Security Protection Mechanisms Research Articles

Robust control with protected feedback information for switched systems under injection attacks

In this paper, the robust security protection control for switched systems is studied. A robust anti-disturbance mechanism using the Radial Basis Function Neural Network (RBFNN) is proposed for switched systems, which can approximate and compensate for the impact of unknown disturbance on the system state. Then, a network security protection mechanism based on encoder and decoder is presented, which has the ability to resist the dual impact on the feedback information caused by the network privacy snooping and data injection attacks. Accordingly, stability analysis and state-feedback controller design are given for the switched systems under unknown disturbance, network privacy snooping and injection attacks. Finally, simulation results illustrate the effectiveness of the proposed method.

Application of distributed database in management information system

This article discusses the application methods of distributed databases in management information systems. First, the two main types of structured and isomorphic databases are analyzed. Then, it elaborates on the application technology of distributed databases from five aspects: structural design, data transmission, security strategy, function implementation, and data synchronization maintenance, and proposes the selection of network topology mode, data segmentation strategy, and optimized transmission algorithm according to business needs. , establish security protection mechanisms, and support OLTP and OLAP processing methods. Finally, distributed databases provide essential support for management information systems by horizontally expanding storage and computing capabilities but also bring technical challenges.

Traffic Fingerprints for Homogeneous IoT Traffic Based on Packet Payload Transition Patterns

Traffic fingerprint was considered an effective security protection mechanism in IoT scenarios because it can be used to automatically identify accessed devices. However, the results of replication experiments show that the classic traffic fingerprints based on simple network traffic attribute features have a significantly lower ability to identify accessed devices in real 5G IoT scenarios compared to what was stated in traditional IoT scenarios. The growing homogenization of IoT traffic caused by the application of 5G is believed to be the reason for the poor ability of traditional traffic fingerprints to identify 5G IoT terminals. Studying an enhanced traffic fingerprint is necessary to accommodate the homogeneous Internet of Things traffic. In addition, during the reproducing experiments, we noticed that the solution of overlap is a key factor that restricts the recognition ability of one-vs-all multi-classifiers, and the efficiency of existing methods still has some room for optimization. Based on targeted improvements to these two issues, we proposed an enhanced IoT terminal traffic fingerprint based on packet payload transition patterns to improve the device recognition ability in homogeneous IoT traffic. Additionally, we designed an improved solution for overlap based on density centers to expedite decision making. According to the experimental results, when compared with the existing traffic fingerprint, the proposed traffic fingerprint in this study demonstrated a Macro-Average Precision of close to 90% for network traffic from real 5G IoT terminals. The proposed overlap solution based on the density centers reduced the decision-making time from hundreds of seconds to tens of seconds while ensuring decision-making accuracy.

Analysis of the Current Situation, Opportunities, Challenges and Countermeasures of China's Digital Payment System in the Post-epidemic Era

In recent years, under the influence of the COVID-19 pandemic, China's digital payment system has encountered significant opportunities, challenges, and changes. The digital payment system mainly includes three parts: electronic currency, electronic payment, and digital currency. This paper takes the perspective of the post-pandemic era and integrates the previous research on the digital payment system. It comprehensively analyzes the current situation, challenges, and development opportunities of China's digital payment system from different angles by examining the development of China's digital payment market. It is found that the pandemic has stimulated the continuous development of digital payment, gradually replacing cash settlement. In the post-pandemic era, emerging industries have shown strong vitality for digital payment. However, digital payment also has risks of inadequate information security, digital divide, and private enterprise monopolies. In the post-pandemic era, digital payment has more development opportunities and presents a sustainable development trend with the promotion of legal digital currency, stimulation of emerging industries, and support of government policies. This article proposes corresponding forward-looking suggestions from the perspectives of governments, companies, and individuals to further promote the development of China's future digital payment system in response to the above risks. Specifically, it should improve risk control and data security protection mechanisms, help vulnerable groups cross the digital divide and mobile payment dilemma, and accelerate the promotion and use of China's legal digital currency.

Research on the Impact of Artificial Intelligence on the Development of the Accounting Industry

The article explores the application of artificial intelligence in the field of accounting. Although artificial intelligence technology can improve accounting efficiency and accuracy, there are also a series of issues such as data quality, data security and privacy, model interpretability and auditing, and the balance between artificial intelligence technology and human professional literacy. Therefore, in application, it is necessary to balance the relationship between technology and human professional literacy, establish data quality control, data security protection, model interpretability, and audit mechanisms, and enhance the skills and knowledge of accountants to adapt to the development of artificial intelligence technology.

