Security Protection Measures Research Articles

Assessing Evaluative Practice in Protective Security

With the potential implementation of Martyn’s Law in the UK, a national legislation focused on enhancing protective security measures in public and crowded spaces, there is an urgent need to understand effective evaluation in this field. Drawing insights from monitoring and evaluation practices in counter-terrorism, preventing and countering violent extremism and other fields, the article explores how the success of protective security interventions could be defined and measured across various dimensions. In doing so, it aims to contribute not only to the effective implementation of Martyn’s Law but also to the broader evidence base for future interventions in protective security.

Research on Information Security Management and Protection Measures of Financial Enterprises in the Era of Big Data---Take the USA of Industrial and Commercial Bank of China

Research on Information Security Management and Protection Measures of Financial Enterprises in the Era of Big Data---Take the USA of Industrial and Commercial Bank of China

Global Information Management Strategy Driven by IT

This paper explores the strategy of global information management driven by IT. Firstly, the concept and characteristics of global information are introduced, emphasizing the flow and exchange of information on a global scale. Then, the pivotal role of IT in driving global information management is discussed, including providing efficient communication and collaboration tools, supporting global business processes, and managing data. Subsequently, an IT-driven global information management strategy is proposed, encompassing the establishment of a global information architecture, implementation of unified information management standards and policies, and strengthening data security and privacy protection measures. Finally, the future development trends of IT-driven global information management are outlined, and a summary of the paper is provided.

Survey: federated learning data security and privacy-preserving in edge-Internet of Things

AbstractThe amount of data generated owing to the rapid development of the Smart Internet of Things is increasing exponentially. Traditional machine learning can no longer meet the requirements for training complex models with large amounts of data. Federated learning, as a new paradigm for training statistical models in distributed edge networks, alleviates integration and training problems in the context of massive and heterogeneous data and security protection for private data. Edge computing processes data at the edge layers of data sources to ensure low-data-delay processing; it provides high-bandwidth communication and a stable network environment, and relieves the pressure of processing massive data using a single node in the cloud center. A combination of edge computing and federated learning can further optimize computing, communication, and data security for the edge-Internet of Things. This review investigated the development status of federated learning and expounded on its basic principles. Then, in view of the security attacks and privacy leakage problems of federated learning in the edge Internet of things, relevant work was investigated from cryptographic technologies (such as secure multi-party computation, homomorphic encryption and secret sharing), perturbation schemes (such as differential privacy), adversarial training and other privacy security protection measures. Finally, challenges and future research directions for the integration of edge computing and federated learning are discussed.

The Role of Artificial Intelligence in Cyber Security

The research paper explores the pivotal role of artificial intelligence (AI) in bolstering cyber security threat detection and protection measures. By leveraging advanced AI techniques such as machine learning, deep learning, and behavioral analysis, organizations can effectively identify and mitigate cyber threats in real-time. AI enables proactive threat detection by analyzing vast amounts of data, detecting patterns, and predicting potential security incidents. Moreover, AI-driven security solutions offer scalability, efficiency, and adaptability, making them indispensable in combating the evolving cyber threat landscape. Overall, AI plays a crucial role in fortifying cyber security defenses and safeguarding critical assets against sophisticated cyberattacks. As cyber threats continue to grow in complexity and frequency, the integration of AI into security frameworks will not only enhance response times but also empower organizations to stay one step ahead of potential attacks Furthermore, as organizations increasingly adopt AI-driven security measures, the importance of integrating ethical considerations and addressing potential biases in AI algorithms becomes paramount. While these technologies can significantly enhance threat detection capabilities, they also risk perpetuating existing inequalities if not meticulously designed and monitored. For instance, biased data sets may lead to disproportionate scrutiny of certain user groups, undermining trust in security systems. Additionally, the dynamic nature of cyber threats necessitates continuous learning and adaptation from AI systems, highlighting the need for ongoing research and collaboration among cybersecurity professionals, technologists, and ethicists alike. By fostering a holistic approach that emphasizes both technological advancement and ethical responsibility, organizations can better navigate the complexities of modern cyber environments while enhancing their overall security posture

