Security Enhancement Research Articles

Network security enhancement with advanced techniques in network scanning.

This article details the development and evaluation of an advanced network scanning framework that uses Scapy for packet manipulation and Tkinter for the GUI. The framework aims to improve detection accuracy, resource efficiency, and user interface usability, addressing the shortcomings of traditional methods. Key components include a Network Scanner, Packet Handler, Security Measures, and a GUI, all designed for comprehensive network security. Tested in a controlled environment, the framework achieved a 100% detection rate, surpassing traditional methods, and demonstrated resource efficiency with a 20% reduction in CPU usage and a 60 MB decrease in memory usage. The analysis showed network diversity and low utilization, suggesting capacity for future expansion. The framework's GUI effectively displayed scan results, enhancing usability. The novelty of this research lies in its integration of optimized ARP packet handling with a user-friendly interface, ensuring real-time threat detection and scalability. The study contributes to the advancement of network scanning techniques and proposes future enhancements like machine learning integration for better threat detection.

DeepOpt: a deep learning optimized privacy preservation framework for cyber-physical systems

Privacy preservation and security enhancement are the key components of any network architecture due to advanced attack procedures. Cyber-Physical Systems (CPS) also need a mitigation and prevention strategy to deal with cyber threats. The existing approaches majorly deal with attack detection and focus on one or two attacks at a time. With this focus and demand of the CPS, this work proposes a deep learning optimized privacy preservation framework called DeepOpt. This proposed framework prevents the network from attackers and maintains security by classifying multiple attackers simultaneously using deep learning architecture. The proposed framework initializes privacy preservation using the trust-based approach and a hybrid optimization algorithm. In this, the network is divided into different zones, and each zone is secured using trust parameters with additional verification by secure hash function. The hybrid optimization selects the communication path using trust and energy that returns the attack-free path. This proposed architecture is simulated over different network scenarios with or without attacker nodes, and their traces are labeled to train the proposed deep convolutional neural network architecture. Finally, these models are integrated, and their performance is analyzed in different network scenarios and the presence of five different attackers such as blackhole, wormhole, man-in-the-middle attack, spoofing, and distributed denial of service. The simulation results, with improvement in detection accuracy, packet delivery ratio, and other performance factors, indicate the effectiveness of the proposed framework for both prevention and mitigation. Hence, this overall architecture preserves the privacy of CPS even in multifarious dynamic network scenarios.

Developing a security enhancement for healthcare applications using blockchain-based firefly-optimized elliptic curve digital signature algorithm

Developing a security enhancement for healthcare applications using blockchain-based firefly-optimized elliptic curve digital signature algorithm

Noncooperative game-theoretic model for security enhancement in cloud gaming environments

Noncooperative game-theoretic model for security enhancement in cloud gaming environments

Integrating Blockchain in Scheduling: A Comprehensive Review of Current Trends and Future Directions

This review paper offers a comprehensive exploration of blockchain technology's integration in scheduling activities across various industries. It systematically gathered and analyzed literature, identifying 133 relevant documents through rigorous screening from 2017 till 2023. The bibliometric analysis, including co-authorship and co-occurrence maps, revealed key trends, influential authors, and emerging research areas. Content analysis focused on blockchain applications in service optimization, system and resource planning, management, project and production planning, energy optimization, and security enhancement. The research acknowledged significant advancements in these areas, particularly in terms of improved efficiency, transparency, and security. The findings underscore the potential of blockchain in revolutionizing scheduling and management processes across different sectors, highlighting the need for further exploration of its applications and implications in various professional contexts. The paper aims to bridge the gap in the literature by presenting a holistic view of blockchain's role in advancing scheduling methods, driving innovation, and fostering more robust, efficient, and transparent scheduling systems across industries.

Enhancing Security, Authentication and Traceability for Non Standard Internet of Things (IoT) Enabled Devices by Implementing Mandatory IoT Enabled Devices Having Hardware and Software Authentication Cum Registration Mechanism Before Their Usage to Every IoT Enabled Devices Manufacturer Cum Supplier as Well as User

Internet of things (IoT) [1] has emerged a new field of huge numbers of interconnect devices in term of connecting various gadgets with devices together for sharing data between them as well as working together to form a cluster of sensors with actuators for better monitoring and controlling of various processes of agriculture and industrial automation. A differential non standard approach by various vendors and developers of IoT based application has led to impose great challenge and threat in terms of data security and integrity as well as cost on traceability for Government agencies.

