Biblio

Intelligent Systems for Personal Data Cyber Security is a critical component of the Personal Information Management of Medicaid Enterprises. Intelligent Systems for Personal Data Cyber Security combines components of Cyber Security Systems with Human-Computer Interaction. It also uses the technology and principles applied to the Internet of Things. The use of software-hardware concepts and solutions presented in this report is, in the authors’ opinion, some step in the working-out of the Intelligent Systems for Personal Data Cyber Security in Medicaid Enterprises. These concepts may also be useful for developers of these types of systems.

There are a large number of illegal websites on the Internet, such as pornographic websites, gambling websites, online fraud websites, online pyramid selling websites, etc. This paper studies the use of crawler technology for digital forensics on illegal websites. First, a crawler based illegal website forensics program is designed and developed, which can detect the peripheral information of illegal websites, such as domain name, IP address, network topology, and crawl key information such as website text, pictures, and scripts. Then, through comprehensive analysis such as word cloud analysis, word frequency analysis and statistics on the obtained data, it can help judge whether a website is illegal.

Under the background of rapid development of campus informatization, the information management of college students' innovative activities is slightly outdated, and the operation of the traditional innovative activity record system has gradually become rigid. In response to this situation, this paper proposes a B/S architecture-based information management system for college students' innovative activities based on the current situation that the network and computers are widely used, which is designed for the roles of relevant managers of students on campus, such as class teachers, teachers and counselors, and has developed various functions to meet the needs of such users as class teachers, including user The system is designed to meet the needs of classroom teachers, classroom teachers and tutors. In order to meet the requirements of generality, expandability and ease of development, the overall architecture of the system is based on the javaEE platform, with JSP technology as the main development technology.

In the process of compiling the power-cut window period of the power grid equipment maintenance plan, problems such as omission of constraints are prone to occur due to excessive reliance on manual experience. In response to these problems, this paper proposes a method for mining key features of the power-cut window based on grey relational analysis. Through mining and analysis of the historical operation data of the power grid, the operation data of new energy, and the historical power-cut information of equipment, the indicators that play a key role in the arrangement of the outage window period of the equipment maintenance plan are found. Then use the key indicator information to formulate the window period. By mining the relationship between power grid operation data and equipment power outages, this paper can give full play to the big data advantages of the power grid, improve the accuracy and efficiency of the power-cut window period.

With the development of society, people have higher and higher requirements for the quality of life, and the management of legal cases has become more and more important. In this case, the research on how to realize electronization and networking has become the inevitable demand of the current information age. Therefore, this paper designs and develops the legal case management information system based on J2EE. Firstly, this paper introduces the related technologies of J2EE, then expounds the importance of legal case management informatization, and designs the legal case management information system according to the technical framework of J2EE. Finally, the performance of the system is tested. The test results show that the load capacity of the system is strong, the response time is 2–4 seconds, the resource utilization is relatively low, and the number of concurrent users is maintained at about 150. These show that the performance of the system fully meets the needs of legal case information management.

Cyber Threat Intelligence (CTI) sharing platforms are valuable tools in cybersecurity. However, despite the fact that effective CTI exchange highly depends on human aspects, cyber behavior in CTI sharing platforms has been notably less investigated by the security research community.Motivated by this research gap, we ground our work in the concrete challenge of understanding users’ perceptions of information sharing in CTI platforms. To this end, we propose a conceptual workflow and toolchain that would seek to verify whether users have an accurate comprehension of how far information travels when shared in a CTI sharing platform.We contextualize our concept within MISP as a use case, and discuss the benefits of our socio-technical approach as a potential tool for security analysis, simulation, or education/training support. We conclude with a brief outline of future work that would seek to evaluate and validate the proposed model.

Nowadays, biometric-based authentication systems are widely used. This fact has led to increased attacks on biometric data of users. Therefore, biometric template protection is sure to keep the attention of researchers for the security of the authentication systems. Many previous works proposed the biometric template protection schemes by transforming the original biometric data into a secure domain, or establishing a cryptographic key with the use of biometric data. The main purpose was that fulfill the all three requirements: cancelability, security, and performance as many as possible. In this paper, using random projection merged with fuzzy commitment, we will introduce a hybrid scheme of biometric template protection. We try to limit their own drawbacks and take full advantages of these techniques at the same time. In addition, an analysis of non-invertibility property will be exercised with regards to the use of random projection aiming at enhancing the security of the system while preserving the discriminability of the original biometric template.

