Security Protocols Research Articles

Hong–Ou–Mandel Interference in Quantum Optics, Monogamy of Entanglement, Nonorthogonality, and Untrusted Nodes

Quantum key distribution systems with an untrusted intermediate node described by the so-called measurement device independent (MDI) protocol have been actively studied in the last decade. In early works, it was only argued why such a quantum key distribution system ensures the security of distributed keys mentioning that the security proof of the MDI protocol, which was not presented, is similar to that for the basic Bennett‒Brassard 84 (BB84) protocol. For this reason, despite the existing experimental implementations of the MDI quantum key distribution system, physical reasons for the protocol security are still questionable. Such quantum key distribution systems provide a common key between two network nodes connected through the intermediate untrusted node, which does not require protection of the equipment on it, and an eavesdropper sees the entire operation of the equipment, including the results of the operation of photodetectors. In this work, the MDI protocol has been analyzed. It has been shown that the physical reasons for the protocol security are based on fundamental properties such as the interference of photons from different sources, monogamy of entanglement, and nonorthogonality of states. A simple and explicit derivation is given showing the equivalence of the MDI and BB84 protocols and physical reasons for the identity of the corresponding expressions for the length of the final key.

An ECC based Secure Authentication Protocol for M2M Communication in Industrial IOT Edge Device

Abstract: Machine to machine communication in industrial IoT is becoming a common phenomenon now-a-days where all the machines like IoT edge devices communicate independently. It’s always been a challenging task to create a protocol for authentication that is both secure and efficient with regard to resources for use in industrial IoT environments' M2M communication. Here we present a safe and effective authentication method that makes use of elliptic curve cryptography (ECC). We demonstrated these vulnerabilities by testing the current protocol with various assaults, including SSL strip attack and MITM attack. To further evaluate the protocol's security against various IoT threats, we employed both official and informal approaches, such as the Scyther tool and BAN logic. Secured from a variety of IoT Threats, such as Man-in-the-middle (MITM) attacks, replay attacks, and impersonation attacks, our suggested protocol, absence of device anonymity, weak mutual authentication etc. but also suitable for resource constraint IoT edge devices like RFID devices, sensors, routers and gateways.

IoT Device Security and Network Protocols: A Survey on the Current Challenges, Vulnerabilities, and Countermeasures

IoT Device Security and Network Protocols: A Survey on the Current Challenges, Vulnerabilities, and Countermeasures

Malware Classification Using Dynamically Extracted API Call Embeddings

Malware classification stands as a crucial element in establishing robust computer security protocols, encompassing the segmentation of malware into discrete groupings. Recently, the emergence of machine learning has presented itself as an apt approach for addressing this challenge. Models can undergo training employing diverse malware attributes, such as opcodes and API calls, to distill valuable insights for effective classification. Within the realm of natural language processing, word embeddings assume a pivotal role by representing text in a manner that aligns closely with the proximity of similar words. These embeddings facilitate the quantification of word resemblances. This research embarks on a series of experiments that harness hybrid machine learning methodologies. We derive word vectors from dynamic API call logs associated with malware and integrate them as features in collaboration with diverse classifiers. Our methodology involves the utilization of Hidden Markov Models and Word2Vec to generate embeddings from API call logs. Additionally, we amalgamate renowned models like BERT and ELMo, noted for their capacity to yield contextualized embeddings. The resultant vectors are channeled into our classifiers, namely Support Vector Machines (SVMs), Random Forest (RF), k-Nearest Neighbors (kNNs), and Convolutional Neural Networks (CNNs). Through two distinct sets of experiments, our objective revolves around the classification of both malware families and categories. The outcomes achieved illuminate the efficacy of API call embeddings as a potent instrument in the domain of malware classification, particularly in the realm of identifying malware families. The best combination was RF and word embeddings generated by Word2Vec, ELMo, and BERT, achieving an accuracy between 0.91 and 0.93. This result underscores the potential of our approach in effectively classifying malware.

