The growing amount of data and advances in data science have created a need for a new kind of cloud platform that provides users with flexibility, strong security, and the ability to couple with supercomputers and edge devices through high-performance networks. We have built such a nation-wide cloud platform, called "mdx" to meet this need. The mdx platform's virtualization service, jointly operated by 9 national universities and 2 national research institutes in Japan, launched in 2021, and more features are in development. Currently mdx is used by researchers in a wide variety of domains, including materials informatics, geo-spatial information science, life science, astronomical science, economics, social science, and computer science. This paper provides an overview of the mdx platform, details the motivation for its development, reports its current status, and outlines its future plans.

Forming a secure autonomous vehicle group is extremely challenging since we have to consider threats and vulnerability of autonomous vehicles. Existing studies focus on communications among risk-free autonomous vehicles, which lack metrics to measure passenger security and cargo values. This work proposes a novel autonomous vehicle group formation method. We introduce risk assessment scoring to assess passenger security and cargo values, and propose an autonomous vehicle group formation method based on it. Our vehicle group is composed of a master node, and a number of core and border ones. Finally, the extensive simulation results show that our method is better than a Connectivity Prediction-based Dynamic Clustering model and a Low-InDependently clustering architecture in terms of node survival time, average change count of master nodes, and average risk assessment scoring.

The vertiginous technological advance related to globalization and the new digital era has led to the design of new techniques and tools that deal with the risks of technology and information. Terms such as "cybersecurity" stand out, which corresponds to that area of computer science that is responsible for the development and implementation of information protection mechanisms and technological infrastructure, in order to deal with cyberattacks. Phishing is a crime that uses social engineering and technical subterfuge to steal personal identity data and financial account credentials from users, representing a high economic and financial risk worldwide, both for individuals and for large organizations. The objective of this research is to determine the ways to prevent phishing, by analyzing the characteristics of this computer fraud, the various existing modalities and the main prevention strategies, in order to increase the knowledge of users about this. subject, highlighting the importance of adequate training that allows establishing efficient mechanisms to detect and block phishing.

Memory-based vulnerabilities are becoming more and more common in low-power and low-cost devices in IOT. We study several low-level vulnerabilities that lead to memory corruption in C and C++ programs, and how to use stack corruption and format string attack to exploit these vulnerabilities. Automatic methods for resisting memory attacks, such as stack canary and address space layout randomization ASLR, are studied. These methods do not need to change the source program. However, a return-oriented programming (ROP) technology can bypass them. Control flow integrity (CFI) can resist the destruction of ROP technology. In fact, the security design is holistic. Finally, we summarize the rules of security coding in embedded devices, and propose two novel methods of software anomaly detection process for IOT devices in the future.

The development of autonomous agents have gained renewed interest, largely due to the recent successes of machine learning. Social robots can be considered a special class of autonomous agents that are often intended to be integrated into sensitive environments. We present experiences from our work with two specific humanoid social service robots, and highlight how eschewing privacy and security by design principles leads to implementations with serious privacy and security flaws. The paper introduces the robots as platforms and their associated features, ecosystems and cloud platforms that are required for certain use cases or tasks. The paper encourages design aims for privacy and security, and then in this light studies the implementation from two different manufacturers. The results show a worrisome lack of design focus in handling privacy and security. The paper aims not to cover all the security flaws and possible mitigations, but does look closer into the use of the WebSocket protocol and it’s challenges when used for operational control. The conclusions of the paper provide insights on how manufacturers can rectify the discovered security flaws and presents key policies like accountability when it comes to implementing technical features of autonomous agents.

Protecting an identity of IPv6 packet against Denial-of-Service (DoS) attack, depend on the proposed methods of cryptography and steganography. Reliable communication using the security aspect is the most visible issue, particularly in IPv6 network applications. Problems such as DoS attacks, IP spoofing and other kinds of passive attacks are common. This paper suggests an approach based on generating a randomly unique identities for every node. The generated identity is encrypted and hided in the transmitted packets of the sender side. In the receiver side, the received packet verified to identify the source before processed. Also, the paper involves implementing nine experiments that are used to test the proposed scheme. The scheme is based on creating the address of IPv6, then passing it to the logistics map then encrypted by RSA and authenticated by SHA2. In addition, network performance is computed by OPNET modular. The results showed better computation power consumption in case of lost packet, average events, memory and time, and the better results as total memory is 35,523 KB, average events/sec is 250,52, traffic sent is 30,324 packets/sec, traffic received is 27,227 packets/sec, and lose packets is 3,097 packets/sec.

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.