Adaptive Deep Feature Fusion for Continuous Authentication With Data Augmentation

Mobile devices are becoming increasingly popular and are playing significant roles in our daily lives. Insufficient security and weak protection mechanisms, however, cause serious privacy leakage of the unattended devices. To fully protect mobile device privacy, we propose ADFFDA, a novel mobile continuous authentication system using an Adaptive Deep Feature Fusion scheme for effective feature representation, and a transformer-based GAN for Data Augmentation, by leveraging smartphone built-in sensors of the accelerometer, gyroscope and magnetometer. Given the normalized sensor data, ADFFDA utilizes the transformer-based GAN consisting of a transformer-based generator and a CNN-based discriminator to augment the training data for CNN training. With the augmented data and the especially designed CNN based on the ghost module and ghost bottleneck, ADFFDA extracts deep features from the three sensors by the trained CNN, and exploits an adaptive-weighted concatenation method to adaptively fuse the CNN-extracted features. Based on the fused features, ADFFDA authenticates users by using the one-class SVM (OC-SVM) classifier. We evaluate the authentication performance of ADFFDA in terms of the efficiency of the transformer-based GAN, GAN-based data augmentation, CNN architecture, adaptive-weighted feature fusion, and OC-SVM classifier. The experimental results show that ADFFDA obtains the best authentication performance w.r.t representative approaches, by achieving a mean equal error rate of 0.01%.

Digital Forensic Readiness Model for Internet Voting

Voting is an exercise of choosing a preferred candidate through a process called an election. In many countries, this exercise is a basic human right. In every election process, there are some pre-requisite processes and procedures which must be set up first. These are essential in the pre-vote-casting stage, during vote-casting and post-vote-casting stage. Electoral disagreements amongst stakeholders and parties of interest are usually experienced in each of the above-mentioned voting process stages. The main points of conflict in an election process are vote rigging and vote fraud. Failure to amicably mitigate these issues can result in a criticised/rejected election result. Therefore, this research aims to address the problem of vote rigging and vote fraud allegations in an election process. The resolution thereof is achieved through the introduction of an online based voting system which is supported by a digital forensic readiness mechanism. Online voting system gives citizens the flexibility to use internet-enabled devices such as cell phones and laptops to cast their votes in a safe, secrete and secure protocol. To address the problem of vote rigging and vote fraud, the online voting system is integrated with cyber security and vote protection mechanisms. The cyber security and vote protection mechanism is based on Blockchain algorithms. A Blockchain-based voting process is a peer-to-peer mechanism where a decentralised database is used to store data. Tokens move directly from one peer (voter) to another peer (candidate). The results are tallied by counting the number of tokens paid to each candidate. Each voter is allocated a Bitcoin token and each candidate is allocated a Bitcoin address. During vote casting, the voter transfers their Bitcoin token into the wallet of a registered candidate. At the end of the voting process, the total number of Bitcoin tokens transferred to each candidate is counted and tallied up. The wallet is loaded with only one Bitcoin token, hence there is no possibility of double voting. The model ensures vote security, anonymity, auditability, accountability, accuracy and uniqueness.

Intrusion Detection in Intelligent Connected Vehicles Based on Weighted Self-Information

The Internet of Vehicles (IoV) empowers intelligent and tailored services for intelligent connected vehicles (ICVs). However, with the increasing number of onboard external communication interfaces, ICVs face the challenges of malicious network intrusions. The closure of traditional vehicles had led to in-vehicle communication protocols, including the most commonly applied controller area network (CAN), and a lack of security and privacy protection mechanisms. Therefore, to protect the connected vehicles and IoV systems from being attacked, an intrusion-detection method is proposed based on the features extracted from the arbitration identifier (ID) field of CAN messages. Specifically, a sliding window is used to continuously extract a frame of streaming CAN messages first. Afterward, the weighted self-information of the CAN message ID is defined, and both the weighted self-information and the normalized value of an ID are extracted as features. Based on the extracted features, a lightweight one-class classifier, the local outlier factor (LOF), is used to identify the outliers and detect malicious network intrusion attacks. Simulation experiments were conducted based on the public CAN dataset provided by the HCR Lab. The proposed method, using four different one-class classifiers, was analyzed, and it is also benchmarked with three information entropy-based intrusion-detection methods in the literature. The experimental results indicate that, compared to the benchmarks, the proposed method dramatically improves the detection accuracy for injection attacks, namely denial-of-service (DoS) and spoofing, especially when the number of injected messages is low.