Polyalphabetic cipher cryptosystem application in making anti-hacker passwords learning management systems in junior high schools

Research on developing an anti-hacker network security system for the learning management system was conducted at SMP Plus Darus Salam. Learning activities at SMP Plus Darus Salam use LMS as a media, which is a program to maximize learning activities at school. its also has risks, it is related to the security of students data work, so network security is needed. Network security used within computer networks to facilitate secure communication in different network entities based on predefined security protocols. On the other hand, the Learning Management System (LMS) is a software platform designed for online educational activities, e-learning programs, and educational resources, simplifying student engagement in learning processes. This study employs applied research methodology, focusing on a single tribune graph, and involves a labeling process for vertices and edges to construct a graph tree, resulting in the generation of ciphertext. This research aims to develop anti-hacker passwords in the SMP Plus Darusalam LMS using a polyalphabetic cipher cryptosystem. The research utilizes a data set comprising 24 LMS account samples. Data collection techniques encompass observation, interviews, distribution of questionnaires, and documentation, while data analysis involves tabulation and the generation of ciphertext codes in the form of usernames and passwords. Based on the results, it shows a total score of 2,096 with an average of 91.13 or a security percentage of 91.13%, which shows that it is successful and useful in the SMP Plus Darus Salam learning management system (LMS) account. Thus, it can be concluded that the secjar (network security) network security efforts made were successful and provided significant benefits in securing learning management system (LMS) accounts at SMP Plus Darus Salam. Therefore, the researcher suggests to the readers to conduct research on the development of anti-hacker network security using Tribun graphs with ???? = 1 and ???? = 2.

New Technology in Marine Logistics Warehouse Management

The marine logistics industry faces challenges in improving efficiency, reducing operational costs, and supporting environmental sustainability. One crucial aspect that can be optimized is warehouse management, which is responsible for the storage, handling, and distribution of cargo. This research aims to explore the application of new technologies such as the Internet of Things (IoT), artificial intelligence (AI), robotics, automation, and green technologies in marine logisticswarehousemanagement. Themethodology used includes a comprehensive literature review, in-depth interviews with industry experts, and analysis of case studies of new technology implementation in marine logistics companies. The results revealed significant opportunities and benefits from the implementation of new technologies, such as improved visibility and real-time monitoring of cargo, optimization of cargo storage and handling, increased productivity and safety, and reduced operational carbon footprint. However, challenges such as initial investment costs, data security, system interoperability, and human resource training needs must be overcome. This research presents a comprehensive implementation strategy, including cost-benefit analysis, development of industry standards, implementation of strict security protocols, and collaboration between industry, government, and standardization bodies.A conceptual framework is developed to provide practical guidance for the marine logistics industry to effectively implement new technologies and optimize their benefits. By adopting new technologies appropriately, the marine logistics industry can improve its competitiveness and global supply chain performance, and support environmental sustainability.

Privacy-Centric AI and IoT Solutions for Smart Rural Farm Monitoring and Control.

The integration of artificial intelligence (AI) and the Internet of Things (IoT) in agriculture has significantly transformed rural farming. However, the adoption of these technologies has also introduced privacy and security concerns, particularly unauthorized breaches and cyber-attacks on data collected from IoT devices and sensitive information. The present study addresses these concerns by developing a comprehensive framework that provides practical, privacy-centric AI and IoT solutions for monitoring smart rural farms. This is performed by designing a framework that includes a three-phase protocol that secures data exchange between the User, the IoT Sensor Layer, and the Central Server. In the proposed protocol, the Central Server is responsible for establishing a secure communication channel by verifying the legitimacy of the IoT Sensor devices and the User and securing the data using rigorous cryptographic techniques. The proposed protocol is also validated using the Automated Validation of Internet Security Protocols and Applications (AVISPA) tool. The formal security analysis confirms the robustness of the protocol and its suitability for real-time applications in AI and IoT-enabled smart rural farms, demonstrating resistance against various attacks and enhanced performance metrics, including a computation time of 0.04 s for 11 messages and a detailed search where 119 nodes were visited at a depth of 12 plies in a mere search time of 0.28 s.

