MODERN METHODS OF ENSURING INFORMATION PROTECTION IN CYBERSECURITY SYSTEMS USING ARTIFICIAL INTELLIGENCE AND BLOCKCHAIN TECHNOLOGY

As the reliance on networked devices and software applications grows, there is a need for robust, secure, and cost-effective software update management approaches. To facilitate safe software updates, this paper proposes a blockchain-enabled ecosystem that generates keys, payment, and QR codes for delivery and authorization. The proposed Cost-Efficiency, Scalable, Stable ecosystem was thoroughly analyzed and evaluated. It is shown that the key generation systems provide strong protection against software piracy, while the blockchain backbone prevents unwanted manipulation. Safe banking integration paves the way for smooth monetization while QR-code-based delivery makes it easier for users to engage while reducing errors and increasing adoption rates. While the individual formation of these towers has greatly improved the overall level of security, they have inherent disadvantages as structures. Simple, secure payment methods are often not enough, and CP- ABE can be to monitor and implemented quite difficult because of its complexity. To resolve these drawbacks, we proposed a new ecosystem, where a secure payment method is combines with the resilience of Advanced Encryption Standard (AES) cryprographic algorithm. Instead of relying on OTPs, our solution uses a unique approach of encrypting OTPs and showing them as QR codes. This innovative technique effectively minimizes the risk of third-party applications compromising payment transactions, ensuring a secure and successful currency transfer.

The article examines the methods of social engineering used by attackers to gain unauthorized access to confidential information and manipulate the behavior of victims. The main types of attacks, such as phishing, vishing, smishing, pretexting, spear-phishing and whaling, as well as their features, implementation mechanisms and methods of deceiving users, are considered. Particular attention is paid to the psychological aspects of social engineering, including the influence of fear, trust, urgency, social proof and cognitive biases on the decision-making process. Modern approaches to protection against social engineering attacks are outlined, which include a combination of technological and educational methods. Measures are proposed to increase the digital literacy of users, develop information security policies, use multi-factor authentication, user behavior analysis systems and artificial intelligence to detect threats. Particular attention is paid to the use of large language models to identify fraudulent schemes and automate cybersecurity. The results of the study indicate the need for a comprehensive approach to protection against social engineering attacks, which involves synergy between technological tools and the human factor. The proposed recommendations are aimed at minimizing risks and increasing the overall level of security in the digital environment.

A significant growth of the number of embedded systems connected to the cloud services in the recent years has brought numerous new applications, but also exposed the IoT ecosystem to new threats and challenges in the security domain. The well-known and widely used security protocols such as SSL and TLS address these challenges successfully. However, they were primarily designed to protect the web traffic in a context where the bandwidth usage and computational power are not a big issue. In a case where massive-scale IoT connectivity is a requirement, it is of critical importance to optimize the bandwidth usage, without compromising the overall level of security. Our solution is presented in this paper, where we combine a low-bandwidth NB-IoT communication module with a hardware security element, in order to achieve secure connectivity to the cloud services, while reducing the communication overhead and offloading the computationally difficult security algorithms from the host MCU. By implementing the most recent version of TLS (v1.3) with an ECC-based key exchange scheme, we achieve 60% of reduction in the communication overhead during the handshaking phase in comparison to the widely used RSA-based TLS implementations.

Many credible threats against hazardous materials (hazmat) transportation exist in the United States. This paper outlines the overall regulatory approach to hazmat transportation security by commercial motor carriers in the United States and focuses on the well-received training and outreach efforts that have been put in place to improve the overall level of security in the United States. In the post-September 11, 2001 (9/11), environment, safety-oriented agencies are being asked to accommodate a new role in the security arena. Many regulations affect the highway transportation of hazmat. The Federal Motor Carrier Safety Administration is primarily concerned with the enforcement of safety regulations, and the Research and Special Programs Administration (RSPA) regulates hazmat transportation across all modes. Recently, RSPA implemented new requirements for security assessments and security plans. Because many motor carriers previously did not have much experience with security issues, the guidance provide...

