Network Intrusion Research Articles

EFFECT OF DATA PROTECTION FRAMEWORKS AGAINST CYBERCRIMES ON CYBER SECURITY IN NIGERIA

The paper Effect of Data Security Frameworks against Cybercrimes and Cyber Security in Nigeria critically examines the various data protection frameworks in Nigeria and how these are impacting curtailing cybercrimes in the country. The paper examined the institutions responsible for data security and how they carry it out. The work utilized both the secondary and primary sources of data collection. Key Informant Interview (KII) and the observation method were deployed to collect primary data for the study. Information on data protection was also collected from (NDPC) and other relevant agencies. Information from reports from the internet, scholarly articles in journals, and information from websites and books were used in the paper. The paper utilised the Social Strain Theory (SST) as the theoretical framework for the study. The paper observed that data protection regulations in Nigeria make it mandatory for all Public Institutions (PIs) in Nigeria to have a comprehensive cyber security framework, which is to protect against unauthorized access. Several factors are responsible for data insecurity and the attendant increase in cybercrimes in the country. These include, among others: i. Lack of knowledge on the part of most officers of the penalty for data breach allowed by them. ii. Poor dissemination of the content of the NDPA. iii. The poor economic condition of the country makes many officers to seek means of getting extra money, this also includes their volunteering information that they should not disclose in the first place. The paper observed that though cyber security in Nigeria is not within the mandate of the EFCC, the Commission plays very important role in dealing with the threat of cybercrimes in Nigeria. Records shows that of the 12,394 convictions of the EFCC from inception till 31st December 2023. Over 80% of them are on cybercrime and related offences. The paper concluded that inspire of the efforts of the EFCC and other agencies in tackling cybercrimes in Nigeria, the failure of the data protection frameworks in the country in ensuring confidentiality, integrity and security of the data at their disposal is the reason for the spiralling of cybercrimes in the country. The paper recommends that the Nigerian government adopt the best practice policies and regulations, promote awareness, and continuously update cyber security practices, which is germane to enhancing data protection in the country.

Genomic Data Fusion using Paillier Cryptosystem

The proposed work performs secure data fusion using homomorphic encryption, specifically the Paillier cryptosystem. The Paillier cryptosystem allows computation to be performed on encrypted data without decrypting it first, thus ensuring the privacy and security of the computation. The experiment measures the algorithm's performance based on execution time, memory usage, security, accuracy, and scalability. The data-level Paillier cryptosystem approach is generally slower than the feature-level fusion method due to its more complex operations and computations. Scalability is limited by the time required for encryption, homomorphic addition, and decryption. Improving scalability can be achieved by parallelizing the encryption and decryption steps, optimizing the homomorphic addition algorithm, or using more efficient cryptographic primitives. The article compares the performance of the Paillier cryptosystem with differential privacy in terms of their advantages and disadvantages. By adopting a preemptive approach to data fusion security, healthcare organizations can minimize the risk of data breaches and protect patient privacy. Data fusion security is an important factor when dealing with medical records. In the field of medical records, data fusion refers to the method of combining multiple sources of data into a distinct record. This can include data from electronic health records (EHRs), medical imaging devices, wearable devices, and other sources. There are several security considerations that must be addressed when fusing data from multiple sources.

Privacy and personal data risk governance for generative artificial intelligence: A Chinese perspective

The rapid development of generative artificial intelligence (AI) has attracted global attention and posed challenges to existing data governance frameworks. The increased technical complexity and expanded scale of data usage not only make it more difficult to regulate AI but also present challenges for the current legal system. This article, which takes ChatGPT's training data and working principles as a starting point, examines specific privacy risks, data leakage risks, and personal data risks posed by generative AI. It also analyzes the latest practices in privacy and personal data protection in China. This article finds that while China's governance on privacy and personal data protection takes a macro-micro integration approach and a private-and-public law integration approach, there are shortcomings in the legal system. Given that the current personal data protection system centered on individual control is unsuitable for the modes of data processing by generative AI, and that private law is insufficient in safeguarding data privacy, urgent institutional innovation is needed to achieve the objective of “trustworthy AI.”