Toward designing a lightweight RFID authentication protocol for constrained environments

AbstractIn present times, Radio‐Frequency Identification (RFID) systems have seen a significant rise in their usage. There has been an increasing interest in developing even lighter RFID protocols suitable for resource‐constrained environments. Ensuring security and privacy remain critical challenges in RFID‐based systems. Recently proposed lightweight authentication schemes, namely LRSAS+ and LRARP+, are ideally suited for constrained devices. However, this article investigates these schemes and reveals certain vulnerabilities: LRSAS+ is susceptible to tag impersonation, desynchronization, and traceability attacks, while LRARP+ can fall prey to traceability and secret disclosure attacks. An enhanced version of these authentication systems is proposed that tackles their inherent weaknesses by leveraging the function. To verify the security of the proposed scheme, a formal analysis is conducted using Gong–Needham–Yahalom logic (GNY logic) and an automated security protocol verification tool, ProVerif. The improved scheme's effectiveness is also compared with multiple contemporary lightweight systems. The results indicate that the enhanced scheme not only meets the security requirements for lightweight authentication schemes but also achieves this with minimal computational overhead.

MCTE-RPL: A multi-context trust-based efficient RPL for IoT

The Internet of things (IoT) is highly exposed to various attacks due to its sensitive applications, but it is very vulnerable in dealing with these attacks. So, various studies have been introduced to improve IoT security. Most of methods have focused on improving the security of the RPL protocol (Low-Power and Lossy Networks routing protocol) based on the development of trust models. However, most of these researches have considered good and bad behaviors to calculate the value of trust. This way of evaluating trust alone is not enough and it is vulnerable in dealing with some attacks, especially smart attacks (such as on-off attack, selective sending). To improve this issue, in this paper, a method called multi-context trust-based efficient RPL for IoT (MCTE-RPL) is proposed based on RPL development utilizing trust models with intrusion detection system. MCTE-RPL focuses on three important principles, including establishing secure and reliable routing topology, evaluating trust, and detecting malicious nodes. In the first step, the network routing topology is formed based on the trust and conditions of the nodes. In the second step, in accordance with the data exchanges, the trust of the nodes is evaluated and malicious factors are identified. The simulation results using cooja indicated the superiority of MCTE-RPL in improving routing reliability and data exchange compared to previous studies.

Web Application Security: A Pragmatic Exposé

Many individuals, organizations, and industries rely on web applications for the daily operations of their businesses. With the increasing deployment and dependence on these applications, significant attention has been directed toward developing more accurate and secure mechanisms to safeguard them from malicious web-based attacks. The slow adoption of the latest security protocols, coupled with the utilization of inaccurate and inadequately tested security measures, has hindered the establishment of efficient and effective security measures for web apps. This article reviews recent research and their recommendations for web security over the last 4 years. It identifies code injection as one of the recent most prevalent web-based attacks. The recommendations presented in this article offer a practical guide, enabling individuals and security personnel across various industries and organizations to implement tested and proven security measures for web applications. Furthermore, it serves as a roadmap for security developers, aiding them in creating more accurate and quantifiable measures and mechanisms for web security.

Highly secure authentication and key agreement protocol for the internet of vehicles

Highly secure authentication and key agreement protocol for the internet of vehicles

Enhancing UAV‐HetNet security through functional encryption framework

SummaryIn the current landscape, the rapid expansion of the internet has brought about a corresponding surge in the number of data consumers. As user volume and diversity have escalated, the shift from conventional, uniform networks to Heterogeneous Networks (HetNets) has emerged. HetNets are designed with a primary objective: enhancing Quality of Service (QoS) standards for users. In the context of HetNets facilitated by Unmanned Aerial Vehicles (UAVs), a substantial influx of users and devices is observed. Within this multifaceted environment, the potential for malicious intruder nodes to efficiently execute and propagate harmful actions across the network is a distinct concern. Consequently, the entirety of network communication becomes susceptible to a multitude of security threats. To address these vulnerabilities and safeguard communication, the Functional Encryption (FE) technique is employed. FE empowers the protection of data against intrusion attacks. This paper presents a comprehensive methodology for implementing FE within UAV‐integrated HetNets, executed in two sequential phases. The initial phase secures communication between User Equipment (UE) and Micro Base Station (MBS), followed by the second phase, which focuses on securing communication among MBS and UAV. The viability of the proposed approach is substantiated through validation using the Automated Validation of Internet Security Protocols and Applications (AVISPA) tool. The validation process involves the development of High‐Level Protocol Specification Language (HLPSL) codes. The successful security validation outcome underscores the capacity of the proposed methodology to provide the intended security measures and robustness to the network environment.

