Security Enhancement Research Articles

ROSIC: Enhancing secure and accessible robot control through open‐source instant messaging platforms

AbstractEnsuring secure communication and seamless accessibility remains a primary challenge in controlling robots remotely. The authors propose a novel approach that leverages open‐source instant messaging platforms to overcome the complexities and reduce costs associated with implementing a secure and user‐centred communication system for remote robot control named Robot Control System using Instant Communication (ROSIC). By leveraging features, such as real‐time messaging, group chats, end‐to‐end encryption and cross‐platform support inherent in the majority of instant messenger platforms, we have developed middleware that establishes a secure and efficient communication system over the Internet. By using instant messaging as the communication interface between users and robots, ROSIC caters to non‐technical users, making it easier for them to control robots. The architecture of ROSIC enables various scenarios for robot control, including one user controlling multiple robots, multiple users controlling one robot, multiple robots controlled by multiple users, and one user controlling one robot. Furthermore, ROSIC facilitates the interaction of multiple robots, enabling them to interoperate and function collaboratively as a swarm system by providing a unified communication platform that allows for seamless exchange of data and commands. Telegram was specifically chosen as the instant messaging platform by the authors due to its open‐source nature, robust encryption, compatibility across multiple platforms and interactive communication capabilities through channels and groups. Notably, the ROSIC is designed to communicate effectively with robot operating system (ROS)‐based robots to enhance our ability to control them remotely.

Versatile Machine Learning-Based Authentications by Using Enhanced Time-Sliced Electrocardiograms

This paper addresses the enhancement of modern security through the integration of electrocardiograms (ECGs) into biometric authentication systems. As technology advances, the demand for reliable identity authentication systems has grown, given the rise in breaches associated with traditional techniques that rely on unique biological and behavioral traits. These techniques are emerging as more reliable alternatives. Among the biological features used for authentication, ECGs offer unique advantages, including resistance to forgery, real-time detection, and continuous identification ability. A key contribution of this work is the introduction of a variant of the ECG time-slicing technique that outperforms existing ECG-based authentication methods. By leveraging machine learning algorithms and tailor-made compact data learning techniques, this research presents a more robust, reliable biometric authentication system. The findings could lead to significant advancements in network information security, with potential applications across various internet and mobile services.

Home network security enhancement

Home network security enhancement

Enhancing secure multi-group data sharing through integration of IPFS and hyperledger fabric.

Data sharing is increasingly important across various industries. However, issues such as data integrity verification during sharing, encryption key leakage, and difficulty sharing data between different user groups have been identified. To address these challenges, this study proposes a multi-group data sharing network model based on Consortium Blockchain and IPFS for P2P sharing. This model uses a dynamic key encryption algorithm to provide secure data sharing, avoiding the problems associated with existing data transmission techniques such as key cracking or data leakage due to low security and reliability. Additionally, the model establishes an IPFS network for users within the group, allowing for the generation of data probes to verify data integrity, and the use of the Fabric network to record log information and probe data related to data operations and encryption. Data owners retain full control over access to their data to ensure privacy and security. The experimental results show that the system proposed in this study has wide applicability.

Enhancing security in Wireless Body Area Networks (WBANs) with ECC-based Diffie-Hellman Key Exchange algorithm (ECDH).

Wireless Body Area Networks (WBANs) are integral to modern healthcare systems, providing continuous health monitoring and real-time data transmission. The sensitivity of medical data being transmitted makes security a significant concern in WBANs. This study explores the application of the Elliptic Curve Cryptography (ECC)-based Diffie-Hellman Key Exchange (ECDH) algorithm to enhance security within WBANs. The study investigates the suitability of ECC for this context and evaluates the performance and security implications of implementing ECDH in WBANs. The findings reveal that ECDH provides a robust and computationally efficient solution for secure key exchange in WBANs, addressing inherent vulnerabilities. The adoption of ECC-based ECDH is poised to bolster data confidentiality and integrity in WBANs, promoting trust and widespread use of these networks in healthcare applications. This research contributes to the growing body of knowledge regarding security measures in WBANs and opens new avenues for the secure transmission of sensitive medical information.