INLEC: An involutive and low energy lightweight block cipher for internet of things

The Internet of Things (IoT) has emerged as a pivotal force in the global technological revolution and industrial transformation.Despite its advancements, IoT devices continue to face significant security challenges, particularly during data transmission, and are often constrained by limited battery life and energy resources.To address these challenges,a low energy lightweight block cipher (INLEC) is proposed to mitigate data leakage in IoT devices. In addition, the Structure and Components INvolution (SCIN) design is introduced. It is constructed using two similar round functions to achieve front–back symmetry.This design ensures coherence throughout the INLEC encryption and decryption processes and addresses the increased resource consumption during the decryption phase in Substitution Permutation Networks (SPN).Furthermore, a low area S-box is generated through a hardware gate-level circuit search method combined with Genetic Programming (GP).This optimization leads to a 47.02% reduction in area compared to the S0 of Midori, using UMC 0.18μm technology. Moreover, a chaotic function is used to generate the optimal nibble-based involutive permutation, further enhancing its efficiency.In terms of performs, the energy consumption for both encryption and decryption with INLEC is 6.88 μJ/bit, representing 25.21% reduction compared to Midori.Finally, INLEC is implemented using STM32L475 PanDuoLa and Nexys A7 FPGA development boards, establishing an encryption platform for IoT devices. This platform provides functions for data acquisition, transmission, and encryption.

Carbon Price Forecasting Using Optimized Sliding Window Empirical Wavelet Transform and Gated Recurrent Unit Network to Mitigate Data Leakage

Precise forecasts of carbon prices are crucial for reducing greenhouse gas emissions and promoting sustainable, low-carbon development. To mitigate noise interference in carbon price data, hybrid models integrating data decomposition techniques are commonly utilized. However, it has been observed that the improper utilization of data decomposition techniques can lead to data leakage, thereby invalidating the model’s practical applicability. This study introduces a leakage-free hybrid model for carbon price forecasting based on the sliding window empirical wavelet transform (SWEWT) algorithm and the gated recurrent unit (GRU) network. First, the carbon price data are sampled using a sliding window approach and then decomposed into more stable and regular subcomponents through the EWT algorithm. By exclusively employing the data from the end of the window as input, the proposed method can effectively mitigate the risk of data leakage. Subsequently, the input data are passed into a multi-layer GRU model to extract patterns and features from the carbon price data. Finally, the optimized hybrid model is obtained by iteratively optimizing the hyperparameters of the model using the tree-structured Parzen estimator (TPE) algorithm, and the final prediction results are generated by the model. When used to forecast the closing price of the Guangdong Carbon Emission Allowance (GDEA) for the last nine years, the proposed hybrid model achieves outstanding performance with an R2 value of 0.969, significantly outperforming other structural variants. Furthermore, comparative experiments from various perspectives have validated the model’s structural rationality, practical applicability, and generalization capability, confirming that the proposed framework is a reliable choice for carbon price forecasting.

Application of Big Data and Quantum Computing in the Secure Federated Internet of Things

Federated Internet of Things (IoT) presents both unprecedented opportunities and challenges in security and data management. This study explores the integration of big data analytics and Quantum Computing as potential solutions to address security concerns within the Federated IoT ecosystem. The study examines the implications of leveraging big data analytics to process and analyze the massive volume of data generated by IoT devices. Advanced analytics techniques, including machine learning and anomaly detection algorithms, are employed to enhance the detection and mitigation of security threats such as unauthorized access, data breaches and malicious attacks. Furthermore, the study investigates the role of Quantum Computing infrastructure in providing scalable and reliable resources for securely storing, processing and transmitting IoT data. By offloading computational tasks to quantum-based platforms, the aim is to alleviate the burden on edge devices while ensuring robust security measures are in place to safeguard sensitive information. A comprehensive review of existing literature and case studies identifies key challenges and opportunities in implementing big data and Quantum Computing solutions within the Federated IoT environment. The study also proposes potential frameworks and methodologies for integrating these technologies effectively, considering factors such as data privacy, scalability and interoperability. Overall, this research aims to advance secure IoT systems by leveraging big data analytics and cloud computing. By addressing security concerns proactively and adopting innovative approaches, the goal is to create a more resilient and trustworthy Federated IoT ecosystem, benefiting society at large.

