Current Security Protocols Research Articles

Advancing Cybersecurity with Honeypots and Deception Strategies

Cybersecurity threats are becoming more intricate, requiring preemptive actions to safeguard digital assets. This paper examines the function of honeypots as critical instruments for threat detection, analysis, and mitigation. A novel methodology for comparative analysis of honeypots is presented, offering a systematic framework to assess their efficacy. Seven honeypot solutions, namely Dionaea, Cowrie, Honeyd, Kippo, Amun, Glastopf, and Thug, are analyzed, encompassing various categories, including SSH and HTTP honeypots. The solutions are assessed via simulated network attacks and comparative analyses based on established criteria, including detection range, reliability, scalability, and data integrity. Dionaea and Cowrie exhibited remarkable versatility and precision, whereas Honeyd revealed scalability benefits despite encountering data quality issues. The research emphasizes the smooth incorporation of honeypots with current security protocols, including firewalls and incident response strategies, while offering comprehensive insights into attackers’ tactics, techniques, and procedures (TTPs). Emerging trends are examined, such as incorporating machine learning for adaptive detection and creating cloud-based honeypots. Recommendations for optimizing honeypot deployment include strategic placement, comprehensive monitoring, and ongoing updates. This research provides a detailed framework for selecting and implementing honeypots customized to organizational requirements.

Healthcare Cybersecurity: Data Poisoning in the Age of AI

This study focuses on the challenges the healthcare sector faces in the wake of increasing digitalization, particularly the growing threat of data poisoning in AI systems. Unlike other research, this work delves into the current security protocol weaknesses, highlighting the specific vulnerabilities of healthcare systems and the urgent need for innovative solutions to protect both patients and institutions. Throughout the research, key gaps in security mechanisms are identified and analyzed, showing how these flaws can be exploited by attackers to compromise sensitive information, undermining trust in digital healthcare tools. The methodology combines existing theories with real-world data, allowing for an in-depth and detailed analysis of the risks posed by data poisoning. Advanced cybersecurity strategies are presented, emphasizing the importance of multi-layered detection and mitigation systems designed specifically for the healthcare sector’s needs. Additionally, the broader impact these cybersecurity challenges could have on business processes is explored, revealing how they might slow down the essential digital transformation required to enhance modern healthcare services. This study not only sheds light on security issues in the healthcare sector but also offers practical recommendations to strengthen current defenses. In conclusion, it calls for urgent action to develop new technologies and enforce stricter regulations that safeguard data integrity and ensure a safe and successful digital transition in the face of emerging cybersecurity threats. Received: 9 August 2024 | Revised: 23 September 2024 | Accepted: 8 October 2024 Conflicts of Interest The author declares that he has no conflicts of interest to this work. Data Availability Statement Data sharing is not applicable to this article as no new data were created or analyzed in this study. Author Contribution Statement Edwin Gerardo Acuña Acuña: Conceptualization, Methodology, Software, Validation, Formal analysis, Investigation, Resources, Data Curation, Writing - Original Draft, Writing -Review & Editing, Visualization, Supervision, Project administration, Funding acquisition.

Survey of UAV Security Authentication and Cryptography Protocols

Unmanned aerial vehicles, or UAVs, are gaining significant attention because of the strategic and financial information and value involved in aerial applications, as well as the sensitive data collected through embedded sensors. UAVs are rapidly developing in various fields and find widespread utility in military applications for effective target tracking, battlefield surveillance, radiation monitoring, and sports. UAVs are useful and advantageous, but they can also be attacked by a variety of techniques, including jamming, fuzzing, false data injection, and zero attacks. Researchers were looking on robust security mechanisms to protect UAVs from attackers in order to combat such security risks. However, there is a large number of vulnerabilities in designed protocols that hackers could take advantage of. For the identification and addressing of those vulnerabilities and weaknesses, it is becoming highly important to study and analyze current security protocols that are used in the UAVs. In this study, a thorough survey will be introduced on authentication and cryptography that are used in the UAV protocols, as well as describing architecture, procedure, and security of Micro Aerial Vehicle Link (MavLink) protocol. We will explain as well the cryptography and authentication approach that has been developed by the use of the MavLink protocol. Index Terms— UAV, Unmanned Aerial Vehicles, MavLink, Zero attack.