Enhancing Security and Reliability in Industrial IoT Networks through Machine Learning

Industrial Internet of Things (IIoT) networks are very important to modern manufacturing because they allow processes to be monitored, controlled, and improved in real time. IoT systems are linked to each other, which makes them vulnerable to cyberattacks, system breakdowns, and communication problems. This makes them less reliable and secure. To deal with these problems, we need advanced technologies that can find problems before they happen, reduce risks, and keep processes running smoothly. In this paper, we suggest a new way to make IIoT networks safer and more reliable by using machine learning methods together. Our system uses machine learning techniques to look at network traffic trends, spot strange behaviors, and find possible security holes in real time, using the huge amounts of data that IIoT devices produce. Our system can successfully find and stop a wide range of cyberattacks, such as hacking attempts, malware infections, and denial-of-service (DoS) attacks, because it is always learning from past data and changing to new threats. We also use machine learning models to predict when systems might fail or perform worse, so we can do preventative maintenance and keep downtime to a minimum.To prove that our method worked, we did a lot of tests using real-world IIoT datasets and checked how well our system worked by looking at how accurate it was at finding things, how often it gave false positives, and how fast it responded. These results show that our approach based on machine learning makes IIoT networks much safer and more reliable than standard rule-based approaches. In addition, our framework is strong against new and unknown threats, which shows that it could be used in a wide range of business settings.Overall, the paper research shows that machine learning has a lot of potential to make IIoT networks more reliable and to make sure that processes in industrial settings run smoothly and safely

Enhancing Secure and Reliable Data Transfer through Robust Integrity

Cloud computing has emerged as a highly efficient platform that allows multiple users to access various services through virtualization on a shared physical network. The participants in a Cloud Computing (CC) environment include Cloud Service Providers (CSP), Consumers, Brokers, and Auditors. The advantages of cloud storage, such as universal network access, convenience, and scalability, have led to data owners preferring to store their data on remote servers. However, the transfer of outsourced data has become a critical requirement for cloud users due to the availability of different cloud storage services with varying quality of services.
 One major challenge in this context is ensuring the security of secret keys and data integrity. There is no guarantee of data integrity when storing data on an untrusted cloud server. To address this issue, this paper proposes a secure and efficient data integrity verification scheme for cloud storage services. The scheme utilizes a key-homomorphic cryptographic primitive to reduce system complexity and eliminate the need for a public key authentication framework based on a public key infrastructure (PKI) in the data integrity checking protocol. By employing this approach, the proposed method ensures the integrity of remote data stored on cloud servers. Through security analysis and empirical evaluation, it is demonstrated that our scheme is both practical and effective for securely sharing records with multiple owners in cloud computing.

Enhancing secure financial transactions through the synergy of blockchain and artificial intelligence

Blockchain and artificial intelligence are innovative technologies that can securely process and share data across unreliable networks. Due to data leakage from user information, it is critical to keep the data confidential and completely protected because criminals are looking for this information to attack the system or steal information in the banking sector. To address this issue, this paper suggests an Integrated Blockchain and Artificial intelligence (IBAI) Framework for secure financial transactions. A blockchain can store every customer's data in one place, while AI-driven algorithms can speedily examine that data and make an unbiased decision. The rising blockchain technology provides a decentralized architecture that enables the secure sharing of data and resources to the different networks and is promoted for removing centralized control and resolving the problems of AI. When suspicious behaviour happens, alerts can be triggered to avert theft. Furthermore, registration protocols based on AI are utilized to keep this data in comprehensive security. The numerical results show that the suggested IBAI model enhances the suspicious behaviour detection ratio and increases accuracy up to 98% compared to other models.