Design of a Trusted Content Authorization Security Framework for Social Media

Artificial intelligence-generated content (AIGC) technology has had disruptive results in AI, representing a new trend in research and application and promoting a new era of AI. The potential benefits of this technology are both profound and diverse. However, the benefits of generative tools are accompanied by a series of significant challenges, the most critical of which is that it may cause AI information pollution on social media and mislead the public. Traditional network security models have shown their limitations in dealing with today’s complex network threats, so ensuring that generated content published on social media accurately reflects the true intentions of content creators has become particularly important. This paper proposes a security framework called “secToken”. The framework adopts multi-level security and privacy protection measures. It combines deep learning and network security technology to ensure users’ data integrity and confidentiality while ensuring credibility of the published content. In addition, the framework introduces the concept of zero trust security, integrates OAuth2.0 ideas, and provides advanced identity authentication, fine-grained access control, continuous identity verification, and other functions, to comprehensively guarantee the published content’s reliability on social media. This paper considers the main issues of generative content management in social media and offers some feasible solutions. Applying the security framework proposed in this paper, the credibility of generated content published on social media can be effectively ensured and can help detect and audit published content on social media. At the operational level, when extracting key information summaries from user-generated multimodal artificial intelligence-generated content and binding them to user identity information as a new token to identify user uniqueness, it can effectively associate user identity information with the current network status and the generated content to be published on the platform. This method significantly enhances system security and effectively prevents information pollution caused by generative artificial intelligence on social media platforms. This innovative method provides a powerful solution for addressing social and ethical challenges and network security issues.

Network awareness of security situation information security measurement method based on data mining

Awareness of Network Security Situation (abbreviated as NSS for short) technology is in a period of vigorous development recently. NSS technology means network security situational awareness technology. It refers to the technology of collecting, processing, and analyzing various real-time information in the network to understand and evaluate the current network security status. It can not only find network security threats, but also reflect the NSS in the system security metrics, and provide users with targeted security protection measures. Based on data mining methods, this paper analyzed and models perceived threats and security events with data mining algorithms, and improved information security measurement methods based on association analysis. This paper proposed network security information analysis and NSS based on data mining, and analyzed the experimental results of network awareness of NSS information security measurement. The experimental results showed that when the Timer was 8, the accuracy of the awareness of NSS information security measurement method based on data mining can reach 92.89%. The data mining model had the highest accuracy of 93.14% in situation understanding and evaluation of KDDCup-99 dataset. The results showed that the model can accurately predict the NSS. When Timer was 6, the highest accuracy of the model was 92.71%. In general, the NSS prediction mining model based on KDDCup-99 can better understand, evaluate and predict the situation.

A Double-Direction Cognitive Interaction Security Architecture for CPSS: A Case Study

With the development of big data analytics, new generation artificial intelligence, and other technologies, Cyber-Physical-Social Systems (CPSSs) have gradually come into people’s view. However, the complex integration brings about more complex security issues, especially the risks and problems in double-directional cognition triggered by the interactive integration of humans and Artificial Intelligence (AI). Therefore, based on the security analysis architecture and methodology for CPSSs, this paper carries out a case study with the help of the power operation and maintenance (O&M) system, which is a typical CPSS, to verify the validity of the relevant architecture and methodology and to propose reasonable security protection measures.

Research on Substation Network Security Situational Awareness Strategy and Equipment Remote Operation and Maintenance

Abstract Substation network security is the key to maintaining the stable operation of power systems. In the face of growing threats of network attacks, traditional security protection measures have been brutal to meet the needs of modern power systems. Research on substation network security, situational awareness strategies, and remote operation and maintenance of equipment is essential to improve network defense capability and ensure the continuity and reliability of power supply. This study explores effective security situational awareness methods and remote operation and maintenance techniques to provide new solutions for substation network security. This paper builds an efficient network attack detection model by introducing linear discriminant analysis (LDA) and radial basis function (RBF) neural networks. The experiment uses the KDD Cup99 dataset, which is preprocessed to provide the model training and testing data. The LDA-RBF model in this paper outperforms the traditional RNF neural and BP neural networks regarding recognition rate. Specifically, the recognition rate reaches 90.2% for the Smurf attack and 100% for the Ipsweep attack. The proposed model of the study also performs well in terms of leakage and false alarm rates, with an overall recognition rate of 97.00%. This study proposes a network security situational awareness strategy and equipment remote operation and maintenance method that can effectively enhance substation networks’ security and operation and maintenance efficiency.