Secure SDLC: Incorporating Blockchain for Enhanced Security

In the rapidly evolving landscape of software development, security has emerged as a critical concern, particularly as the frequency and sophistication of cyber threats continue to rise. The Software Development Life Cycle (SDLC) traditionally emphasizes security at various stages; however, the integration of cutting-edge technologies such as blockchain has the potential to revolutionize this process. This research explores the incorporation of blockchain technology into the Secure SDLC to enhance security measures throughout the software development process. Blockchain, characterized by its decentralized, transparent, and immutable nature, offers a robust framework for mitigating risks associated with software vulnerabilities, data breaches, and unauthorized access. The study delves into how blockchain can be seamlessly integrated into each phase of the SDLC—requirements analysis, design, implementation, testing, deployment, and maintenance. By embedding blockchain protocols within these stages, the SDLC can achieve a higher level of security assurance. For instance, during the requirements analysis and design phases, smart contracts can be utilized to enforce security policies and validate the integrity of design documents. The implementation phase can benefit from blockchain's version control capabilities, ensuring that code changes are tracked, verified, and secure. During testing and deployment, blockchain can facilitate the creation of an immutable audit trail, recording all test results, configurations, and deployments, thereby preventing tampering and ensuring transparency.

My Holistic Data Share: A WEB3 Data Share Application: Extending Beyond Finance to Privacy-Protected Decentralized Share of Multi-Dimensional Data to Enhance Global Healthcare

Abstract WEB3.0 technologies on network architectures, distributed ledgers, and decentralized artificial intelligence represents a transformative shift in how data are handled, stored and shared. These innovations promise to significantly enhance consumer data privacy rights by addressing fundamental vulnerabilities associated with traditional centralized systems and self-custody wallets. Data breaches in traditional systems operated mainly by third parties are more common, resulting in significant data leaks because of centralized storage and excessive data movement, sometimes unnecessarily. Healthcare data breaches have been a growing concern globally. Several hospitals faced operational halts on account of the impact of ransomware on patient care and privacy. WEB3 Wallets are a vital component emerging as a significant force for global financial inclusion, especially in developing economies. They promote inclusion, reduce costs and empower individuals through self-custody. Though major improvements are needed in these wallets, their use is rising steadily. The global cryptocurrency user base is expected to reach over 500 million by 2025, with substantial growth in emerging markets, according to a report by Statista in 2023. This paper introduces a concept beyond cryptocurrencies and finance into everyday real-world use cases that need combinatorial access to a person’s holistic data, including financial and health records, genomic data, and advanced directives, among others, that need to be privacy protected and shared with specific actors identified for their roles in the WEB3 ecosystem through decentralized identifiers and non-fungible token badges identifying particular recipients. The author introduced the concept at ETHBoston in April 2024, won accolades for a primitive implementation using underlying threshold cryptography technologies, and enhanced it into a conceptual holistic data share application for global healthcare, as presented in this paper.