Physical‐layer security enhancement in wireless sensor networks through artificial noise optimization

AbstractEnsuring network security is of utmost importance, especially in wireless sensor networks (WSNs), where the confidentiality of data is at risk due to eavesdropping. This paper introduces an artificial‐noise‐aided secure communication scheme for WSNs. This scheme comprises numerous sensor nodes, the sink, a friendly jammer, and an eavesdropper. The primary aim is to enhance the secrecy rate of the network by optimizing the jamming power. The friendly jammer plays a crucial role in degrading the wiretap channel between the sensor nodes and the eavesdropper. The optimization process revolves around maximizing the secrecy rate, while carefully managing the jamming power to prevent any negative impact on the main transmission link between the sink node and the sensor nodes. Due to the nonconvexity of the secrecy rate problem, the convex approximation‐based algorithm is recommened to get a safe convex approximation of the original solution. To address this optimization problem, an iterative algorithm based on quadratic transformation is proposed. Afterwards, the optimum jamming power to achieve high security is obtained. The simulation outcomes underscore the dual impact of the jammer in WSNs. The jammer diminishes the eavesdropper channel. Unfortunately, it introduces interference that consequently affects the security enhancement. In response, we concentrate on the optimization of jamming power. The proposed scheme consistently elevates secrecy rates across a spectrum of eavesdropper positions, adeptly addressing worst‐case scenarios. Notably, the algorithm showcases resilience across diverse node distributions.

FINANCIAL TECHNOLOGY INNOVATION AND BANKING INDUSTRY TRANSFORMATION

This research aims to innovate in technology, finance, and transformation in the banking industry. The research method used in this research is qualitative. Qualitative research is a research method used to understand social phenomena in depth. This type of research is library research, which researchers carry out by collecting, studying, and analyzing references or sources. obtained in writing or written form, such as books, journals, articles, documents, and other sources of information that are significant to the topic or title being researched. The research results show that the impact of development technology on industry banking in Indonesia is positive, namely: Improvement Efficiency and Productivity: Adoption technology has brought significant enhancements to efficiency bank operations, with automation of processes and usage intelligence artificial to reduce costs and improve productivity. Enhancement Experience Customer: Innovation technology has allowed banks to increase customer experience through applications in digital banking, customer chatbot-based services, and self-service platforms. Enhancement Security and Management Risk: Although there are worries related to data security, development technology has the potential to repair significant system security and management risks. With the use of data encryption and analysis behavior, users can identify and respond to threats to security more quickly. Innovation Products and Services: Technology has pushed the development of new products and services in the banking industry, including robo-advisors, peer-to-peer lending platforms, and applications for mobile payments, which deliver a markup for customers and help banks expand their market share. Enhancement Efficiency and Transparency Management: System Management Integrated information and analytical dashboards have increased efficiency and transparency in bank management.

Chaotic discrete Fresnel transform for secure OFDM-PONs

A chaotic discrete Fresnel transform (DFnT) precoding scheme is proposed for physical-layer security enhancement in orthogonal frequency division multiplexing passive optical networks (OFDM-PONs). A multi-fold OFDM data encryption is applied in terms of the chaotic scale factor, and chaotic row/column permutations in the DFnT matrix, and the chaos sensitivity creates a huge key space of 10138. The peak-to-average power ratio (PAPR) performances of OFDM signals associated with a scale factor in DFnT are fully analyzed. A 38.2 Gb/s OFDM signal transmission with chaotic DFnT is successfully demonstrated over a 20 km standard single-mode fiber (SSMF), where the gain of receiver sensitivity is ∼2 dB (BER @10−3), compared with OFDM without precoding.