European Artificial Intelligence Act: an AI security approach

PurposeThe purpose of this paper is to highlight the key technical challenges that derive from the recently proposed European Artificial Intelligence Act and specifically, to investigate the applicability of the requirements that the AI Act mandates to high-risk AI systems from the perspective of AI security.Design/methodology/approachThis paper presents the main points of the proposed AI Act, with emphasis on the compliance requirements of high-risk systems. It matches known AI security threats with the relevant technical requirements, it demonstrates the impact that these security threats can have to the AI Act technical requirements and evaluates the applicability of these requirements based on the effectiveness of the existing security protection measures. Finally, the paper highlights the necessity for an integrated framework for AI system evaluation.FindingsThe findings of the EU AI Act technical assessment highlight the gap between the proposed requirements and the available AI security countermeasures as well as the necessity for an AI security evaluation framework.Originality/valueAI Act, high-risk AI systems, security threats, security countermeasures.

PECULIARITIES OF THE DEVELOPMENT OF THE IT SPHERE IN THE CONTEXT OF DIGITAL TRANSFORMATION OF THE ECONOMY OF UKRAINE

Abstract. The article identifies key problems that hold back the prospects for developing the potential of the IT sphere in Ukraine. The specifics of functioning and development of the IT sphere in Ukraine in the conditions of digital transformation of the economy are considered. The paper analyzes the main scientific approaches to defining the essence of the definition "digital economy". Positive and negative aspects of the influence of digital economy and digital tools on the development of the IT sphere, employment of the population, and the course of business processes of economic entities are systematized. A comparative analysis of the values of indices of network readiness of Ukraine with respect to developed countries of Europe and the world is given. The influence of indices of access to information and communication technologies and the use of ICT on the state of development of digital economy of a certain country is determined. The main trends of new technologies within the framework of the formation of Industry 4.0, including: blockchain technologies, artificial intelligence, creation of "digital doubles", use of drones, 3D printing technologies, VR/AR, cyber security tools, etc., have been established. The fact of rapid development of the IT sphere in Ukraine has been established. At the same time, in terms of international comparisons, Ukraine lags behind developed countries of the world in terms of indicators characterizing the trends in the development of digital economy. The dynamics of changes in the number of individual entrepreneurs in the field of IT has been studied. It has been established that the digitalization of theeconomy radically changes the sphere of work and employment, increasing the share of digital workplaces, volumes of remote and virtual employment, as well as changing the structure of employment in general. The directions for improving the development of domestic IT sphere during the acceleration of the processes of digitization of the economy and social life, including the improvement of legislative framework by regulating the activities of business entities in the IT sphere, the development of digital economy, innovative activities and the protection of rights to intellectual property objects, continuous professional development of human resources on mastering and using modern digital technologies, development of social partnership relations and digital culture of citizens, compliance with cyber security and information protection measures, are substantiated.

Investigating the impact of cyber security risks and reliability scenarios under the influence of IoT on the Smart Grid environment

The modern infrastructure of the smart grid minimises the power losses and maintain the electric power flow closer to nominal values. It enables the bidirectional flow of energy between consumer end and generation end along with a communication flow. Due to increasing number of smart grid equipment, it is important to investigate security protection and appropriate recovery measures for the smart grid application. Cyber security measures are important to consider for improving the reliability and intelligence features of the smart grid. This paper investigated the security measurements and different standards to counter the future security threats to the smart grid power systems. False data injection and risk analysis is carried out along with an internet of thing (IoT) based security solution to increase the reliability of data flow and communication on the smart grid. The investigations are performed by creating a power system model on a MATALB/Simulink environment.

Computer network information security and protection strategies in the era of big data

The Internet has entered the big data era, and as more and more businesses employ big data, cloud computing, the Internet of Things, artificial intelligence, and other technologies, the amount of data on the Internet as a whole has also grown dramatically. While Big Data technology has improved the efficiency of many companies, it has also brought challenges for computer network information security protection technology, including massive data privacy leaks that seriously jeopardize the privacy of Internet users. This paper synthesizes literature review findings proving that computer network information security in the big data era has the characteristics of universality, suddenness, concealment, and intelligence in order to address this issue. It also studies the main network information security technologies, such as network situational awareness, vulnerability scanning, intrusion detection and firewall, and identity authentication. In addition, the paper finds that the current problems of network information security mainly include phishing attacks, computer viruses, ransomware, denial of service attacks, APT attacks and threats from inside the network. Finally, the paper explores a number of protective measures to address existing security issues, including the development of network monitoring and threat intelligence systems, the implementation of access control and authentication technologies, network isolation methods, regular software scanning and updates, and organising internal staff security training. Through this study we are able to fully understand the overall pulse of current computer network information security technology and development trends in the era of Big Data, thus providing a more comprehensive and accurate reference for Big Data-related participants to develop responsive information security protection measures and strategies to provide designation.