Nowadays, data is essential in several fields, such as science, finance, medicine, and transportation, which means its value continues to rise. Relational databases are vulnerable to copyright threats when transmitted and shared as a carrier of data. The watermarking technique is seen as a partial solution to the problem of securing copyright ownership. However, most of them are currently restricted to numerical attributes in relational databases, limiting their versatility. Furthermore, they modify the source data to a large extent, failing to keep the characteristics of the original database, and they are susceptible to solid malicious attacks. This paper proposes a new robust reversible watermarking technique, Fields Based Inserting Position Tuples algorithm (FBIPT), for relational databases. FBIPT does not modify the original database directly; instead, it inserts some position tuples based on three Fields―Group Field, Feature Field, and Control Field. Field information can be calculated by numeric attributes and any attribute that can be transformed into binary bits. FBIPT technique retains all the characteristics of the source database, and experimental results prove the effectiveness of FBIPT and show its highly robust performance compared to state-of-the-art watermarking schemes.

Terrorism, and radicalization are major economic, political, and social issues faced by the world in today's era. The challenges that governments and citizens face in combating terrorism are growing by the day. Artificial intelligence, including machine learning and deep learning, has shown promising results in predicting terrorist attacks. In this paper, we attempted to build a machine learning model to predict terror activities using a global terrorism database in both relational and graphical forms. Using the Neo4j Sandbox, you can create a graph database from a relational database. We used the node2vec algorithm from Neo4j Sandbox's graph data science library to convert the high-dimensional graph to a low-dimensional vector form. In order to predict terror activities, seven machine learning models were used, and the performance parameters that were calculated were accuracy, precision, recall, and F1 score. According to our findings, the Logistic Regression model was the best performing model which was able to classify the dataset with an accuracy of 0.90, recall of 0.94 precision of 0.93, and an F1 score of 0.93.

Databases are at the heart of modern applications and any threats to them can seriously endanger the safety and functionality of applications relying on the services offered by a DBMS. It is therefore pertinent to identify key risks to the secure operation of a database system. This paper identifies the key risks, namely, SQL injection, weak audit trails, access management issues and issues with encryption. A malicious actor can get help from any of these issues. It can compromise integrity, availability and confidentiality of the data present in database systems. The paper also identifies various means and ways to defend against these issues and remedy them. This paper then proceeds to identify from the literature, the potential solutions to these ameliorate the threat from these vulnerabilities. It proposes the usage of encryption to protect the data from being breached and leveraging encrypted databases such as CryptoDB. Better access control norms are suggested to prevent unauthorized access, modification and deletion of the data. The paper also recommends ways to prevent SQL injection attacks through techniques such as prepared statements.

Data Analytics is at the core of almost all modern ap-plications ranging from science and finance to healthcare and web applications. The evolution of data analytics over the last decade has been dramatic - new methods, new tools and new platforms - with no slowdown in sight. This rapid evolution has pushed the boundaries of data analytics along several axis including scalability especially with the rise of distributed infrastructures and the Big Data era, and interoperability with diverse data management systems such as relational databases, Hadoop and Spark. However, many analytic application developers struggle with the challenge of production deployment. Recent experience suggests that it is difficult to deliver modern data analytics with the level of reliability, security and manageability that has been a feature of traditional SQL DBMSs. In this tutorial, we discuss the advances and innovations introduced at both the infrastructure and algorithmic levels, directed at making analytic workloads scale, while paying close attention to the kind of quality of service guarantees different technology provide. We start with an overview of the classical centralized analytical techniques, describing the shift towards distributed analytics over non-SQL infrastructures. We contrast such approaches with systems that integrate analytic functionality inside, above or adjacent to SQL engines. We also explore how Cloud platforms' virtualization capabilities make it easier - and cheaper - for end users to apply these new analytic techniques to their data. Finally, we conclude with the learned lessons and a vision for the near future.

Based on the analysis of material performance data management requirements, a network-sharing scheme of material performance data is proposed. A material performance database system including material performance data collection, data query, data analysis, data visualization, data security management and control modules is designed to solve the problems of existing material performance database network sharing, data fusion and multidisciplinary support, and intelligent services Inadequate standardization and data security control. This paper adopts hierarchical access control strategy. After logging into the material performance database system, users can standardize the material performance data and store them to form a shared material performance database. The standardized material performance data of the database system shall be queried and shared under control according to the authority. Then, the database system compares and analyzes the material performance data obtained from controlled query sharing. Finally, the database system visualizes the shared results of controlled queries and the comparative analysis results obtained. The database system adopts the MVC architecture based on B/S (client/server) cross platform J2EE. The Third-party computing platforms are integrated in System. Users can easily use material performance data and related services through browsers and networks. MongoDB database is used for data storage, supporting distributed storage and efficient query.