K‐Fuse: Credit card fraud detection based on a classification method with a priori class partitioning and a novel feature selection strategy

AbstractOnline transactions have become the dominant and most popular form of online payment in today's digital economy. Due to the growing popularity of e‐commerce and the convenience it offers, both consumers and businesses are rapidly adopting online transactions. Notably, credit cards have become one of the most popular and standard online payment methods. However, it should be noted that credit card transactions are not without challenges. In particular, detecting and preventing fraudulent transactions is a major concern of the online payment system. It is difficult to find an effective detection model that can detect the new patterns created by fraudsters, due to the constant evolution of their methods to exploit the vulnerability of current security protocols. These fraud patterns are evolving and may not correspond to existing documented models, leading to a reduction in their identification. In addition, the customer's behavior can affect the model detection as it is susceptible to change based on factors such as economic conditions, trends, and individual circumstances. When consumers deviate from their typical behavior, the model may generate false alerts, thereby reducing its ability to differentiate between legitimate and fraudulent transactions. This article presents a new supervised detection model, called K‐Fuse, which introduces an unsupervised phase in order to detect fraud patterns that may correspond to innovative models introduced by fraudsters. K‐Fuse is a supervised classification method that fuses three steps consisting of (i) unsupervised clustering to identify hidden patterns of transactions in a dataset, (ii) a novel feature selection criterion based on the unsupervised results, and (iii) supervised classification to exploit the results of clustering and feature selection to predict new transactions as fraudulent or legitimate.

A Comprehensive Study on the Role of Machine Learning in 5G Security: Challenges, Technologies, and Solutions

Fifth-generation (5G) mobile networks have already marked their presence globally, revolutionizing entertainment, business, healthcare, and other domains. While this leap forward brings numerous advantages in speed and connectivity, it also poses new challenges for security protocols. Machine learning (ML) and deep learning (DL) have been employed to augment traditional security measures, promising to mitigate risks and vulnerabilities. This paper conducts an exhaustive study to assess ML and DL algorithms’ role and effectiveness within the 5G security landscape. Also, it offers a profound dissection of the 5G network’s security paradigm, particularly emphasizing the transformative role of ML and DL as enabling security tools. This study starts by examining the unique architecture of 5G and its inherent vulnerabilities, contrasting them with emerging threat vectors. Next, we conduct a detailed analysis of the network’s underlying segments, such as network slicing, Massive Machine-Type Communications (mMTC), and edge computing, revealing their associated security challenges. By scrutinizing current security protocols and international regulatory impositions, this paper delineates the existing 5G security landscape. Finally, we outline the capabilities of ML and DL in redefining 5G security. We detail their application in enhancing anomaly detection, fortifying predictive security measures, and strengthening intrusion prevention strategies. This research sheds light on the present-day 5G security challenges and offers a visionary perspective, highlighting the intersection of advanced computational methods and future 5G security.

Detecting the Security Level of Various Cryptosystems Using Machine Learning Models

With recent advancements in multimedia technologies, the security of digital data has become a critical issue. To overcome the vulnerabilities of current security protocols, researchers tend to focus their efforts on modifying existing protocols. Over the last few decades, though, several proposed encryption algorithms have been proven insecure, leading to major threats against important data. Using the most appropriate encryption algorithm is a very important means of protection against such attacks, but which algorithm is most appropriate in any particular situation will also be dependent on what sort of data is being secured. However, testing potential cryptosystems one by one to find the best option can take up an important processing time. For a fast and accurate selection of appropriate encryption algorithms, we propose a security level detection approach for image encryption algorithms by incorporating a support vector machine (SVM). In this work, we also create a dataset using standard encryption security parameters, such as entropy, contrast, homogeneity, peak signal to noise ratio, mean square error, energy, and correlation. These parameters are taken as features extracted from different cipher images. Dataset labels are divided into three categories based on their security level: strong, acceptable, and weak. To evaluate the performance of our proposed model, we have performed different analyses (f1-score, recall, precision, and accuracy), and our results demonstrate the effectiveness of this SVM-supported system. INDEX TERMS Support vector machine (SVM), security analysis, image encryption, cryptosystem.

Application of mathematics in design of group key management method

Group theory and Combinatorics play important role in design security protocols, algorithms and techniques for various security applications. Secure group-oriented communication is crucial to a wide range of applications in Internet of Things (IoT). Key management is the mechanism which is used to work out the problem of creation, establishment, to distribute, periodic refresh and the maintenance of the cryptographic keys. It is not enough to care only about primitives that satisfy a stated security objective in the domain of the IoT. The design of group key establishment techniques for securing group communications between resource-constrained IoT devices is presented in this work. Furthermore, the paper assesses possible ways for tailoring current security protocols to the peculiarities of IoT devices and networks.