Enhancing Security and Confidentiality in Decentralized Payment System Based on Blockchain Technology

The advent of blockchain technology (BC), has revolutionized the financial landscape with its decentralized, immutable, and verifiable characteristics. Blockchain technology can be deployed in many applications, however the Decentralized Payment System (DPS) can be considered as the most widely used application among all others. The public nature of early decentralized payment systems (e.g. bitcoin) has led to privacy challenges, making them susceptible to cybercriminal activities such as ransomware attacks and illegal financial transactions. This research investigates various decentralized payment systems adopted in blockchain technology and outlines the security requirements necessary to safeguard user transactions. The objectives of this study include examining payment transactions in decentralized systems, identifying potential threats, proposing improved and efficient security solutions, and assessing personal data sharing confidentiality.

The role of technological modernization in Brazil's border security: a study on the Integrated Border Monitoring System (SISFRON)

The central purpose of this study is to conduct a scientific analysis of the influence of technological innovations on crime mitigation and the enhancement of public security in the border zone of Brazil. This examination aims to understand how the adoption of cutting-edge technologies can contribute to reducing criminal activity and strengthening security in the country's border areas. This study is of great relevance as it seeks to assess the importance of implementing the Integrated Border Monitoring System (SISFRON) as a preventive and repressive measure against crimes in the border regions between Brazil and other Latin American countries. SISFRON is essential as it integrates the Armed Forces, government agencies, and federative states, playing a crucial role in National Defense. The methodology employed in this work is based on a deductive approach, involving qualitative bibliographic and documentary research. The analysis of institutional documents, mapping of key actors from institutions involved in border protection, and investigating their role in promoting public security and defending Brazilian borders are fundamental steps in this research. By concluding this study, it is expected to confirm that SISFRON emerges as a significant tool of the National Defense Policy. It not only focuses on internal security and strengthening territorial defense in border areas but also serves as a link for cooperation between neighboring countries, enabling the sharing of information and joint actions in combating various forms of transboundary illicit activities.

Enhancing Security in Visible Light Communication: A Tabu-Search-Based Method for Transmitter Selection.

In this paper, we explore the secrecy performance of a visible light communication (VLC) system consisting of distributed light-emitting diodes (LEDs) and multiple users (UEs) randomly positioned within an indoor environment while considering the presence of an eavesdropper. To enhance the confidentiality of the system, we formulate a problem of maximizing the sum secrecy rate for UEs by searching for an optimal LED for each UE. Due to the non-convex and non-continuous nature of this security maximization problem, we propose an LED selection algorithm based on tabu search to avoid getting trapped in local optima and expedite the search process by managing trial vectors from previous iterations. Moreover, we introduce three LED selection strategies with a low computational complexity. The simulation results demonstrate that the proposed algorithm achieves a secrecy performance very close to the global optimal value, with a gap of less than 1%. Additionally, the proposed strategies exhibit a performance gap of 28% compared to the global optimal.

Maritime Cooperation in the Indian Ocean Region: India's Diplomatic Engagement with its Neighbours

This review paper delves into India's diplomatic efforts within the Indian Ocean Region (IOR), particularly focusing on its maritime engagement with neighbouring countries. By examining both bilateral and multilateral initiatives, the paper aims to uncover how these endeavours have contributed to cooperation, security enhancement, and economic development in the region. Through case studies and empirical analysis, it scrutinizes the trajectory of India's maritime interactions with nations like Sri Lanka, Maldives, Bangladesh, and Myanmar. Moreover, it sheds light on crucial areas of collaboration, hurdles faced, and prospects for deeper cooperation. This comprehensive analysis offers valuable insights into India's evolving role and relationships in the IOR, providing a nuanced understanding of its maritime diplomacy.

Enhancing security of cloud using static IP techniques

The user authentication and access control procedures for data stored on a cloud server encountered numerous security risks and concerns. Because critical company data should only be accessible by authorized workers, enhancing cloud security with static IP approaches is more beneficial. Even though the cloud providers maintain many security mechanisms, still the level of security should be raised by them due to lot of intruders who want to break their security mechanism. An encryption method is frequently used in most servers’ security mechanisms. In this security mechanism, data in the cloud server may be stolen and misused. Security mechanisms using static IP addresses are another method for data security in cloud security. This paper explains that the static IP address security mechanism is better and more goal-oriented than that of earlier security systems.