The article discusses the possibilities of using deep autoencoders in the field of information hiding (steganography). It is shown that the combination of steganography methods with deep learning makes it possible to improve system reliability and increase the bandwidth of the hidden data transmission channel. The purpose of this paper is to analyse modern approaches to the use of deep autoencoders in steganography, to determine their main advantages and disadvantages, and to formulate promising directions for further research. The development of deep learning opens up broad prospects for enhancing classical steganographic approaches. The use of deep neural networks allows increasing the bandwidth of the steganographic concealment channel, increase resistance to attacks, in particular, to compression, noise and other distortions and adapt to different types of media and specific security requirements will allow identifying new tools for optimising the process of information concealment. The scientific novelty lies in the combination of the ideas of steganography and deep auto-encoders, which makes it possible to obtain high quality recovery of hidden information with minimal visual, acoustic or other artefacts. A comparative review of modern autoencoder architectures is carried out, the principles of encoding and decoding are analysed, and the results of experimental studies demonstrating the effectiveness of the proposed approaches are summarised. The prospects for the development of this area in terms of security, efficiency and resistance to attacks are assessed through a detailed analysis of potential vulnerabilities and practical implementation scenarios. The results of the study indicate the significant potential of deep autoencoders in the field of information security, in particular for integration with steganographic methods. A number of recommendations for further improvement of the technology are proposed, including optimisation of the neural network architecture, expansion of the scope of applications, and consideration of ethical and legal aspects. The results of the study can be used in various areas of information security, including: digital watermarks for copyright protection; covert transmission of messages in communication channels resistant to interception; security of IoT devices, where it is important to minimise the amount of data and at the same time maintain secrecy. The methodology itself can be improved by integrating additional encryption at the level of the hidden message, which will increase the overall level of security.

The use of web applications endows production and business processes with new qualities, primarily such as: high business mobility; availability of services; continuity of business processes; scalability of the resulting effect, etc. Taking into account all these circumstances, the issue of ensuring information security during the processing and storage of personalized and "sensitive" corporate information retains the highest priority and is an extremely relevant area of activity, both for specialists of the relevant divisions of companies (information security departments and services), and for specialized specialists the field of information security. SQL injection is one of the most common techniques for hacking applications and websites that work with various databases. The attack, as a rule, is carried out based on the introduction of incorrect SQL operators into various types of requests, which allows the attacker to gain almost complete unauthorized access to the corresponding database, local files, as well as the possibility of remote execution of arbitrary operations on the server. Additionally, SQL attacks are often the result of unshielded input being passed to a site and used as part of a database query. The article provides a brief overview of known techniques for hacking applications and websites that work with databases. Based on the analysis of the main types of SQL attacks, the most serious types of threats were identified. Attention was drawn to the need for periodic testing and monitoring of websites, which is an actual means of protection against SQL injections. It has been noted that the best testing method is an attempt to subject the code to SQL injection. The considered methods of protection are able to increase the overall level of security of software products against attacks of the "SQL injection" type, ensure the correct operation of applications and the integrity of user data. The use of methods and means of testing web applications for resistance to denial-of-service attacks (DoS-attacks) is considered. The approach presented in the article will provide an opportunity to identify vulnerabilities and potential threats that can be used by attackers for unauthorized access to web resources.

The article considers a comprehensive approach to predicting the security index of local computer networks of organizations using modern econometric tools. The main attention is paid to the adaptation of classical production functions, in particular the Cobb–Douglas model, to the sphere of information security. The application of two modifications of the Cobb–Douglas production function — linear and nonlinear forms — is proposed to model the impact of key factors on the level of security of information infrastructure. The econometric models presented in the study take into account the impact of investments in cybersecurity, human capital, as well as technical and software protection tools used within organizational information systems. In particular, the following independent variables are included in the model: the amount of investment in security hardware (for example, firewalls, firewalls, routers with security functions), the cost of security software (intrusion detection and prevention systems, antivirus, SIEM), as well as parameters characterizing the human factor, in particular the number of information security specialists involved in the enterprise. The article provides a theoretical justification for the choice of the model, identifies the main endogenous and exogenous variables, analyzes their impact on the resulting variable, and also considers approaches to collecting, validating, and pre-processing empirical data. An econometric model was built based on real data collected from a number of organizations of various profiles, which made it possible to test the model in real conditions. The estimation of model parameters was carried out using multiple regression methods using the Statistica econometric analysis software. The modeling results confirmed the feasibility of using Cobb–Douglas functions in the field of cybersecurity and made it possible to combine the volumes of input data to model the future situation and determine the overall level of security of organizational networks. The conclusions obtained can be useful for forming an effective information security management policy, optimizing costs for cyber protection, as well as for strategic planning of investments in the development of information infrastructure. The proposed approach is flexible and scalable, which allows it to be adapted to the specifics of individual industries or organizations.