Flexible and Efficient Multi-Keyword Ranked Searchable Attribute-Based Encryption Schemes

Currently, cloud computing has become increasingly popular and thus, many people and institutions choose to put their data into the cloud instead of local environments. Given the massive amount of data and the fidelity of cloud servers, adequate security protection and efficient retrieval mechanisms for stored data have become critical problems. Attribute-based encryption brings the ability of fine-grained access control and can achieve a direct encrypted data search while being combined with searchable encryption algorithms. However, most existing schemes only support single-keyword or provide no ranking searching results, which could be inflexible and inefficient in satisfying the real world’s actual needs. We propose a flexible multi-keyword ranked searchable attribute-based scheme using search trees to overcome the above-mentioned problems, allowing users to combine their fuzzy searching keywords with AND–OR logic gates. Moreover, our enhanced scheme not only improves its privacy protection but also goes a step further to apply a semantic search to boost the flexibility and the searching experience of users. With the proposed index-table method and the tree-based searching algorithm, we proved the efficiency and security of our schemes through a series of analyses and experiments.

A Dual Security Protection Mechanism for Cloud-Based Data Storage and Sharing

Abstract: Cloud-based data storage service has drawn increasing interest from both academic and industry in recent years due to their efficient and low-cost management. Since it provides services in an open network, it is urgent for service providers to make use of secure data storage and sharing mechanism to ensure data confidentiality and service user privacy. The most widely used method is encryption to protect sensitive data from being compromised. However, simply encrypting data (e.g., via AES) cannot fully address the practical need for data management. Besides, effective access control over download requests also needs to be considered so that Economic Denial of Sustainability (EDoS) attacks cannot be launched to hinder users from enjoying service.

Crime Record Management System

The Crime Record Management System is a web-based application designed to improve the management of crime records across all police stations in the country. Effective crime prevention, detection, and conviction rely heavily on a responsive information management system. Therefore, centralizing information management in crime is proposed for the efficient sharing of crucial information among police stations. Initially, the system will be implemented in cities and towns and later interconnected to enable police staff to access information from all records in the state, ultimately helping to close cases faster. The system will also generate information for proactive and preventive measures to fight crime. The project will adopt a distributed architecture with a centralized database storage system, with high priority given to security and data protection mechanisms. The application is designed to handle various modules and their associated reports, produced in line with the administrative staff's applicable strategies and standards. In summary, the Crime Record Management System is a web-based application that aims to improve the management of crime records across police stations in the country. The system is designed to centralize information management, generate crucial information for crime prevention, and provide efficient and fast access to records. The project adopts a distributed architecture with centralized database storage, prioritizing data protection and security mechanisms while producing reports that align with applicable administrative strategies and standards

A Reflection-Based Channel Fingerprint to Locate Physically Intrusive Devices in ICS

It is hard to conduct cyber attacks in industrial control systems (ICSs) because most underlying networks of ICS like the field bus network are isolated from the Internet. However, attackers can physically connect the intrusive device into the target network to launch various attacks, which bypasses the security protection mechanisms between the ICS and the Internet. Currently, no effective measures could defend against such unauthorized physical access attacks. In this paper, a reflection-based channel fingerprint is proposed to detect and locate these physically intrusive devices in the field bus network. We theoretically analyze the signal reflection characteristics and utilize inevitable changes in the channel fingerprint to detect the intrusive device. Besides, the detected anomaly features could be used to accurately estimate the intrusive device's location. In the end, the proposed method's effectiveness is validated through extensive simulation experiments.

Privacy‐preserving remote sensing images recognition based on limited visual cryptography

AbstractWith the arrival of new data acquisition platforms derived from the Internet of Things (IoT), this paper goes beyond the understanding of traditional remote sensing technologies. Deep fusion of remote sensing and computer vision has hit the industrial world and makes it possible to apply Artificial intelligence to solve problems such as automatic extraction of information and image interpretation. However, due to the complex architecture of IoT and the lack of a unified security protection mechanism, devices in remote sensing are vulnerable to privacy leaks when sharing data. It is necessary to design a security scheme suitable for computation‐limited devices in IoT, since traditional encryption methods are based on computational complexity. Visual Cryptography (VC) is a threshold scheme for images that can be decoded directly by the human visual system when superimposing encrypted images. The stacking‐to‐see feature and simple Boolean decryption operation make VC an ideal solution for privacy‐preserving recognition for large‐scale remote sensing images in IoT. In this study, the secure and efficient transmission of high‐resolution remote sensing images by meaningful VC is achieved. By diffusing the error between the encryption block and the original block to adjacent blocks, the degradation of quality in recovery images is mitigated. By fine‐tuning the pre‐trained model from large‐scale datasets, we improve the recognition performance of small encryption datasets for remote sensing images. The experimental results show that the proposed lightweight privacy‐preserving recognition framework maintains high recognition performance while enhancing security.