This paper designs a network security protection system based on artificial intelligence technology from two aspects of hardware and software. The system can simultaneously collect Internet public data and secret-related data inside the unit, and encrypt it through the TCM chip solidified in the hardware to ensure that only designated machines can read secret-related materials. The data edge-cloud collaborative acquisition architecture based on chip encryption can realize the cross-network transmission of confidential data. At the same time, this paper proposes an edge-cloud collaborative information security protection method for industrial control systems by combining end-address hopping and load balancing algorithms. Finally, using WinCC, Unity3D, MySQL and other development environments comprehensively, the feasibility and effectiveness of the system are verified by experiments.

Web-based technologies are evolving day by day and becoming more interactive and secure. Completely Automated Public Turing test to tell Computers and Humans Apart (CAPTCHA) is one of the security features that help detect automated bots on the Web. Earlier captcha was complex designed text-based, but some optical recognition-based algorithms can be used to crack it. That is why now the captcha system is image-based. But after the arrival of strong image recognition algorithms, image-based captchas can also be cracked nowadays. In this paper, we propose a new captcha system that can be used to differentiate real humans and bots on the Web. We use advanced deep layers with pre-trained machine learning models for captchas authentication using a facial recognition system.

Visual Question Answering or VQA is a technique used in diverse domains ranging from simple visual questions and answers on short videos to security. Here in this paper, we talk about the video captcha that will be deployed for user authentication. Randomly any short video of length 10 to 20 seconds will be displayed and automated questions and answers will be generated by the system using AI and ML. Automated Programs have maliciously affected gateways such as login, registering etc. Therefore, in today's environment it is necessary to deploy such security programs that can recognize the objects in a video and generate automated MCQs real time that can be of context like the object movements, color, background etc. The features in the video highlighted will be recorded for generating MCQs based on the short videos. These videos can be random in nature. They can be taken from any official websites or even from your own local computer with prior permission from the user. The format of the video must be kept as constant every time and must be cross checked before flashing it to the user. Once our system identifies the captcha and determines the authenticity of a user, the other website in which the user wants to login, can skip the step of captcha verification as it will be done by our system. A session will be maintained for the user, eliminating the hassle of authenticating themselves again and again for no reason. Once the video will be flashed for an IP address and if the answers marked by the user for the current video captcha are correct, we will add the information like the IP address, the video and the questions in our database to avoid repeating the same captcha for the same IP address. In this paper, we proposed the methodology of execution of the aforementioned and will discuss the benefits and limitations of video captcha along with the visual questions and answering.

Currently, the rapid development of digital communication and multimedia has made security an increasingly prominent issue of communicating, storing, and transmitting digital data such as images, audio, and video. Encryption techniques such as chaotic map based encryption can ensure high levels of security of data and have been used in many fields including medical science, military, and geographic satellite imagery. As a result, ensuring image data confidentiality, integrity, security, privacy, and authenticity while transferring and storing images over an unsecured network like the internet has become a high concern. There have been many encryption technologies proposed in recent years. This paper begins with a summary of cryptography and image encryption basics, followed by a discussion of different kinds of chaotic image encryption techniques and a literature review for each form of encryption. Finally, by examining the behaviour of numerous existing chaotic based image encryption algorithms, this paper hopes to build new chaotic based image encryption strategies in the future.

To protect the security of video information use encryption technology to be effective means. In practical applications, the structural complexity and real-time characteristics of video information make the encryption effect of some commonly used algorithms have some shortcomings. According to the characteristics of video, to design practical encryption algorithm is necessary. This paper proposed a novel scheme of chaotic image encryption, which is based on scrambling and diffusion structure. Firstly, the breadth first search method is used to scramble the pixel position in the original image, and then the pseudo-random sequence generated by the time-varying bilateral chaotic symbol system is used to transform each pixel of the scrambled image ratio by ratio or encryption. In the simulation experiment and analysis, the performance of the encrypted image message entropy displays that the new chaotic image encryption scheme is effective.

In the era of Internet usage growth, storage services are widely used where users' can store their data, while hackers techniques pose massive threats to users' data security. The proposed system introduces multiple layers of security where data confidentiality, integrity and availability are achieved using honey encryption, hashed random passwords as well as detecting intruders and preventing them. The used techniques can ensure security against brute force and denial of service attacks. Our proposed methodology proofs the efficiency for storing and retrieving data using honey words and password hashing with less execution time and more security features achieved compared with other systems. Other systems depend on user password leading to easily predict it, we avoid this approach by making the password given to the user is randomly generated which make it unpredictable and hard to break. Moreover, we created a simple user interface to interact with users to take their inputs and store them along with the given password in true database, if an adversary detected, he will be processed as a normal user but with fake information taken from another database called false database, after that, the admin will be notified about this illegitimate access by providing the IP address. This approach will make the admin have continuous detection and ensure availability and confidentiality. Our execution time is efficient as the encryption process takes 244 ms and decryption 229 ms.