Блокчейн у системі охорони здоров’я: проблеми правового забезпечення

The concept of the Internet of Things (IoT) has received much attention over the last five years. It is predicted that the IoT will influence every aspect of our lifestyles in the near future. Wireless Sensor Networks are one of the key enablers of the operation of IoTs, allowing data to be collected from the surrounding environment. However, due to limited resources, nature of deployment and unattended operation, a WSN is vulnerable to various types of attack. Security is paramount for reliable and safe communication between IoT embedded devices, but it does, however, come at a cost to resources. Nodes are usually equipped with small batteries, which makes energy conservation crucial to IoT devices. Nevertheless, security cost in terms of energy consumption has not been studied sufficiently. Previous research has used a security specification of 802.15.4 for IoT applications, but the energy cost of each security level and the impact on quality of services (QoS) parameters remain unknown. This research focuses on the cost of security at the IoT media access control (MAC) layer. It begins by studying the energy consumption of IEEE 802.15.4 security levels, which is followed by an evaluation for the impact of security on data latency and throughput, and then presents the impact of transmission power on security overhead, and finally shows the effects of security on memory footprint. The results show that security overhead in terms of energy consumption with a payload of 24 bytes fluctuates between 31.5% at minimum level over non-secure packets and 60.4% at the top security level of 802.15.4 security specification. Also, it shows that security cost has less impact at longer packet lengths, and more with smaller packet size. In addition, the results depicts a significant impact on data latency and throughput. Overall, maximum authentication length decreases throughput by almost 53%, and encryption and authentication together by almost 62%.

High levels of scalability and reliability are needed to support the massive Internet-of-Things (IoT) services. In particular, blockchains can be effectively used to safely manage data from large-scale IoT networks. However, current blockchain systems have low transactions per second (TPS) rates and scalability limitations that make them unsuitable. To solve the above issues, this article proposes a deep Q network shard-based blockchain (DQNSB) scheme that dynamically finds the optimal throughput configuration. In this article, a novel analysis of sharded blockchain latency and security-level characterization is provided. Using the analysis equations, the DQNSB scheme estimates the level of maliciousness and adapts the blockchain parameters to enhance the security level considering the amount of malicious attacks on the consensus process. To achieve this purpose, deep reinforcement learning (DRL) agents are trained to find the optimal system parameters in response to the network status, and adaptively optimizes the system throughput and security level. The simulation results show that the proposed DQNSB scheme provides a much higher TPS than the existing DRL-enabled blockchain technology while maintaining a high security level.

SummaryThe block chain is the emerging domain that offers the features like shared, immutable, and decentralized dataset that saves the asset's history. Security and storage are the most challenging aspect. The classical techniques' issues and challenges are an encouragement for developing a fresh block chain‐assisted IPFS model with access control policies. Hence, this research paper introduces an effective access control and authentication based on an interplanetary file system (IPFS)‐based block chain and access control system for attaining secured transactions. The developed method comprises five entities, that is smart contract, data owner, data requester, block chain and IPFS along with eight phases, to be exact setup, user registration, initialization, storage and data encryption, authentication, testing, access control, and decryption phase, to provide better access control and storage capacity. The proposed IPFS‐based block chain and access control system offered effective storage and secure transactions in the block chain network. The highest detection rate (0.870), lowest communication overhead (0.766 MB), and highest privacy rate (0.719) are all achieved by the proposed IPFS‐based block chain and access control system.

