analyzes the demand and characteristics of enterprise shared data information based on the Internet of things, and analyzes the current situation of enterprise big data fusion at home and abroad. Firstly, using the idea of the Internet of things for reference, the intelligent sensor is used as the key component of data acquisition, and the multi energy data acquisition technology is discussed. Then the data information of entity enterprises is taken as the research object and a low energy consumption transmission method based on data fusion mechanism for industrial ubiquitous Internet of things is proposed. Finally, a network monitoring and data fusion platform for the industrial Internet of things is implemented. The monitoring node networking and platform usability test are also performed. It is proved that the scheme can achieve multi parameter, real-time, high reliable network intelligent management.

Current power sensitive data security protection adopts classification and grading protection. Company classification and grading are mainly in formulating specifications. Data classification and grading processing is carried out manually, which is heavy and time-consuming, while traditional data identification mainly relies on rules for data identification, the level of automation and intelligence is low, and there are many problems in recognition accuracy. Data classification and classification is the basis of data security protection. Sensitive data identification is the key to data classification and classification, and it is also the first step to achieve accurate data security protection. This paper proposes an intelligent identification and tracking technology of sensitive data for electric power big data, which can improve the ability of data classification and classification, help the realization of data classification and classification, and provide support for the accurate implementation of data security capabilities.

This paper reviews the video watermarking schemes resistance against tampering attacks. There are several transform methods which are used for Video Watermarking including Discrete Fourier Transform (DFT), Discrete Cosine Transform (DCT), Discrete wavelet transform (DWT) and are discussed and compared in this paper. The results are presented in a table with a summary of their advantages.

With the evolution of the communication technology, fast and efficient tools for secure exchanged data are highly required. Through this research work, we introduce a simplified and fast chaos-based scheme for multimedia data encryption and in particular for color image encryption application. The new algorithm is based on an extracted four-dimension (4-D) discrete time map. The proposed 4-D chaos system includes seven (07) nonlinear terms and four (04) controllers to generate a robust chaos that can satisfy the encryption requirements. The performance of this image encryption algorithm are analyzed with the help of four important factors which are key space, correlation, complexity and running time. Results of the security analysis compared to some of similar proposals, show that our encryption scheme is more effective in terms of key stream cipher space, correlation, complexity and running time.

Digital signatures are increasingly used today. It replaces wet signature with the development of technology. Elliptic curve digital signature algorithm (ECDSA) is used in many applications thanks to its security and efficiency. However, some mathematical operations such as inversion operation in modulation slow down the speed of this algorithm. In this study, we propose a more efficient and secure ECDSA. In the proposed method, the inversion operation in modulation of signature generation and signature verification phases is removed. Thus, the efficiency and speed of the ECDSA have been increased without reducing its security. The proposed method is implemented in Python programming language using P-521 elliptic curve and SHA-512 algorithm.

Noise has been used as a way of protecting privacy of users in public datasets for many decades now. Differential privacy is a new standard to add noise, so that user privacy is protected. When this technique is applied for a single end user data, it's called local differential privacy. In this study, we evaluate the effects of adding noise to generate randomized responses on machine learning models. We generate randomized responses using Gaussian, Laplacian noise on singular end user data as well as correlated end user data. Finally, we provide results that we have observed on a few data sets for various machine learning use cases.

Aiming at the problem of low detection accuracy in traditional power system network intrusion detection methods, in order to improve the performance of power system network intrusion detection, a power system network intrusion detection method based on random forest algorithm is proposed. Firstly, the power system network intrusion sub sample is selected to construct the random forest decision tree. The random forest model is optimized by using the edge function. The accuracy of the vector is judged by the minimum state vector of the power system network, and the measurement residual of the power system network attack is calculated. Finally, the power system network intrusion data set is clustered by Gaussian mixture clustering Through the design of power system network intrusion detection process, the power system network intrusion detection is realized. The experimental results show that the power system network intrusion detection method based on random forest algorithm has high network intrusion detection performance.

Since the personal data protection law is on the way of many countries, how to use data mining method to secure sensitive information has become a challenge for enterprises. To make sure every employee follows company's data protection strategy, it may take lots of time and cost to seek and scan thousands of folders and files in user equipment, ensuring that the file contents meet IT security policies. Hence, this paper proposed a lightweight, pattern-based detection system, PIDS, which is expecting to enable an affordable data leakage prevention with essential cost and high efficiency in small business enterprise. For verification and evaluation, PIDS has been deployed on more than 100,000 PCs of collaboration enterprises, and the feedback shows that the system is able to approach its original design functionality for finding violated or sensitive contents efficiently.

With the tremendous growth of IoT application, providing security to IoT systems has become more critical. In this paper, a technique is presented to ensure the safety of Internet of Things (IoT) devices. This technique ensures hardware and software security of IoT devices. Blockchain technology is used for software security and hardware logics are used for hardware security. For enabling a Blockchain, Ethereum Network is used for secure peer-to-peer transmission. A prototype model is also used using two IoT nodes to demonstrate the security logic.