Privacy preservation-based federated learning with uncertain data

Federated learning (FL) belongs to distributed machine learning. It allows data information sharing between users while protecting their data privacy at the same time. However, in many real-world scenarios, the data collected by client devices may be affected by noise in the working environment, leading to the decreased accuracy and confidence. Therefore, it is necessary to take measures to reduce data uncertainty in order to enhance the performance of FL algorithms. Traditional FL methods encounter challenges in handling uncertain data, which motivates the introduction of multi-view learning in this paper which designs an FL model suitable for highly variable data characteristics. We first achieve information complementarity among data views while ensuring the consistency in data representation. Furthermore, we quantify data uncertainty using the reachable region of data noise, thereby improving model robustness. To maintain data privacy between clients, we design an adaptive Kalman filter-based differential protection security protocol. Clients use the protocol to process local data and upload it to the master server, which returns the updated model parameters to the clients. The experimental results demonstrate the effectiveness of the federated learning model proposed in this paper.

Securing Cloud Infrastructure: An In-Depth Analysis of Microsoft Azure Security

Amid the rapid expansion of cloud computing, businesses are increasingly recognizing the significance of security, particularly when utilizing platforms such as Microsoft Azure. As companies increasingly shift their operations to cloud environments, the importance of implementing robust security protocols cannot be overstated. Microsoft Azure, a leading provider of cloud services, offers a comprehensive suite of security features designed to safeguard data, applications, and infrastructure.This document presents an in-depth analysis of Azure's security framework, examining its architectural nuances, diverse range of embedded security functionalities, suggested methodologies, and adherence to industry standards. By immersing themselves in the complexities of Azure's layered security model and adopting resilient security measures, companies can adeptly maneuver through the cloud environment, effectively reducing risks and safeguarding the confidentiality and availability of their data and software solutions

How to mitigate the risks of deployment of artificial intelligence in medicine?

The aim of this study is to examine the risks associated with the use of artificial intelligence (AI) in medicine and to offer policy suggestions to reduce these risks and optimize the benefits of AI technology. AI is a multifaceted technology. If harnessed effectively, it has the capacity to significantly impact the future of humanity in the field of health, as well as in several other areas. However, the rapid spread of this technology also raises significant ethical, legal, and social issues. This study examines the potential dangers of AI integration in medicine by reviewing current scientific work and exploring strategies to mitigate these risks. Biases in data sets for AI systems can lead to inequities in health care. Educational data that is narrowly represented based on a demographic group can lead to biased results from AI systems for those who do not belong to that group. In addition, the concepts of explainability and accountability in AI systems could create challenges for healthcare professionals in understanding and evaluating AI-generated diagnoses or treatment recommendations. This could jeopardize patient safety and lead to the selection of inappropriate treatments. Ensuring the security of personal health information will be critical as AI systems become more widespread. Therefore, improving patient privacy and security protocols for AI systems is imperative. The report offers suggestions for reducing the risks associated with the increasing use of AI systems in the medical sector. These include increasing AI literacy, implementing a participatory society-in-the-loop management strategy, and creating ongoing education and auditing systems. Integrating ethical principles and cultural values into the design of AI systems can help reduce healthcare disparities and improve patient care. Implementing these recommendations will ensure the efficient and equitable use of AI systems in medicine, improve the quality of healthcare services, and ensure patient safety.

Enhanced Encrypted Traffic Analysis Leveraging Graph Neural Networks and Optimized Feature Dimensionality Reduction

With the continuously growing requirement for encryption in network environments, web browsers are increasingly employing hypertext transfer protocol security. Despite the increase in encrypted malicious network traffic, the encryption itself limits the data accessible for analyzing such behavior. To mitigate this, several studies have examined encrypted network traffic by analyzing metadata and payload bytes. Recent studies have furthered this approach, utilizing graph neural networks to analyze the structural data patterns within malicious encrypted traffic. This study proposed an enhanced encrypted traffic analysis leveraging graph neural networks which can model the symmetric or asymmetric spatial relations between nodes in the traffic network and optimized feature dimensionality reduction. It classified malicious network traffic by leveraging key features, including the IP address, port, CipherSuite, MessageLen, and JA3 features within the transport-layer-security session data, and then analyzed the correlation between normal and malicious network traffic data. The proposed approach outperformed previous models in terms of efficiency, using fewer features while maintaining a high accuracy rate of 99.5%. This demonstrates its research value as it can classify malicious network traffic with a high accuracy based on fewer features.