Enhancing security and transparency in telecommunications through blockchain technology implementation

Enhancing security and transparency in telecommunications through blockchain technology implementation

Enhancing security for smart healthcare in wireless body area networks using a novel adversarial detection using ACR BiLSTM with multi-batch stochastic gradient descent

Enhancing security for smart healthcare in wireless body area networks using a novel adversarial detection using ACR BiLSTM with multi-batch stochastic gradient descent

Face privacy protection scheme by security-enhanced encryption structure and nonlinear dynamics

This article proposes a secure communication enhancement scheme based on face detection and chaotic partition permutation. The scheme uses edge detection technology to detect facial information, which is then used as an encryption object. The hash value of the plaintext image is extracted as the secret key to the chaotic sequence generated by the chaotic system. Then a series of encryption operations are performed on the face image to obtain the final ciphertext image. In this article, two chaotic systems are used to generate pseudo-random chaotic sequences for different encryption steps. The initial key is computed by combining the hash function of the image and external parameters. The experimental results and security analysis show that the algorithm has excellent encryption effectiveness and security performance against various typical attacks.

Retraction Note: Enhancement of security using optimized DoS (denial-of-service) detection algorithm for wireless sensor network

Retraction Note: Enhancement of security using optimized DoS (denial-of-service) detection algorithm for wireless sensor network

The Enablers and Interdependencies of Blockchain Technology in Supply Chain Management: Evidence From Luxury Products

ABSTRACTBlockchain is a technology that combines a set of properties to guarantee network security, transparency, and visibility, including a decentralized structure, distributed notes and storage mechanism, consensus algorithm, intelligent contracts, and asymmetric encryption. Supply chain management activities, including supply chain management provenance, business process reengineering, and security enhancement, have enormous potential to be transformed by blockchain. This research uses the integrated interpretive structural modeling–cross‐impact matrix multiplication applied to classification (ISM‐MICMAC) technique to ascertain the hierarchical relationships and to comprehend the severity of interrelationships among various components in tackling our research questions. In MICMAC analysis, the enablers were classified into four categories based on their dependence and driving powers. The combined ISM‐MICMAC methodology employed for this study relies on experts’ individual evaluations and subjective assessments. Therefore, even with extreme caution, it is impossible to guarantee that the results are entirely devoid of personal biases. To further validate the linkages discovered in this study, we suggest employing more multiple‐criteria decision‐making (MCDM) methodologies and comparing the results with those of our research. Another method for confirming the results of the current study is to use an empirical research design based on survey methods.

Concurrent performance and security enhancement of ultrahigh-order DSM signals via null subcarriers.

In this paper, we propose leveraging null subcarriers in discrete multi-tone modulation (DMT) to process the DMT signal in both time and frequency domains. Additionally, we employ discrete memory enhanced chaos (DMEC) to scramble the signal in the frequency domain, thereby achieving physical layer signal encryption while ensuring a more uniform power distribution in the time-domain waveform. In our experimental demonstration, we achieved high-security transmission of a DSM-based 65536-QAM signal at a data rate of 16.01 Gb/s over a 25 km single-mode fiber (SMF) in an intensity-modulation direct-detection (IMDD) system. Additionally, in the transmission experiments for 13684-QAM and 65536-QAM signals, the proposed method demonstrated a receiver sensitivity gain of over 0.5 dB compared to the traditional DSM-based ultrahigh-order transmission.