A Comparative Study on Database Breach and Security in Contemporary Perspective

A Comparative Study on Database Breach and Security in Contemporary Perspective

Network Intrusion Detection System Based on Information Gain with Deep Bidirectional Long Short-Term Memory

Network Intrusion Detection System Based on Information Gain with Deep Bidirectional Long Short-Term Memory

Detection of Intrusions using Transformer - LSTM Model

The project focuses on addressing the escalating issue of web attacks and ensuring cyberspace security in the era of advancing internet technology. The primary goal is to enhance intrusion detection systems (IDS) by moving beyond traditional keyword and rule- based methods. The project explores the integration of artificial intelligence, specifically deep learning techniques, to develop a more robust and adaptive approach to identifying network intrusion events. With the limitations of conventional detection methods in mind, there is a critical need for a more effective and versatile intrusion detection system. The rise in diverse attack methods necessitates an intelligent system capable of autonomously adapting to new attack patterns without relying on manual rule creation. This project is designed to benefit both individuals and organizations reliant on network services. Improved intrusion detection ensures a higher level of cybersecurity, safeguarding sensitive data and systems. The project's outcomes have the potential to contribute significantly to the broader field of network security. Leveraging the power of deep learning, the proposed model- Transformer (autoencoder) LSTM combines Transformer and LSTM architectures. Multiple attention mechanisms are employed to select and extract features, while the LSTM model facilitates understanding the sequential relationships within network traffic, enabling the accurate identification of intrusion events. And we are comparing the model with DNN, LSTM and Transformer(autoencoder). To enhance performance, CNN and CNN+LSTM extensions were incorporated into the project, complementing the Transformer (autoencoder) LSTM model. These models aim to further refine feature extraction and exploit spatial relationships within network traffic. Key Words:intrusion detection, transformer, LSTM, deep learning.

Enhancing Cybersecurity with Blockchain-based Identity Management

In the digital age, the rapid increase in cyber threats has highlighted the need for more robust and secure identity management systems. Traditional centralized identity management systems are often vulnerable to data breaches, unauthorized access, and identity theft, posing significant risks to individuals and organizations. This paper explores the potential of blockchain technology to enhance cybersecurity through decentralized identity management. By leveraging the inherent properties of blockchain—such as immutability, transparency, and decentralization—blockchain-based identity management systems offer a more secure and efficient alternative to traditional methods. The paper provides an in-depth analysis of how blockchain can be utilized to store and manage digital identities securely, reducing the risk of identity fraud and data breaches. It examines various blockchain-based identity management frameworks, highlighting their advantages and potential challenges in implementation. Furthermore, the study discusses the integration of blockchain with other emerging technologies, such as cryptographic algorithms and smart contracts, to create a more resilient cybersecurity infrastructure. Through a review of current literature and case studies, the paper demonstrates the effectiveness of blockchain in enhancing data security and privacy while promoting user control over personal information. The findings suggest that adopting blockchain-based identity management could significantly mitigate cybersecurity threats and enhance trust in digital transactions. However, the paper also acknowledges the need for addressing scalability, regulatory compliance, and interoperability issues to fully realize the potential of blockchain in cybersecurity. This research contributes to the growing body of knowledge on innovative cybersecurity solutions and provides a foundation for future studies in the field.

RUSSIA AND CHINA IN THE INTERNATIONAL DIGITAL DOMAIN

The purpose of the research. The article discusses interaction of Russia and China in the digital space and its impact on international relations. The purpose of the study is to analyze the strategies of Russian Federation and China in the digital technologies sphere, including cybersecurity, data protection and digital sovereignty. It is important to predict trends and potential changes in the international digital space, taking into account the growing influence of Russia and China, as well as their possible strategies in the world scene. Results. As a result of the research, the authors come to the conclusion that in the context of an increasingly multipolarity, Russia and China are seeking to expand their influence in the international digital space, which implies the use of various strategies aimed at strengthening their role in the global technology ecosystem. The authors conclude that cooperation between Russia and China in the digital sphere is becoming increasingly close, which can become a significant factor in changing the balance of power in global politics. Both states, using their unique governance models and approaches to digital technologies, have the opportunity to influence the formation of international norms and rules in this area. The study findings highlight the importance of public policy in identifying key challenges and risks associated with digital cooperation between Russia and China, including cyberattacks, data leaks and privacy issues.