With the rapid development of the electric power Internet-of-Things (power IoT) technology and the widespread use of general-purpose software, hardware and network facilities, the power IoT has become more and more open, which makes the traditional power system face new cyber security threats. In order to find the vulnerable device nodes and attack links in the power IoT system, this paper studies a set of attack path calculation methods and vulnerability node discovery algorithms, which can construct a power IoT attack simulation program based on the value of equipment assets and information attributes. What’s more, this paper has carried on the example analysis and verification on the improved IEEE RBTS Bus 2 system. Based on the above research plan, this paper finally developed a set of power IoT attack simulation tool based on distribution electronic stations, which can well find the vulnerable devices in the system.

In this paper, we will describe the design and implementation of a secure payment system based on QR codes. These QR codes have been extensively used in recent years since they speed up the payment process and provide users with ultimate convenience. However, as convenient as they may sound, QR-based online payment systems are vulnerable to different types of attacks. Therefore, transaction processing needs to be secure enough to protect the integrity and confidentiality of every payment process. Moreover, the online payment system must provide authenticity for both the sender and receiver of each transaction. In this paper, the security of the proposed QR-based system is provided using visual cryptography. The proposed system consists of a mobile application and a payment gateway server that implements visual cryptography. The application provides a simple and user-friendly interface for users to carry out payment transactions in user-friendly secure environment.

Distributed Denial-of-Service (DDoS) attack is a long-lived attack that is hugely harmful to the Internet. In particular, the emergence of a new type of DDoS called Link Flooding Attack (LFA) makes the detection and defense more difficult. In LFA, the attacker cuts off a specific area by controlling large numbers of bots to send low-rate traffic to congest selected links. Since the attack flows are similar to the legitimate ones, traditional schemes like anomaly detection and intrusion detection are no longer applicable. Blockchain provides a new solution to address this issue. In this paper, we propose a blockchain-based LFA detection scheme, which is deployed on routers and servers in and around the area that we want to protect. Blockchain technology is used to record and share the traceroute information, which enables the hosts in the protected region to easily trace the flow paths. We implement our scheme in Ethereum and conduct simulation experiments to evaluate its performance. The results show that our scheme can achieve timely detection of LFA with a high detection rate and a low false positive rate, as well as a low overhead.

Cybersecurity has become an emerging challenge for business information management and critical infrastructure protection in recent years. Artificial Intelligence (AI) has been widely used in different fields, but it is still relatively new in the area of Cyber-Physical Systems (CPS) security. In this paper, we provide an approach based on Machine Learning (ML) to intelligent threat recognition to enable run-time risk assessment for superior situation awareness in CPS security monitoring. With the aim of classifying malicious activity, several machine learning methods, such as k-nearest neighbours (kNN), Naïve Bayes (NB), Support Vector Machine (SVM), Decision Tree (DT) and Random Forest (RF), have been applied and compared using two different publicly available real-world testbeds. The results show that RF allowed for the best classification performance. When used in reference industrial applications, the approach allows security control room operators to get notified of threats only when classification confidence will be above a threshold, hence reducing the stress of security managers and effectively supporting their decisions.

Commodity hypervisors are widely deployed to support virtual machines (VMs) on multiprocessor hardware. Their growing complexity poses a security risk. To enable formal verification over such a large codebase, we introduce microverification, a new approach that decomposes a commodity hypervisor into a small core and a set of untrusted services so that we can prove security properties of the entire hypervisor by verifying the core alone. To verify the multiprocessor hypervisor core, we introduce security-preserving layers to modularize the proof without hiding information leakage so we can prove each layer of the implementation refines its specification, and the top layer specification is refined by all layers of the core implementation. To verify commodity hypervisor features that require dynamically changing information flow, we introduce data oracles to mask intentional information flow. We can then prove noninterference at the top layer specification and guarantee the resulting security properties hold for the entire hypervisor implementation. Using microverification, we retrofitted the Linux KVM hypervisor with only modest modifications to its codebase. Using Coq, we proved that the hypervisor protects the confidentiality and integrity of VM data, while retaining KVM’s functionality and performance. Our work is the first machine-checked security proof for a commodity multiprocessor hypervisor.

With the rapid development of e-commerce and the increasing popularity of credit cards, online transactions have become increasingly smooth and convenient. However, many online transactions suffer from credit card fraud, resulting in huge losses every year. Many financial organizations and e-commerce companies are devoted to developing advanced fraud detection algorithms. This paper presents an approach to detect fraud transactions using the IEEE-CIS Fraud Detection dataset provided by Kaggle. Our stacked model is based on Gradient Boosting, LightGBM, CatBoost, and Random Forest. Besides, implementing StackNet improves the classification accuracy significantly and provides expandability to the network architecture. Our final model achieved an AUC of 0.9578 for the training set and 0.9325 for the validation set, demonstrating excellent performance in classifying different transaction types.