Network Traffic Classification Based on SD Sampling and Hierarchical Ensemble Learning

With the increase in cyber threats in recent years, there have been more forms of demand for network security protection measures. Network traffic classification technology is used to adapt to the dynamic threat environment. However, network traffic has a natural unbalanced class distribution problem, and the single model leads to the low accuracy and high false-positive rate of the traditional detection model. Given the above two problems, this paper proposes a new dataset balancing method named SD sampling based on the SMOTE algorithm. Different from the SMOTE algorithm, this method divides the sample into two types that are easy and difficult to classify and only balances the difficult-to-classify sample, which not only overcomes the SMOTE’s overgeneralization but also combines the idea of oversampling and undersampling. In addition, a two-layer structure combined with XGBoost and the random forest is proposed for multiclassification of anomalous traffic, since using a hierarchical structure can better classify minority abnormal traffic. This paper conducts experiments on the CICIDS2017 dataset. The results show that the classification accuracy of the proposed model is more than 99.70% and that the false-positive rate is less than 0.34%, indicating that the proposed model is better than traditional models.

Research on Network Behavior Risk Measurement Method Based on Traffic Analysis

At present, the network security problem is facing a serious threat, and network security events continue to occur. It has become an important link to prevent network attacks and ensure network security. According to the network security protection measures and security technical requirements, it has become an urgent need to establish appropriate security measurement methods and strengthen the monitoring and analysis of network security status. This study proposes a network behavior risk measurement method based on traffic analysis to accurately and objectively evaluate the security state of the network. Traffic is the most basic behavior of the network and the basis of security risk measurement. Firstly, we regard the traffic data as network behavior to build scenarios. Through differential manifold modeling, the traffic data and topology of the network system are semantically described to form a matrix. Then, after manifold dimensionality reduction, the objective risk assessment value can be obtained by manifold mapping and Riemann metric. In this study, the differential manifold theory is applied to network behavior risk measurement, and the innovation of differential manifold in the field of network behavior risk measurement is given. After giving the network behavior risk measurement theory, we first verify the effectiveness of the proposed method through the simulation experiments. Secondly, the public CIC-IDS-2017 data set is used for analysis and calculation to prove the accuracy of the proposed method.

Research on the Technology of Computer Network Security Protection

The continuous deepening of the number and importance of sensitive and key info stored in computer network system has brought severe challenges to the computer cyber security protection tech. It is urgent to upgrade the cyber security protection, so that it can serve and match the healthy development of computer network. Based on this, this paper first analyses the concept and connotation of computer cyber security, then studies the main content of computer cyber security protection tech, and finally gives the computer cyber security protection measures.

Research on Personal Information Security Protection Measures in the Big Data Era

While big data brings convenience to people, it also exposes various problems. Personal information is always exposed to risks. Protecting personal information from infringement in the big data era has become an urgent problem to be solved. This paper studies from three aspects. First, it analyzes the importance of personal information security protection in the big dataera: maintaining normal social production and life order, protecting personal physical and mental health and property safety, and reducing the occurrence of cyber crimes. Second, it summarizes the problems existing in personal information security protection in the big data era: serious disclosure of personal privacy information, malicious attacks on personal privacy by cyber hackers, low awareness of personal information security protection, and imperfect laws and regulations on personal information security protection. Third, the main measures of personal information security protection in the big data eraare put forward: improving the laws and regulations of personal information protection, raising the awareness of personal information security protection, building a data-centric security governance system, and strengthening the prevention and governance of cyber hacking attacks.