A physical layer security enhancement scheme for CO-OFDM systems based on 3D Brownian motion and TZBN Joint Scramble

In this paper, we propose a scheme for enhancing the physical layer security and transmission performance of coherent optical orthogonal frequency division multiplexing (CO-OFDM) systems. The scheme for OFDM data first after 3-dimensional (3D) Brownian motion rotational permutation, and then according to the time zone and band number (TZBN) scrambling to achieve the effect of further data perturbation, which through the SHA-256 algorithm and 6-dimensional (6D) hyperchaotic processing to obtain the chaotic keys to enhance the security. After simulation experiment of 16QAM CO-OFDM security system transmitting over 80 km standard single-mode fiber (SSMF) at the symbol rate of 30 Gbaud, it is shown that the encryption scheme can guarantee that the authorized user receives valid data and the eavesdropper obtains the information with the BER of about 0.5. The key space of the scheme is about 2.88×10194, which can effectively resist attacks such as violent attacks and chosen plaintext attacks. And the peak-to-average power ratio (PAPR) can be reduced by about 1.09 dB while the encryption process.

Enhancing security in digitized healthcare system using blockchain technology.

In the modern medical industry, sensors and other medical electronic equipment such as ECG, thermometers, etc. that measure vitals like blood pressure, body temperature, heartbeat, and blood cells are used to record patient data. The information gathered helps to create a patient profile, which in turn helps doctors treat patients appropriately and helps users gain insights into their health. But, the information collected is regionally specific and not worldwide. Thus, this may create problems in transparency, integrity, and much more. The medical details of patients collected are stored in a simple database which appears to be a centralized application that is prone to be modified or hacked by intruders and also suffers from fault tolerance. This paper aims to solve this problem using Blockchain and Data Possession methodologies. Blockchain helps to store multiple copies of data in a decentralized fashion. Thus, a secured model could be implemented using blockchain in which the patient's data can be securely sent to any medical professional, thus promoting transparency, security, and integrity. Provable Data Possession helps the users to verify that their information is intact and has not been tampered. It can be achieved using cryptography which encrypts the information of the patients stored. This paper will be beneficial for the medical department and the public such that it will be beneficial in terms of transparency, scalability, and security.

Network and cybersecurity applications of defense in adversarial attacks: A state-of-the-art using machine learning and deep learning methods

Abstract This study aims to perform a thorough systematic review investigating and synthesizing existing research on defense strategies and methodologies in adversarial attacks using machine learning (ML) and deep learning methods. A methodology was conducted to guarantee a thorough literature analysis of the studies using sources such as ScienceDirect, Scopus, IEEE Xplore, and Web of Science. A question was shaped to retrieve articles published from 2019 to April 2024, which ultimately produced a total of 704 papers. A rigorous screening, deduplication, and matching of the inclusion and exclusion criteria were followed, and hence 42 studies were included in the quantitative synthesis. The considered papers were categorized into a coherent and systematic classification including three categories: security enhancement techniques, adversarial attack strategies and defense mechanisms, and innovative security mechanisms and solutions. In this article, we have presented a systematic and comprehensive analysis of earlier studies and opened the door to potential future studies by discussing in depth four challenges and motivations of adversarial attacks, while three recommendations have been discussed. A systematic science mapping analysis was also performed to reorganize and summarize the results of studies to address the issues of trustworthiness. Moreover, this research covers a large variety of network and cybersecurity applications of defense in adversarial attack subjects, including intrusion detection systems, anomaly detection, ML-based defenses, and cryptographic techniques. The relevant conclusions well demonstrate what have achieved in defense mechanisms against adversarial attacks. In addition, the analysis revealed a few emerging tendencies and deficiencies in the area to be remedied through better and more dependable mitigation methods against advanced persistent threats. The findings of this review have crucial implications for the community of researchers, practitioners, and policy makers in network and cybersecurity using artificial intelligence applications.