With big data and artificial intelligence, we conduct the research of the buyers' identification and involvement, and their investments such as time, experience and consultation in various channels are analyzed and iterated. We establish a set of AI channel governance system with the functions of members' behavior monitoring, transaction clearing and deterrence; Through the system, the horizontal spillover effect of their behavior is controlled. Thus, their unfair perception can be effectively reduced and the channel performance can be improved as well.

Cloud computing facilitates the access of applications and data from any location by using any device with an internet connection. It enables multiple applications and users to access the same data resources. Cloud based information sharing is a technique that allows researchers to communicate and collaborate, that leads to major new developments in the field. It also enables users to access data over the cloud easily and conveniently. Privacy, authenticity and confidentiality are the three main challenges while sharing data in cloud. There are many methods which support secure data sharing in cloud environment such as Attribute Based Encryption(ABE), Role Based Encryption, Hierarchical Based Encryption, and Identity Based Encryption. ABE provides secure access control mechanisms for integrity. It is classified as Key Policy Attribute Based Encryption(KP-ABE) and Ciphertext Policy Attribute Based Encryption(CP-ABE) based on access policy integration. In KPABE, access structure is incorporated with user's private key, and data are encrypted over a defined attributes. Moreover, in CPABE, access structure is embedded with ciphertext. This paper reviews CP-ABE methods that have been developed so far for achieving secured data sharing in cloud environment.

The advancement of Information and Communication Technologies has brought numerous facilities and benefits to society. In this environment, surrounded by technologies, data, and personal information, have become an essential and coveted tool for many sectors. In this scenario, where a large amount of data has been collected, stored, and shared, privacy concerns arise, especially when dealing with sensitive data such as health data. The information owner generally has no control over his information, which can bring serious consequences such as increases in health insurance prices or put the individual in an uncomfortable situation with disclosing his physical or mental health. While privacy regulations, like the General Data Protection Regulation (GDPR), make it clear that the information owner must have full control and management over their data, disparities have been observed in most systems and platforms. Therefore, they are often not able to give consent or have control and management over their data. For the users to exercise their right to privacy and have sufficient control over their data, they must know everything that happens to them, where their data is, and where they have been. It is necessary that the entire life cycle, from generation to deletion of data, is managed by its owner. To this end, this article presents an Information Chain of Custody Model based on Blockchain technology, which allows from the traceability of information to the offer of tools that will enable the effective management of data, offering total control to its owner. The result showed that the prototype was very useful in the traceability of the information. With that it became clear the technical feasibility of this research.

The recent technological advances and changes in the daily human activities increased the production and sharing of data. In the ecosystem of interconnected systems, data can be circulated among systems for various reasons. This could lead to exchange of private or sensitive information between entities. Data Sanitisation involves processes and practices that remove sensitive and private information from documents before sharing them with entities that should not have access to this information. This paper presents the design and development of a data sanitisation and redaction solution for a Cyber Threat Intelligence sharing platform. The Data Sanitisation and Redaction Plugin has been designed with the purpose of operating as a plugin for the ECHO Project’s Early Warning System platform and enhancing its operative capabilities during information sharing. This plugin aims to provide automated security and privacy-based controls to the concept of CTI sharing over a ticketing system. The plugin has been successfully tested and the results are presented in this paper.

Nowadays, peoples are very concerned about their data privacy. Hence, all the current security methods should be improved to stay relevant in this fast-growing technology world. Visual Cryptography (VC) is a cryptographic technique that using the image processing method. The implementation of VC can be varying and flexible to be applied to the system that requires an extra security precaution as it is one of the effective solutions in securing the data exchange between two or more parties. The main purpose of the development of V-CRYPT System is to improve the current VC technique and make it more complex in the encryption and decryption process. V-CRYPT system will let the user enter the key, then select the image that they want to encrypt, and the system will split the image into four shares: share0, share1, share2, share3. Each pixel of the image will be splatted into a smaller block of subpixels in each of the four shares and encrypted as two subpixels in each of the shares. The decryption will work only when the user selects all the shares, and the correct text key is entered. The system will superimpose all the shares and producing one perfect image. If the incorrect key is entered, the resulted image will be unidentified. The results show that V- CRYPT is a valuable alternative to existing methods where its security level is higher in terms of adding a secure key and complexity.