Internet of things intrusion detection model and algorithm based on cloud computing and multi-feature extraction extreme learning machine

With the rapid development of the Internet of Things (IoT), there are several challenges pertaining to security in IoT applications. Compared with the characteristics of the traditional Internet, the IoT has many problems, such as large assets, complex and diverse structures, and lack of computing resources. Traditional network intrusion detection systems cannot meet the security needs of IoT applications. In view of this situation, this study applies cloud computing and machine learning to the intrusion detection system of IoT to improve detection performance. Usually, traditional intrusion detection algorithms require considerable time for training, and these intrusion detection algorithms are not suitable for cloud computing due to the limited computing power and storage capacity of cloud nodes; therefore, it is necessary to study intrusion detection algorithms with low weights, short training time, and high detection accuracy for deployment and application on cloud nodes. An appropriate classification algorithm is a primary factor for deploying cloud computing intrusion prevention systems and a prerequisite for the system to respond to intrusion and reduce intrusion threats. This paper discusses the problems related to IoT intrusion prevention in cloud computing environments. Based on the analysis of cloud computing security threats, this study extensively explores IoT intrusion detection, cloud node monitoring, and intrusion response in cloud computing environments by using cloud computing, an improved extreme learning machine, and other methods. We use the Multi-Feature Extraction Extreme Learning Machine (MFE-ELM) algorithm for cloud computing, which adds a multi-feature extraction process to cloud servers, and use the deployed MFE-ELM algorithm on cloud nodes to detect and discover network intrusions to cloud nodes. In our simulation experiments, a classical dataset for intrusion detection is selected as a test, and test steps such as data preprocessing, feature engineering, model training, and result analysis are performed. The experimental results show that the proposed algorithm can effectively detect and identify most network data packets with good model performance and achieve efficient intrusion detection for heterogeneous data of the IoT from cloud nodes. Furthermore, it can enable the cloud server to discover nodes with serious security threats in the cloud cluster in real time, so that further security protection measures can be taken to obtain the optimal intrusion response strategy for the cloud cluster.

AN EMPIRICAL SURVEY TO SUBSTANTIATE THE NEED FOR IMPROVEMENT IN USER SECURITY AWARENESS IN MOBILE BANKING IN NIGERIA

Mobile banking application solutions are now the most preferable means of performing financial transactions and almost all banks in Nigeria have developed and deployed at least one mobile application to service their Client (Customers). However, the security of users of these solutions is a critical issue that cannot be overlooked and understand the concerns of users (Customers) when it comes to their privacy, data, and their perceptions on the issues of access delegation, an empirical survey was conducted to identify what user opinion and concerns are. This study seeks to investigate if users of mobile banking applications have sufficient security awareness and if it has translated into better security protection measures. Also, the knowledge level on the security features available on mobile banking apps and the best way users can be educated to understand their role in securing their privacy. The research work investigates the security risk in access delegation by the account holders to third parties. The findings from the survey identified a low level of security awareness among users in terms of security safety measures as only 14.4% of respondents admitted that they will consider security as a factor which further that there is a lack of security awareness among users and this has heightened the concerns of users with 58.2% expressing serious concern over their privacy and the problem of Access delegation as a risky behavior most users indulge in. The study also reveals that most users believe that the current security measures in mobile banking are at a low level. An Empirical Survey using quantitative descriptive analysis was used for analyzing the data collected and the tool used for data collection was a structured questionnaire and Statistical Package for Social Science (SPSS) to code the data for analysis.

Secure authentication scheme in 6G‐enabled mobile Internet of things for online English education

In recent years, the mobile Internet of Things (MIoT) for online English education has quickly become an important means and main tool for people to learn and communicate English due it has characteristics and advantages such as high efficiency and no geographical restrictions. However, most MIoT for online English education is vulnerable to security attacks because of the online learning device's weak security protection measures and their communication channels with open wireless nature. To address these issues, herein, a novel secure authentication scheme in 6G-enabled MIoT for online English education is proposed. The proposed authentication scheme does not rely on the ideal tamper-proof device. In this scheme, the certificate and key are updated in real-time to resist side-channel attacks, and pseudonym seeds are used to generate pseudonyms to hide user's identity information and realise identity privacy protection. The scheme allows the attacker to continuously obtain private key information. Detailed security analysis proves that the authors’ scheme is unforgeable to adaptive selection message attacks and can satisfy the security requirements of 6G-enabled MIoT for online English education. In addition, compared with other related studies, the proposed scheme achieves better performance in terms of computation and communication costs.