State-of-the-art survey of in-vehicle protocols and automotive Ethernet security and vulnerabilities.

With the help of advanced technology, the automotive industry is in continuous evolution. Modern vehicles are not only comprised of mechanical components but also contain highly complex electronic devices and connections to the outside world. Today's vehicle usually has between 30 and 70 ECUs (Electronic Control Units), which communicate with each other over standard communication protocols. There are different types of in-vehicle network protocols and bus systems, including the Controlled Area Network (CAN), Local Interconnected Network (LIN), FlexRay, Media Oriented System Transport (MOST), and Automotive Ethernet (AE). Modern cars are also able to communicate with other devices through wired or wireless interfaces such as USB, Bluetooth, Wi-Fi or even 5G. Such interfaces may expose the internal network to the outside world and can be seen as entry points for cyber-attacks. In this paper, the main interest is in the AE network protocol. AE is a special Ethernet design that provides the bandwidth needed for today's applications, and the potential for even greater performance in the future. However, AE is a "best effort" protocol, which cannot be considered reliable. This implies that it is not trustworthy in terms of reliability and timely deliveries. The focus of this paper is to present a state-of-the-art survey of security threats and protection mechanisms relating to AE. After introducing and comparing the different protocols being used in the embedded networks of current vehicles, we analyze the potential threats targeting the AE network and describe how attackers' opportunities can be enhanced by the new communication abilities of modern cars. Finally, we present and compare the AE security solutions currently being devised to address these problems and propose some recommendations and challenges to deal with security issue in AE protocol.

A Lightweight Privacy-Preserving System for the Security of Remote Sensing Images on IoT

The acquisition of massive remote sensing data makes it possible to deeply fuse remote sensing and artificial intelligence (AI). The mobility and cost advantages of new sensing platforms in the Internet of Things (IoT) make them ideal for continuous deployment rather than traditional airborne platforms. However, remote sensing devices are vulnerable to malicious attacks and privacy leaks when sharing data due to the complex architecture and heterogeneity of IoT and the lack of a unified security protection mechanism. Traditional protection methods based on public-key encryption require not only complex operations but also energy consumption, which poses new challenges for resources-limited IoT. The objective of this paper was to propose a lightweight privacy-preserving system for the security of remote-sensing images based on visual cryptography. This stacking-to-see feature of visual cryptography enables the efficient encryption of big data such as high-resolution and multi-scale remote sensing images in resource-constrained IoT. To alleviate image quality degradation in visual cryptography, we combined denoising neural networks to extract high-quality images from encrypted datasets, thus improving the recognition accuracy of loss datasets. We conducted extensive experiments, and the results verify the effectiveness of the proposed method in terms of privacy protection and classification accuracy.

A survey on federated learning: challenges and applications.

Federated learning (FL) is a secure distributed machine learning paradigm that addresses the issue of data silos in building a joint model. Its unique distributed training mode and the advantages of security aggregation mechanism are very suitable for various practical applications with strict privacy requirements. However, with the deployment of FL mode into practical application, some bottlenecks appear in the FL training process, which affects the performance and efficiency of the FL model in practical applications. Therefore, more researchers have paid attention to the challenges of FL and sought for various effective research methods to solve these current bottlenecks. And various research achievements of FL have been made to promote the intelligent development of all application areas with privacy restriction. This paper systematically introduces the current researches in FL from five aspects: the basics knowledge of FL, privacy and security protection mechanisms in FL, communication overhead challenges and heterogeneity problems of FL. Furthermore, we make a comprehensive summary of the research in practical applications and prospect the future research directions of FL.