Block chain technology has surfaced as a transformative influence, revolutionizing the verification and recording of transactions across industries. In to traditional data base systems, presenting a novel approach to database management. The objective is to enhance the trust, transparency, and security of data storage and transactions. Research in this field aims to strengthen data protection, reduce the risk of unauthorized access, and prevent data tampering. By providing a transparent and auditable record of transactions, block chain enhances trust among parties. This is particularly important in industries such as finance, where the integrity of transactions is paramount. The Weighted Sum Model (WSM) stands as a straightforward and widely utilized method of multi-criteria decision-making. It operates by multiplying assigned value of each attribute by the weight of importance designated by the decision maker. Subsequently, it entails aggregating these products across all criteria to compute evaluation scores for each alternative parameters considered in this study include Bitcoin, Ethereum, Biance Smart Chain, Hyperledger Fabric, and Corda, evaluated based on criteria such as Transaction Throughput (tps), Security Level (1-10), Energy Efficiency (kWh/tx), and Initial Setup Cost (USD). the term "Weighted Sum Model (WSM)" is correctly spelled, but the term "multi-Criteria" should be "multi-criteria" for proper phrasing. It involves assigning a value to each attribute, determined by the decision maker, which is then multiplied by a specified importance weight Bitcoin, Ethereum, Binance Smart Chain, Hyperledger Fabric and Corda. Transaction Throughput (tps), Security Level (1-10) Energy, Efficiency (kWh/tx) and Initial Setup Cost (USD). the ranking for blockchain-based database systems. Binance Smart Chain) got the top rank, whereas faculty strength Bitcoin) has a low ranking.

Blockchain (BC) technology has been established in 2009 by Nakamoto, using the Proof-of-Work (PoW) to reach consensus in public permissionless networks (Praveen et al., 2020). Since then, several consensus algorithms were proposed to provide equal (or higher) levels of security, democracy, and scalability, yet with lower levels of energy consumption. However, Nakamoto’s model (a.k.a. Bitcoin) still dominates as the most trusted model in the described sittings since alternative solutions might provide lower energy consumption and higher scalability, but they would always require deviating the system towards unrecommended centralization or lower levels of security. That is, Nakamoto’s model claims to tolerate (up to) < 50% of the network being controlled by a dishonest party (minority), which cannot be realized in alternative solutions without sacrificing the full decentralization property. In this paper, we investigate this tolerance claim, and we review several approaches that can be used to undermine/overpower PoW-based BCs, even with minority. We discuss those BCs taking Bitcoin as a representative application, where needed. However, the presented approaches can be applied in any PoW-based BC. Specifically, we technically discuss how a dishonest miner in minority, can take over the network using improved Brute-forcing, AI-assisted mining, Quantum Computing, Sharding, Partial Pre-imaging, Selfish mining, among other approaches. Our review serves as a needed collective technical reference (concluding more than 100 references), for practitioners and researchers, who either seek a reliable security implementation of PoW-based BC applications, or seek a comparison of PoW-based, against other BCs, in terms of adversary tolerance.

Using blockchain technology into telecom networks offers a great opportunity to improve network security. The decentralized and cryptographic aspects of blockchain technology result in a more effective and resilient system for safeguarding data, preserving confidentiality, reducing risks, and preventing unauthorized access. The paper has used the secondary data sources for data collection, making qualitative data analysis, driven with ethical standards for research. From the research, blockchain integration is revolutionizing security measures in the telecom network segment by addressing the conventional.

Most organizations today use two levels of security: Data security level (encryption) and Network security level (firewall) etc. Therefore, encryption plays a vital role in securing daily operations of organizations. Securing data at first level aims to hide data characteristics. So, different algorithms and methods are being researched and developed to cover this gap for securing different data types especially text messages and make it very difficult for unauthorized parties to break it. From these, the major no. of algorithms are specialized to encrypt English texts, but no one for Arabic language texts although there were many attempts to invent such algorithms. As a result, this research proposed a new encryption method to encrypt Arabic text by using the principle of integration to provide better security and increasing the complexity of guessing the correct keys and correct plain text. At the end, the results show that the new cryptosystem is inevitable to cryptanalysis attack.