Improving Energy Efficiency in Internet of Things using Artificial Bee Colony Algorithm

Background: With the advent of IoT, the deployment of batteries with a limited lifetime in remote areas is a major concern. In certain conditions, the network lifetime gets restricted due to limited battery constraints. Subsequently, the collaborative approaches for key facilities to reduce the constraint demands of the current security protocols. Objective: This work covers and combines a wide range of concepts linked by IoT based on security and energy efficiency. Specifically, this study examines the WSN energy efficiency problem among IoT devices and security for the management of threats in IoT through collaborative approaches and finally outlines the future. The concept of energy-efficient key protocols which clearly cover heterogeneous IoT communications among peers with different resources has been developed. Because of the low capacity of sensor nodes, the energy efficiency in WSNs has been an important concern. Methods: In this paper, we present an algorithm for Artificial Bee Colony (ABC) which reviews security and energy consumption to discuss their constraints in the IoT scenarios. Results: The results of a detailed experimental assessment are analyzed in terms of communication cost, energy consumption and security, which prove the relevance of a proposed ABC approach and a key establishment. Conclusion: The validation of DTLS-ABC consists of designing an inter-node cooperation trust model for the creation of a trusted community of elements that are mutually supportive. Initial attempts to design the key methods for management are appropriate individual IoT devices. This gives the system designers, an option that considers the question of scalability.

ALAM: Anonymous Lightweight Authentication Mechanism for SDN-Enabled Smart Homes

The smart connected devices are the first choice of cybercriminals for spreading spy wares and different security attacks. The current security standards and protocols for Internet of Things (IoT) have failed in providing security to these devices. In addition, IoT market giants are producing nonsecure smart products in order to grab the open market. Furthermore, low resources of IoT devices, limits the traditional host-based protection solutions like anti-virus, IDS, IPS, etc. To overcome the resource constraintness and security barriers of smart devices, a network-level security architecture based on lightweight cryptographic parameters is required. Software-defined networking (SDN) is a new networking paradigm to overcome the control, management, and security issues in traditional networking. The SDN controller handles all the computation and complexities at the network level, rather than smart devices. In this research, we first present a new privacy-preserving security architecture for SDN-based smart homes. Subsequently, an anonymous lightweight authentication mechanism (ALAM) is designed based on the proposed security architecture core foundations. Furthermore, the security characteristics of the proposed protocol are formally analyzed using Burrows-Abadi-Needham (BAN) logic and ProVerif, followed by informal security analysis. Finally, performance evaluation and comparative analysis of the scheme is carried out.

Detecting the Security Level of Various Cryptosystems Using Machine Learning Models

With recent advancements in multimedia technologies, the security of digital data has become a critical issue. To overcome the vulnerabilities of current security protocols, researchers tend to focus their efforts on modifying existing protocols. Over the last few decades, though, several proposed encryption algorithms have been proven insecure, leading to major threats against important data. Using the most appropriate encryption algorithm is a very important means of protection against such attacks, but which algorithm is most appropriate in any particular situation will also be dependent on what sort of data is being secured. However, testing potential cryptosystems one by one to find the best option can take up an important processing time. For a fast and accurate selection of appropriate encryption algorithms, we propose a security level detection approach for image encryption algorithms by incorporating a support vector machine (SVM). In this work, we also create a dataset using standard encryption security parameters, such as entropy, contrast, homogeneity, peak signal to noise ratio, mean square error, energy, and correlation. These parameters are taken as features extracted from different cipher images. Dataset labels are divided into three categories based on their security level: strong, acceptable, and weak. To evaluate the performance of our proposed model, we have performed different analyses (f1-score, recall, precision, and accuracy), and our results demonstrate the effectiveness of this SVM-supported system.

DESIGN AND DEVELOPMENT OF ONTOLOGY WIRELESS SENSOR NETWORKS USING ARTIFICIAL INTELLIGENCE

Ponte Academic JournalJun 2020, Volume 76, Issue 6 DESIGN AND DEVELOPMENT OF ONTOLOGY WIRELESS SENSOR NETWORKS USING ARTIFICIAL INTELLIGENCEAuthor(s): K. MOHAN RAM ,P. Suneel Kumar, B. MalleswariJ. Ponte - Jun 2020 - Volume 76 - Issue 6 doi: 10.21506/j.ponte.2020.6.1 Abstract:In recent years a lot of attention is received by the wireless communicating system by using Artificial Intelligence. In this paper, mainly discuss about the artificial intelligence of wireless sensor network (WSN) in physical layer and proposing a two-step transmission approach for improving the security rate with channel state information. It was addressing the security related issues. The main aim of this proposed system is to reduce the deployment key materials for improving the current security protocols. Further, it discusses about the implementation and design of the wireless network. The security challenge is occurring in the WSNs, so it is very critical to take the communication path between sensor nodes. In this paper proposing a new technique is Ontology based wireless sensor network, for the transportation of security sensor network. This technique works based on the cooperation of all the sensor nodes for avoiding the distribution of KEY and increases the system security. From simulation results it can be observed that the proposed system gives effective output. Download full text:Check if you have access through your login credentials or your institution Username Password