QSKCG: Quantum‐based secure key communication and key generation scheme for outsourced data in cloud

AbstractIn the era of digital proliferation, individuals opt for cloud servers to store their data due to the diverse advantages they offer. However, entrusting data to cloud servers relinquishes users' control, potentially compromising data confidentiality and integrity. Traditional auditing methods designed to ensure data integrity in cloud servers typically depend on Trusted Third Party Auditors. Yet, many of these existing auditing approaches grapple with intricate certificate management and key escrow issues. Furthermore, the imminent threat of powerful quantum computers poses a risk of swiftly compromising these methods in polynomial time. To overcome these challenges, this paper introduces a Quantum‐based Secure Key Communication and Key Generation Scheme QSKCG for Outsourced Data in the Cloud. Leveraging Elliptic Curve Cryptography, the BB84 secure communication protocol, certificateless signature, and blockchain network, the proposed scheme is demonstrated through security analysis, affirming its robustness and high efficiency. Additionally, performance analysis underscores the practicality of the proposed scheme in achieving post‐quantum security in cloud storage.

IMPLEMENTATION OF ENERGY EFFICIENT SECURITY MECHANISMS FOR WIRELESS SENSOR NETWORKS

Designing energy-efficient security mechanisms is critical for WSNs due to their limited power resources. Techniques like low-power cryptography, duty cycling, and energy-aware security protocols can help in achieving security without significantly impacting the energy consumption of sensor nodes. In this paper, we propose an energy aware geographical multipath routing scheme for WSNs. The distance to the destination location, remaining battery capacity, and queue size of candidate sensor nodes in the local communication range are taken into consideration for next hop relay node selection, and Analytical Hierarchy Process (AHP) and Geographical Routing Algorithm (GRA) are applied for decision making. Simulation results show that these schemes can extend the network lifetime longer than the original geographical routing scheme which only considers distance to the destination location. KEYWORDS: WSN, linear discriminate packet flow analysis system, optimized rout path switch, Analytical Hierarchy Process (AHP) and Geographical Routing Algorithms(GRA).

A Review of The Literature on An Examination of The Effects of Cyber Security in Improved Public Sector Institution Performance in Jordan

Gaining a clearer knowledge of the present level of cyber security adoption by public sector institutions—particularly in Jordan—was one of the study’s intended results. In order to improve the effectiveness of these institutions, the research reviewed various literature review on the current state of cyber security adoption. It also identified the obstacles that these institutions faced in embracing and executing these technologies, as well as the tactics that were required to improve cyber security applications. The assessment also highlighted cyber security initiatives that can boost operational effectiveness, enhance service delivery, and give the populace of the nation access to a safer digital environment. Therefore, by highlighting the difficulties and obstacles these organizations encounter, the research may contribute to the formulation of policy and decision-making frameworks that will direct future expenditures on cyber security and associated technology. Additionally, the results of this study may help improve cyber security protocols and implement them in Jordan’s public sector organizations. This might thus result in better service delivery, more operational efficiency, and a safer online environment for the people of the nation. The study may also contribute to policy and decision-making processes by highlighting the obstacles and difficulties these organizations confront, which might direct subsequent investments within cyber security innovations and technology.

Secure System with Security Protocol for Interactions in Healthcare Internet of Things

The Internet of Things is a network of connected devices that can communicate and share data over the Internet. These devices often have sensors that collect data for various purposes, such as usage statistics, data processing, or performing specific actions based on the collected data. Also, medical Internet of Things devices are crucial in monitoring critical functions, measuring blood glucose levels, indicating when patients require medicine, and ensuring timely medication delivery. Communication in the Internet of Things is demanding, requiring diverse protocols that address communication security concerns. These protocols must be robust and secure, considering technical factors such as the network’s objective, energy requirements, and the nature of the communication because they can be exploited. This paper proposes an innovative system with a security protocol that supports and improves communication security in modern Internet of Things networks. The protocol aims to enhance communication safety between interconnected devices for information exchange in medicine or healthcare, ensuring the confidentiality and integrity of sent data and devices. The proposed protocol, tested through formal and automated verification, meets all security goals, including identity verification, anonymity protection, and access revokement. It also protects against Man-in-the-middle, modification, replay, and impersonation attacks.