Handling Encryption and Data Loss prevention in the Cloud-Based Systems

The current study focused on assessing the handling encryption and data loss prevention in the cloud. Cloud-based systems also known as cloud computing, increase organisational efficiency for proper data management processes. Cloud-based data loss or leakage prevention (DLP) improve data visualisation; hence, the companies can understand the risks and issues through proper data management. This study highlighted the way handling encryption and data loss prevention in the cloud can be useful for the development of business processes and for managing all operations through proper data management. IoT-based and AI-based cloud computing assess root causes and provide a clear insight into business problems. Hence, they can improve their data loss risk management services and promote continuous improvement.

BYOD security behaviour and preferences among hospital clinicians – A qualitative study

Background/ObjectiveThe use of personal devices for work purposes (Bring-your-own-device) has increased in hospitals, as it facilitates productivity and mobility for clinicians. However, owing to increased risk of leaking patient information, and heavy reliance of patient data privacy on user actions, BYOD is a major challenge for hospitals. There has been a dearth of empirical research studying clinicians’ BYOD security behaviour. Therefore, the study’s aim was to attain subjective understanding of clinicians’ attitudes and preferences towards protecting patient data on their devices through a qualitative study. Methods14 semi-structured interviews were conducted among Australian hospital-based clinicians. A hybrid thematic analysis was conducted using the framework method to explore socio-technical themes pertaining to the clinicians’ BYOD security behavioural practices. ResultsLimited use of secure tools like antivirus and passcodes, and inadequate separation of patient and personal data on BYOD devices was found. Key technology concerns included malware introduction into hospital network, inadvertent patient data sharing, and slow remote access. Hospitals lacked dedicated BYOD policies and training, resulting in unsafe practices. Participants also cited misalignment of BYOD policies with workflow needs, privacy maintenance challenges and fears of personal data breaches, while calling for improved communication between technical and clinical staff and a strong cybersecurity culture. ConclusionThis study provides a comprehensive understanding of BYOD related user behaviour and the usefulness of security controls used in time-sensitive and complex hospital environments. It can inform future policies or processes by advocating for secure and productive BYOD use.

TabSAL: Synthesizing Tabular data with Small agent Assisted Language models

Tabular data are widely used in machine-learning tasks because of their prevalence in various fields; however, the potential risks of data breaches in tabular data and privacy protection regulations render such data almost unavailable. Tabular data generation methods alleviate data unavailability by synthesizing privacy-free data, and generating data using language models is a novel innovation. Language models can synthesize high-quality datasets by learning knowledge from nondestructive information and recognizing the semantics of table columns. However, when current language models function as generators, their encoding methods are hindered by complicated decoding processes, and the limited predictive ability of language models restricts their generative capability. To this end, we propose an encoding method based on interactive data structures such as JavaScript Object Notation for converting tabular data. We design TabSAL, which is a pluggable tabular data generation framework with small agent assisted language models, to boost the predictive capability, resulting in high-quality synthetic datasets with a much lower computational resource cost. In addition, a benchmark that integrates eight datasets, three methods, and three assessment directions has been issued, which indicates that TabSAL surpasses the state of the art by up to 60%.

Consumer Data Protection In Electronic Transaction Practices In E-Commerce

Online transactions provide huge benefits for buyers and sellers. For buyers, the convenience of shopping without having to go to a physical store is the main benefit. They can compare prices and products from various sellers, saving time and transportation costs. For sellers, online transactions open up wider market opportunities, increase product exposure, and increase sales and distribution efficiency. However, the risk of personal data leakage always lurks in online transactions. This issue is serious and can compromise consumers' privacy and information security. Data leaks can occur when personal information such as names, addresses, telephone numbers, emails, credit card details and other personal data falls into unauthorized hands. The research aims to explore the protection of consumers' personal data and examine the responsibility of marketplaces in dealing with data leaks. The author uses a normative juridical approach, which includes primary and secondary legal analysis, as well as descriptive analysis to summarize applicable regulations and legal theory related to practice. The results show that Indonesia has Law Number 27 of 2022 concerning Protection of Consumer Personal Data to protect personal data. Marketplaces that violate these rules may be subject to administrative sanctions in accordance with applicable regulations, while consumers have the right to file a lawsuit for marketplace negligence in accordance with Article 1366 of the Civil Code.