Secure communication for Industrie 4.0

Abstract Industrie 4.0 fosters the concept of systems within one company to communicate with external systems in other companies or cloud services. Such communication across security domains creates new challenges as multiple stakeholders with different, sometimes conflicting objectives are involved. Typical examples are confidentiality versus monitoring. Current security architectures and protocols are not designed to resolve these challenges.

SCRPM: securing crowdsourcing-based road pavement monitoring system with location privacy

To provide privacy protection, security protocols for crowdsourcing-based systems typically encrypt data before uploading it to the server. However, current security protocols cannot provide protection for real identities, resulting in the location privacy issue. Moreover, current security protocols for crowdsourcing-based systems are mainly based on bilinear map, which is quite time-consuming. To address the above security and efficiency issues, we propose a novel security protocol with location privacy called SCRPM. Similar to protocols of this field, SCRPM can provide privacy protection for uploaded data. However, different from other well-known approaches, SCRPM uses pseudonyms instead of real identities, which can provide location privacy protection for cars. At the same time, to avoid using bilinear pairing, we introduce the algebraic signature technique to SCRPM and design novel signcryption algorithms, which is quite light weight. By doing so, SCRPM can provide location privacy protection for cars while still enjoying high efficiency. Experimental results show SCRPM is feasible for real world applications.

SIM-RPM: Secure Incentive Mechanism for Crowdsourcing-Based Road Pavement Monitoring System

Crowdsourcing-based road pavement monitoring is a new computing paradigm, where some drivers detect road surface information and publish it to the data center, while other drivers download it for use. However, current road pavement monitoring schemes lack an incentive mechanism, which discourages drivers from publishing road surface information. More importantly, current security protocols for crowdsourcing-based systems are built on real identities of drivers, resulting in the user privacy problem. At the same time, current security protocols for crowdsourcing-based systems use time-consuming bilinear map for designing cryptographic algorithms, resulting in the efficiency problem. To solve the above problems, we present a secure incentive mechanism for road pavement monitoring systems called SIM-RPM. Different from protocols of this field, SIM-RPM is built on pseudonyms, which can provide user privacy protection for drivers. Moreover, by designing an access control protocol, SIM-RPM enables drivers to sell road surface information, which encourages drivers to attend the road pavement monitoring system. Finally, to reduce the time cost, we introduce the Bloom filter technique to SIM-RPM, and design novel light-weight cryptographic algorithms. By doing so, SIM-RPM provides a secure and efficient incentive mechanism for crowdsourcing-based road pavement monitoring systems. Experimential results show SIM-RPM is feasible for real world applications.

A Novel Physical Layer Secure Key Generation and Refreshment Scheme for Wireless Sensor Networks

Physical layer secure key generation (PL-SKG) schemes have received a lot of attention from the wireless security community in recent years because of the potential benefits that they could bring to the security landscape. These schemes aim to strengthen current security protocols by reducing the amount of key material that devices need for deployment. They do this by harnessing the common source of randomness provided by the wireless channel that the physical layer is communicating over. This is of particular importance in wireless sensor networks (WSNs) where resources are particularly scarce and where issues, such as key revocation and recovery make the design of efficient key management schemes extremely difficult. This paper discusses the issues and challenges encountered in the design and implementation of PL-SKG schemes on off-the-shelf WSNs. It then proposes a novel key generation scheme that takes advantage of both the power and simplicity of classic error correcting codes and also the diversity of frequency channels available on 802.15.4 compliant nodes to generate keys from received signal strength readings. This paper shows that our key generation and refreshment scheme can achieve a near 100% key reconciliation rate whilst also providing perfect forward and backward security.