Structured Query Language Injection (SQLi) is a client-side application vulnerability that allows attackers to inject malicious SQL queries with harmful intents, including stealing sensitive information, bypassing authentication, and even executing illegal operations to cause more catastrophic damage to users on the web application. According to OWASP, the top 10 harmful attacks against web applications are SQL Injection attacks. Moreover, based on data reports from the UK's National Fraud Authority, SQL Injection is responsible for 97% of data exposures. Therefore, in order to prevent the SQL Injection attack, detection SQLi system is essential. The contribution of this research is securing web applications by developing a browser extension for Google Chrome using Long Short-Term Memory (LSTM), which is a unique kind of RNN algorithm capable of learning long-term dependencies like SQL Injection attacks. The results of the model will be deployed in static analysis in a browser extension, and the LSTM algorithm will learn to identify the URL that has to be injected into Damn Vulnerable Web Application (DVWA) as a sample-tested web application. Experimental results show that the proposed SQLi detection model based on the LSTM algorithm achieves an accuracy rate of 99.97%, which means that a reliable client-side can effectively detect whether the URL being accessed contains a SQLi attack or not.

Derivatives are key to numerous science, engineering, and machine learning applications. While existing tools generate derivatives of programs in a single language, modern parallel applications combine a set of frameworks and languages to leverage available performance and function in an evolving hardware landscape. We propose a scheme for differentiating arbitrary DAG-based parallelism that preserves scalability and efficiency, implemented into the LLVM-based Enzyme automatic differentiation framework. By integrating with a full-fledged compiler backend, Enzyme can differentiate numerous parallel frameworks and directly control code generation. Combined with its ability to differentiate any LLVM-based language, this flexibility permits Enzyme to leverage the compiler tool chain for parallel and differentiation-specitic optimizations. We differentiate nine distinct versions of the LULESH and miniBUDE applications, written in different programming languages (C++, Julia) and parallel frameworks (OpenMP, MPI, RAJA, Julia tasks, MPI.jl), demonstrating similar scalability to the original program. On benchmarks with 64 threads or nodes, we find a differentiation overhead of 3.4–6.8× on C++ and 5.4–12.5× on Julia.

Network intrusion detection technology has been a popular application technology for current network security, but the existing network intrusion detection technology in the application process, there are problems such as low detection efficiency, low detection accuracy and other poor detection performance. To solve the above problems, a new treatment combining artificial intelligence with network intrusion detection is proposed. Artificial intelligence-based network intrusion detection technology refers to the application of artificial intelligence techniques, such as: neural networks, neural algorithms, etc., to network intrusion detection, and the application of these artificial intelligence techniques makes the automatic detection of network intrusion detection models possible.

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.

Aiming at the single hopping strategy in the terminal information hopping active defense technology, a variety of heterogeneous hopping modes are introduced into the terminal information hopping system, the definition of the terminal information is expanded, and the adaptive adjustment of the hopping strategy is given. A network adversarial training simulation system is researched and designed, and related subsystems are discussed from the perspective of key technologies and their implementation, including interactive adversarial training simulation system, adversarial training simulation support software system, adversarial training simulation evaluation system and adversarial training Mock Repository. The system can provide a good environment for network confrontation theory research and network confrontation training simulation, which is of great significance.

Information security construction is a social issue, and the most urgent task is to do an excellent job in information risk assessment. The bayesian neural network currently plays a vital role in enterprise information security risk assessment, which overcomes the subjective defects of traditional assessment results and operates efficiently. The risk quantification method based on fuzzy theory and Bayesian regularization BP neural network mainly uses fuzzy theory to process the original data and uses the processed data as the input value of the neural network, which can effectively reduce the ambiguity of language description. At the same time, special neural network training is carried out for the confusion that the neural network is easy to fall into the optimal local problem. Finally, the risk is verified and quantified through experimental simulation. This paper mainly discusses the problem of enterprise information security risk assessment based on a Bayesian neural network, hoping to provide strong technical support for enterprises and organizations to carry out risk rectification plans. Therefore, the above method provides a new information security risk assessment idea.

In recent years, as an important part of the Internet, web applications have gradually penetrated into life. Now enterprises, units and institutions are using web applications regardless of size. Intrusion detection to effectively identify malicious traffic has become an inevitable requirement for the development of network security technology. In addition, the proportion of deserialization vulnerabilities is increasing. Traditional intrusion detection mostly focuses on the identification of SQL injection, XSS, and command execution, and there are few studies on the identification of deserialization attack traffic. This paper use a method to extracts relevant features from the deserialized traffic or even the obfuscated deserialized traffic by reorganizing the traffic and running the relevant content through simulation, and combines deep learning technology to make judgments to efficiently identify deserialization attacks. Finally, a prototype system was designed to capture related attacks in real-world. The technology can be used in the field of malicious traffic detection and help combat Internet crimes in the future.