Исследование методов прогнозирования атак на информационные системы

The article analyzes the methods of forecasting attacks on information systems. Within the framework of experimental studies, the best method for predicting attacks on information systems has been identified. At the present stage of society’s development, the problems of information security come to the fore in most areas of the company’s activities. This is due to a significant number of informatization projects currently being implemented. Most of them are aimed at building a unified information space in order to optimize the processing of large volumes of various kinds of information, including ensuring its reliable storage and prompt access for participants of information exchange. Priority tasks in this direction are: identification, analysis and classification of existing mechanisms for the implementation of threats to information security; assessment of possible damage; identification of basic measures to counter threats; elimination of vulnerabilities; development of security criteria and protection mechanisms. One of the means of solving these problems is the detection of computer attacks. Malicious impacts on information systems can be presented in the form of network attacks. A network attack is an intentional action by third parties aimed at establishing control over a local or remote computer or computer system. As a result of attacks, attackers can disrupt the network, change account rights, receive users’ personal data and implement other goals. Network attacks by the nature of the impact on the network can be divided into active and passive. The active ones affect the network, which may cause the network to malfunction, or modification of its settings. This type of exposure leaves traces in the network, which is why it is immediately calculated that it will be detected. A passive attack is carried out without affecting the network. But her work violates network security. It is much more difficult to detect it than the active one due to the lack of direct intervention. Basically, the purpose of the attack is unauthorized access to protected information, distorting it or intercepting it. In the first case, the data is changed, in the second, access is performed without interfering with the data structure.

A CAN Bus Security Testbed Framework for Automotive Cyber-Physical Systems

The popularization of electronic devices and the enrichment of external interfaces have increased the attack surface of the automotive cyber-physical system (CPS). As a vital part of the CPS, the controller area network (CAN) is more vulnerable to security threats due to the lack of corresponding security protection mechanisms. This kind of security problem has also attracted extensive attention from academia and industry. Researchers have proposed numerous intrusion detection models for the in-vehicle CAN bus, solving some security problems to a certain extent. However, vehicle manufacturers seldom disclose the internal details of vehicle ECUs due to safety concerns. Thus, it is difficult for researchers to investigate the operation mechanism of the bus. Meanwhile, there is a risk of personal safety in completing attack experiments on real vehicles, which can also lead to the lack and diversification of in-vehicle network data, especially the data of attack behavior. Based on real vehicle data, an open, adaptable, and low-risk CAN bus security testbed framework in the automotive CPS is proposed in this study, aiming to enrich the operation data of the CAN bus and enhance the personal safety of researchers. Besides, the delay of the testbed sending and receiving periodic and aperiodic CAN messages is theoretically explored. The results demonstrate that the generated timestamp in the dataset is mainly associated with the timestamp of the real vehicle data and that the transmission and collection of time series data are completed by Algorithm 1 and Algorithm 2. In the evaluation of the security testbed, the stability of the security testbed is studied from the two indicators of delay and packet loss rate. The experiment reveals that the testbed has a small relative delay difference and a low packet loss rate. Moreover, the DTW algorithm is employed to calculate the distance between the real vehicle and the testbed, and the experimental results demonstrate that the testbed is featured with high similarity and simulation.

A management method of chronic diseases in the elderly based on IoT security environment

Due to the global influenza pandemic and the increasing health problems of the elderly, healthcare for the elderly has become an important application of Internet of Things(IoT) technology. However, under the condition of smart healthcare, the privacy of the elderly is not well protected. Therefore, to ensure the information security of the elderly, this paper proposes a management method for elderly chronic diseases based on the IoT security environment, establishes a security protection mechanism, and uses the IoT technology to monitor the vital signs of the elderly in real-time. At the same time, the Disease Immune Rehabilitation Algorithm (DIRA) is constructed based on the physiological data collected by the device to identify healthy people and reduce the number of infected people. Through experiments on several generations, the results show that this method can increase the proportion of the immune population to a certain extent and reduce the number of infected people.

Research on technical scheme for multi type load resource information access

In order to improve the regulation ability of adjustable load resources, realize the aggregation, sharing and regulation of “source-load-storage” regulation resources in the region, and improve the adjustable load management ability, the criteria and methods for accessing the operation system of load resource aggregators to the regulation center are proposed according to the safety protection requirements of power monitoring system and the communication ability of multi type load resource aggregators. To meet the security access of multiple resources, combined with different access modes of load resources, a network security protection mechanism combining software and hardware is proposed. Digital certificate, data encryption, situation awareness, analysis and early warning and other technologies are used to build security protection strategies for private network and different access modes such as Internet, wireless and optical fiber, so as to adapt to the security access of multiple resources. An interaction mode based on standardized data format, using web services (restful HTTPS), file interaction and 104 protocol could be established to adapt multi type adjustable load resource model and safe interaction of real-time data..