Wireless Anomaly Detection Based on IEEE 802.11 Behavior Analysis

Wireless communication networks are pervading every aspect of our lives due to their fast, easy, and inexpensive deployment. They are becoming ubiquitous and have been widely used to transfer critical information, such as banking accounts, credit cards, e-mails, and social network credentials. The more pervasive the wireless technology is going to be, the more important its security issue will be. Whereas the current security protocols for wireless networks have addressed the privacy and confidentiality issues, there are unaddressed vulnerabilities threatening their availability and integrity (e.g., denial of service, session hijacking, and MAC address spoofing attacks). In this paper, we describe an anomaly based intrusion detection system for the IEEE 802.11 wireless networks based on behavioral analysis to detect deviations from normal behaviors that are triggered by wireless network attacks. Our anomaly behavior analysis of the 802.11 protocols is based on monitoring the n-consecutive transitions of the protocol state machine. We apply sequential machine learning techniques to model the n-transition patterns in the protocol and characterize the probabilities of these transitions being normal. We have implemented several experiments to evaluate our system performance. By cross validating the system over two different wireless channels, we have achieved a low false alarm rate (<0.1%). We have also evaluated our approach against an attack library of known wireless attacks and has achieved more than 99% detection rate.

Enhancing Security in Mobile IPv6

In the Mobile IPv6 (MIPv6) protocol, a mobile node (MN) is a mobile device with a permanent home address (HoA) on its home link. The MN will acquire a care-of address (CoA) when it roams into a foreign link. It then sends a binding update (BU) message to the home agent (HA) and the correspondent node (CN) to inform them of its current CoA so that future data packets destined for its HoA will be forwarded to the CoA. The BU message, however, is vulnerable to different types of security attacks, such as the man-in-the-middle attack, the session hijacking attack, and the denial-of-service attack. The current security protocols in MIPv6 are not able to effectively protect the BU message against these attacks. The private-key-based BU (PKBU) protocol is proposed in this research to overcome the shortcomings of some existing MIPv6 protocols. PKBU incorporates a method to assert the address ownership of the MN, thus allowing the CN to validate that the MN is not a malicious node. The results obtained show that it addresses the security requirements while being able to check the address ownership of the MN. PKBU also incorporates a method to verify the reachability of the MN.

Enhancing Security in Mobile IPv6

In the Mobile IPv6 (MIPv6) protocol, a mobile node (MN) is a mobile device with a permanent home address (HoA) on its home link. The MN will acquire a care-of address (CoA) when it roams into a foreign link. It then sends a binding update (BU) message to the home agent (HA) and the correspondent node (CN) to inform them of its current CoA so that future data packets destined for its HoA will be forwarded to the CoA. The BU message, however, is vulnerable to different types of security attacks, such as the man-in-the-middle attack, the session hijacking attack, and the denial-of-service attack. The current security protocols in MIPv6 are not able to effectively protect the BU message against these attacks. The private-key-based BU (PKBU) protocol is proposed in this research to overcome the shortcomings of some existing MIPv6 protocols. PKBU incorporates a method to assert the address ownership of the MN, thus allowing the CN to validate that the MN is not a malicious node. The results obtained show that it addresses the security requirements while being able to check the address ownership of the MN. PKBU also incorporates a method to verify the reachability of the MN.

Delay- and Disruption-Tolerant Networking (DTN): An Alternative Solution for Future Satellite Networking Applications

Satellite communications are characterized by long delays, packet losses, and sometimes intermittent connectivity and link disruptions. The TCP/IP stack is ineffective against these impairments and even dedicated solutions, such as performance enhancing proxies (PEPs), can hardly tackle the most challenging environments, and create compatibility issues with current security protocols. An alternative solution arises from the delay- and disruption-tolerant networking (DTN) architecture, which specifies an overlay protocol, called bundle protocol (BP), on top of either transport protocols (TCP, UDP, etc.), or of lower layer protocols (Bluetooth, Ethernet, etc.). The DTN architecture provides long-term information storage on intermediate nodes, suitable for coping with disrupted links, long delays, and intermittent connectivity. By dividing the end-to-end path into multiple DTN hops, in a way that actually extends the TCP-splitting concept exploited in most PEPs, DTN allows the use of specialized protocols on the satellite (or space) links. This paper discusses the prospects for use of DTN in future satellite networks. We present a broad DTN overview, to make the reader familiar with the characteristics that differentiate DTN from ordinary TCP/IP networking, compare the DTN and PEP architectures and stacks, as a preliminary step for the subsequent DTN performance assessment carried out in practical LEO/GEO satellite scenarios. DTN security is studied next, examining the advantages over present satellite architectures, the threats faced in satellite scenarios, and also open issues. Finally, the relation between DTN and quality of service (QoS) is investigated, by focusing on QoS architectures and QoS tools and by discussing the state of the art of DTN research activity in modeling, routing, and congestion control.