With the advancement of network technology, users can now easily gain access to and benefit from networks. However, the number of network violations is increasing. The main issue with this violation is that irresponsible individuals are infiltrating the network. Network intrusion can be interpreted in a variety of ways, including cyber criminals forcibly attempting to disrupt network connections, gaining unauthorized access to valuable data, and then stealing, corrupting, or destroying the data. There are already numerous systems in place to detect network intrusion. However, the systems continue to fall short in detecting and counter-attacking network intrusion attacks. This research aims to enhance the detection of Denial of service (DoS) by identifying significant features and identifying abnormal network activities more accurately. To accomplish this goal, the study proposes an Intrusion Analysis System for detecting Denial of service (DoS) network attacks using machine learning. The accuracy rate of the proposed method using random forest was demonstrated in our experimental results. It was discovered that the accuracy rate with each dataset is greater than 98.8 percent when compared to traditional approaches. Furthermore, when features are selected, the detection time is significantly reduced.

A door enables you to enter a room without breaking through a wall. Also, a door enables you for privacy, environmental or security reasons. The problem statement which is the biometric system sometimes is sensitive and will not be able to sense the biological pattern of the employer’s fingerprint due to sweat and other factors. Next, people tend to misplace their key or RFID card. Apart from that, people tend to forget their pin number for a door lock. The objective of this paper is to present a secret knock intensity for door lock security system using Arduino and mobile. This project works by using a knock intensity and send the information to mobile application via wireless network to unlock or lock the door.

Steganography is the method of hiding one type of information into other type of information, hiding a secret a message in a cover so that others can't know the presence of the secret information. It provides an extra layer of security in communication and information sharing. Security is an important aspect of the communication process; everyone want security in communication. The main purpose of this paper is to introduce security of information that people share among them. In this paper we are presenting different methods of substitution techniques of image steganography and their comparison. Least significant bit and most significant bit substitution techniques are used. Information is hidden in an image file and then decoded back for the secret message. Hiding the presence of any hidden information makes this more secure. This implementation can be used by secret service agencies and also common people for secure communication.

Arbitrary style transfer means that stylized images can be generated from a set of arbitrary input image pairs of content images and style images. Recent arbitrary style transfer algorithms lead to distortion of content or incompletion of style transfer because network need to make a balance between the content structure and style. In this paper, we introduce a dual attention network based on style attention and channel attention, which can flexibly transfer local styles, pay more attention to content structure, keep content structure intact and reduce unnecessary style transfer. Experimental results show that the network can synthesize high quality stylized images while maintaining real-time performance.

Verification code recognition system based on convolutional neural network. In order to strengthen the network security defense work, this paper proposes a novel verification code recognition system based on convolutional neural network. The system combines Internet technology and big data technology, combined with advanced captcha technology, can prevent hackers from brute force cracking behavior to a certain extent. In addition, the system combines convolutional neural network, which makes the verification code combine numbers and letters, which improves the complexity of the verification code and the security of the user account. Based on this, the system uses threshold segmentation method and projection positioning method to construct an 8-layer convolutional neural network model, which enhances the security of the verification code input link. The research results show that the system can enhance the complexity of captcha, improve the recognition rate of captcha, and improve the security of user accounting.

Advanced persistent threat (APT) attackers usually conduct a large number of network reconnaissance before a formal attack to discover exploitable vulnerabilities in the target network and system. The static configuration in traditional network systems provides a great advantage for adversaries to find network targets and launch attacks. To reduce the effectiveness of adversaries' continuous reconnaissance attacks, this paper develops a moving target defense (MTD) enhanced cyber deception defense system based on software-defined networks (SDN). The system uses virtual network topology to confuse the target network and system information collected by adversaries. Also Besides, it uses IP address randomization to increase the dynamics of network deception to enhance its defense effectiveness. Finally, we implemented the system prototype and evaluated it. In a configuration where the virtual network topology scale is three network segments, and the address conversion cycle is 30 seconds, this system delayed the adversaries' discovery of vulnerable hosts by an average of seven times, reducing the probability of adversaries successfully attacking vulnerable hosts by 83%. At the same time, the increased system overhead is basically within 10%.

The industrial revolution 4.0 has become a challenge for various sectors in mastering information technology, one of which is the arts and culture sector. Cultural arts that are quite widely spread and developed in Indonesia are traditional masks. Traditional masks are one of the oldest and most beautiful cultures in Indonesia. However, with the development of the era to the digital world in the era of the industrial revolution 4.0, this beloved culture is fading due to the entry of foreign cultures and technological developments. Many young people who succeed the nation do not understand this cultural art, namely traditional masks. So those cultural arts such as traditional masks can still keep up with the development of digital technology in industry 4.0, we conduct research to use technology to preserve this traditional mask culture. The research uses the ADDIE method starting with Analyze, Design, Develop, Implement, and Evaluate. We took some examples of traditional masks such as Malangan masks, Cirebon masks, and Panji masks from several regions in Indonesia. This research implements marker-based Augmented reality technology and makes a traditional mask book that can be a means of augmented reality.