Deep learning-based authentication for insider threat detection in critical infrastructure

In today’s cyber environment, threats such as data breaches, cyberattacks, and unauthorized access threaten national security, critical infrastructure, and financial stability. This research addresses the challenging task of protecting critical infrastructure from insider threats because of the high level of trust and access these individuals typically receive. Insiders may obtain a system administrator’s password through close observation or by deploying software to gather the information. To solve this issue, an innovative artificial intelligence-based methodology is proposed to identify a user by their password’s keystroke dynamics. This paper also introduces a new Gabor Filter Matrix Transformation method to transform numerical values into images by revealing the behavioral pattern of password typing. A siamese neural network (SNN) with the branches of convolutional neural networks is utilized for image comparison, aiming to detect unauthorized attempts to access critical infrastructure systems. The network analyzes the unique features of a user’s password timestamps transformed into images and compares them with previously submitted user passwords. The obtained results indicate that transforming the numerical values of keystroke dynamics into images and training an SNN leads to a lower equal error rate (EER) and higher user authentication accuracy than those previously reported in other studies. The methodology is validated on publicly available keystroke dynamics collections, the CMU and GREYC-NISLAB datasets, which collectively comprise over 30,000 password samples. It achieves the lowest EER value of 0.04545 compared to state-of-the-art methods for transforming non-image data into images. The paper concludes with a discussion of findings and potential future directions.

Multi-objective evolutionary neural architecture search for network intrusion detection

Network Intrusion Detection (NID) becomes significantly important for protecting the security of information systems, as the frequency and complexity of network attacks are increasing with the rapid development of the Internet. Recent research studies have proposed various neural network models for NID, but they need to manually design the network architectures based on expert knowledge, which is very time-consuming. To solve this problem, this paper proposes a Multi-objective Evolutionary Neural Architecture Search (MENAS) method, which can automatically design neural network models for NID. First, a comprehensive search space is designed and then a weight-sharing mechanism is used to construct a supernet for NID, allowing each subnet to inherit weights from the supernet for direct performance evaluation. Subsequently, the subnets are encoded as chromosomes for multi-objective evolutionary search, which simultaneously optimizes two objectives: enhancing the model’s detection performance and reducing its complexity. To improve the search capability, a path-based crossover method is designed, which can iteratively refine the subnets’ architectures by simultaneously optimizing their accuracy and complexity for NID. At last, our MENAS method has been validated through extensive experiments on three well-known NID datasets: NSL-KDD, UNSW-NB15, and CICIDS2017. The experiments show that our MENAS method obtains an average 1.45% improvement on accuracy and an average 68.70% reduction on floating-point operations through multi-objective optimization process on six scenarios, which outperforms some state-of-the-art NID methods.

Policy and Infrastructure for Data Privacy and Data Protection in South Indian States: Issues and Prospects

In the 21st century, data privacy, data protection and cybersecurity have become challenging issues with increasing cases of data breaches and data disasters. Data privacy has emerged as one of the challenging issues after numerous cases of data breaches and manipulations in many departments, agencies, bodies and organisations in every country. A well-designed data framework may enable not just secure data privacy/protection but also enable users’ control over data through a protective and safe system to share data across organisations, leading to individual empowerment and well-being. In India, privacy still remains a complex area, which has affected data safety and protection. A number of government websites clearly mention that data will not be shared with anyone until and unless for the purpose enshrined so far. In India, there have been various policies, guidelines, strategies and initiatives for cybersecurity, data privacy, data protection and data measures, including the Digital Personal Data Protection Act, 2023; the National Cyber Security Policy, 2013; the Information Technology Act 2000; the Information Technology (Amendment) Act, 2008; the Information Technology Rules, 2011 and so on. This article tries to examine policies/regulations for data privacy, data protection and data safety in South Indian states.