Adaptive Merkle trees for enhanced blockchain scalability

The scalability of blockchain technology remains a critical challenge, hindering its widespread adoption across various sectors. This study introduces an innovative approach to address this challenge by proposing the adaptive restructuring of Merkle trees, fundamental components of blockchain architecture responsible for ensuring data integrity and facilitating efficient verification processes. Unlike traditional static tree structures, our adaptive model dynamically adjusts the configuration of these trees based on usage patterns, significantly reducing the average path length required for verification and, consequently, the computational overhead associated with these processes. Through a comprehensive conceptual framework, we delineate the methodology for adaptive restructuring, encompassing both binary and non-binary tree configurations. This framework is validated through a series of detailed examples, demonstrating the practical feasibility and the efficiency gains achievable with our approach. To empirically assess the effectiveness of our proposed method, we conducted rigorous experiments using real-world data from the Ethereum blockchain. The results provide compelling evidence for the superiority of adaptive Merkle trees, with efficiency gains of up to 30% and higher observed during the initial stages of tree restructuring. Moreover, we present a comparative analysis with existing scalability solutions, highlighting the unique advantages of adaptive restructuring in terms of simplicity, security, and efficiency enhancement without introducing additional complexities or dependencies. This study’s implications extend beyond theoretical advancements, offering a scalable, secure, and efficient method for blockchain data verification that could facilitate broader adoption of blockchain technology in finance, supply chain management, and beyond. As the blockchain ecosystem continues to evolve, the principles, methodologies, and empirical findings outlined herein are poised to contribute significantly to its growth and maturity. Statement The findings of this article were presented at ETHDenver 2024 (https://www.ethdenver.com/), a leading innovation festival that champions the Web3 community’s role in shaping the future of blockchain technology. A video of our presentation can be found at https://www.youtube.com/watch?v=-jjYVCAQkNE. The original manuscript of this article is available on the preprint archive at https://arxiv.org/abs/2403.00406.

Survey on Neural Networks in Networking: Applications and Advancements

The integration of neural networks into networking has paved the way for substantial advancements in network performance, security, and management. This survey delves into the diverse applications of neural networks within the networking domain, highlighting their impact on traffic prediction, anomaly detection, network optimization, and security enhancements. By leveraging the inherent capabilities of neural networks to model complex and non-linear relationships, significant improvements in efficiency and reliability can be achieved. This survey provides a thorough examination of current methodologies, key advancements, and practical implementations, offering insights into the future potential of neural networks in the field of networking.

Advancing cryptography: a novel hybrid cipher design merging Feistel and SPN structures

In the dynamic field of cryptography, lightweight ciphers play a pivotal role in overcoming resource constraints in modern applications. This paper introduces a lightweight cryptographic algorithm by seamlessly merging the proven characteristics of the Feistel cipher CLEFIA with the advanced substitution-permutation network (SPN) framework of RECTANGLE for key generation. The algorithm incorporates a specially optimized feather S-box, balancing efficiency and security in both CLEFIA and RECTANGLE components. The RECTANGLE key generation, vital for the proposed lightweight technique, enhances overall cryptographic security and efficiency. Meticulous consideration of resource limitations maintains the algorithm's lightweight nature, making it well-suited for applications with restricted computational resources. To validate the efficacy of the lightweight algorithm, extensive evaluation on encrypted data is conducted using National Institute of Standards and Technology (NIST) tools, known for assessing cryptographic algorithm quality. Results reveal a high degree of randomness, indicative of robust resistance against cryptographic attacks. This manuscript provides a comprehensive examination of the lightweight algorithm, emphasizing key attributes, security enhancements, and successful integration of the optimized feather S-box. Rigorous testing, particularly NIST tool-based randomness analysis, offers empirical evidence of the algorithm's resilience against attacks, establishing its suitability for secure data encryption in resource-limited environments.<p> </p>

Enhancing security and privacy in educational environments: A secure grade distribution scheme with Moodle integration

Enhancing security and privacy in educational environments: A secure grade distribution scheme with Moodle integration

Community empowerment in handling stunting through technology supporting food security and health

The prevalence of stunting in Indonesia, particularly in Gunung Kidul district, remains high at 23.5% in 2022. The Kosabangsa approach, integrated into the Village Development Plan (RPJM Desa) for 2020-2026, serves as a primary strategy for community empowerment, especially in agriculture and livestock. The methods employed include socialization, training, and mentoring, with a focus on functional food processing and home-based food processing. The results of this activity indicate an increase in the knowledge of PKK groups regarding infant and young child feeding practices and nutritional aspects, as well as a strengthening of their capacity to market local food products. This community service concludes that the integration of technology and a holistic approach to food security plays a crucial role in addressing stunting. Consequently, this community service not only focuses on short-term solutions to stunting but also aims to create a sustainable impact through community empowerment and overall food security enhancement.