Enhancement of Security and Privacy of Smart Contracts in Blockchain

Smart contracts, leveraging the power of blockchain technology, have revolutionized the execution and enforcement of agreements. However, their adoption also brings forth substantial challenges in terms of security and privacy. This research paper aims to identify the recent areas of focus and provide a comprehensive perspective on blockchain applications and smart contracts, highlighting their main issues and corresponding solutions. Furthermore, it seeks to address the gaps in current research and outline future avenues of investigation. The primary objective is to assess the security and privacy concerns associated with smart contracts in blockchain and propose effective measures to enhance their robustness. By conducting a thorough analysis of vulnerabilities, attack vectors, and privacy considerations, this study offers valuable insights into the risks involved in smart contracts. It also puts forth practical solutions and best practices to mitigate these risks, ensuring a more secure and privacy-preserving environment for the deployment and execution of smart contracts.

A Novel Hybrid Feature Selection with Cascaded LSTM: Enhancing Security in IoT Networks

The rapid growth of the Internet of Things (IoT) has created a situation where a huge amount of sensitive data is constantly being created and sent through many devices, making data security a top priority. In the complex network of IoT, detecting intrusions becomes a key part of strengthening security. Since IoT environments can be easily affected by a wide range of cyber threats, intrusion detection systems (IDS) are crucial for quickly finding and dealing with potential intrusions as they happen. IDS datasets can have a wide range of features, from just a few to several hundreds or even thousands. Managing such large datasets is a big challenge, requiring a lot of computer power and leading to long processing times. To build an efficient IDS, this article introduces a combined feature selection strategy using recursive feature elimination and information gain. Then, a cascaded long–short-term memory is used to improve attack classifications. This method achieved an accuracy of 98.96% and 99.30% on the NSL-KDD and UNSW-NB15 datasets, respectively, for performing binary classification. This research provides a practical strategy for improving the effectiveness and accuracy of intrusion detection in IoT networks.

Enhancing Security for Text Message Cryptography based on Basis Vectors

Cryptography is the practice and study of hiding information from all but those with the means or key to decode the message. Also the area of cryptography employs many different means of transforming normal data in to unreadable form. This paper’s aim of the study was how to keep the digital data secretly and sent it secretly through insecure channels based on basis vectors, that an activity builds around one of the techniques that illustrate an application of a set of basis vectors called matrix to cryptography The method involves two matrices of which one is used to encode the encoding matrix and the other one to decode the decoding matrix. The characters, in the original message or stream are assigned numerical values and the matrix must be row reduce echelon form for use in decoding. The proposed method is very simple in its principle and has great potential to be applied to other situations where the exchange of message is done confidentially.

Enhancing Security and Trust in Internet of Things through Meshtastic Protocol Utilising Low-Range Technology

The rapid proliferation of Internet of Things (IoT) devices has raised significant concerns regarding the trustworthiness of IoT devices, which is becoming a crucial aspect of our daily lives. In this paper, we deal with this important aspect by taking into account Meshtastic, a dynamic mesh networking protocol that offers robustness and adaptability, important characteristics for the dynamic and heterogeneous IoT environment. LoRaWAN (Low-Range Wide Area Network), a low-power, long-range wireless communication standard, introduces energy efficiency and extends the reach of IoT networks, enabling secure communication over extended distances. To improve the trustworthiness of IoT devices, we present an integrated approach that leverages the strengths of Meshstastic’s dynamic mesh networking capabilities and LoRa’s low-power, long-range communication, along with the integration of a reputation model specifically designed for IoT. We evaluated the performance of the proposed solution through several simulations and real-world experiments. The results show that the devices’ measured values of trust reflect the real behaviour of the devices. These findings underscore the viability and applicability of the Meshtastic protocol utilising LoRa technology as a pivotal step towards establishing resilient and trustworthy IoT infrastructures in the face of evolving security challenges.