Heterogeneous Network Security Research Articles

Distributed Reinforcement Learning for IoT Security in Heterogeneous and Distributed Networks

The explosive growth of the Internet of Things (IoT) has significantly increased networked devices within distributed and heterogeneous networks. Due to these networks’ inherent vulnerabilities and diversity, the proliferation of IoT devices presents substantial security challenges. Traditional security solutions face challenges in keeping up with the constantly changing threats in dynamic situations. This article reviews the application of distributed Reinforcement Learning approaches to enhance IoT security in dispersed and heterogeneous networks. This paper provides a comprehensive overview of the fundamental theories reinforcing IoT security. We also explore the basis of Distributed Reinforcement Learning and discuss its benefits and drawbacks for IoT security. Then, we focus on how Distributed Reinforcement Learning might address these issues and offer details on the design factors to consider when implementing Distributed Reinforcement Learning-based solutions into practice. We outline case studies and experiments that show how Distributed Reinforcement Learning may enhance IoT security. We also address performance analysis and evaluation measures to compare Distributed Reinforcement Learning-based approaches with conventional security methods. Finally, we highlight the possible uses of Distributed Reinforcement Learning in IoT security and suggest future directions, emerging trends, and unresolved challenges.

Extensive Review of Security and Privacy Issues in Heterogeneous Networks

This paper studies advanced network security, privacy, and performance issues for Heterogeneous Networks (HetNets), which combine multiple types of cells to enhance wireless communication coverage and capacity. The coexistence of heterogeneous network elements inherently leads to considerable issues in terms of security, privacy, and performance. Previous research has primarily addressed security issues using common cryptographic techniques including data encryption with AES, secure key exchange with RSA, and handoff security with mutual authentication. Data anonymization methods and regulatory compliance have been used to address privacy concerns. Allocating resources and managing interference are some of the tactics that have been used in performance optimization. Although the current solutions offer a solid base, they frequently have issues with computational overhead, scalability, and continuous maintenance needs. Performance optimization solutions might not sufficiently handle dynamic network conditions, and privacy protections might not be sufficient to mitigate sophisticated data harvesting operations. This study evaluates existing HetNets solutions using a thorough evaluation and analytical technique. It assesses how well they handle issues with security, privacy, and performance, points out any shortcomings, and suggests areas for further investigation. Our analysis emphasizes the need for improved security mechanisms, including quantum-resistant cryptography, AI-driven threat detection, and technologies that improve privacy, such as differential privacy and homomorphic encryption. Innovative resource management and optimization strategies catered to HetNets' dynamic nature are needed to address performance issues. The necessity of developing new security, privacy, and performance solutions to guarantee the stability and dependability of HetNets is emphasized by this study. Stakeholders can promote the broad adoption and smooth integration of HetNet technology by tackling these obstacles. To sum up, this study offers a thorough analysis of the HetNets' architectural, security, privacy, and performance issues. It lists existing remedies, analyses their drawbacks, and suggests new lines of inquiry for future development to advance the area and improve HetNets' operational capabilities.

A Knowledge Graph Completion Algorithm Based on the Fusion of Neighborhood Features and vBiLSTM Encoding for Network Security

Knowledge graphs in the field of network security can integrate diverse, heterogeneous, and fragmented network security data, further explore the relationships between data, and provide support for deep analysis. Currently, there is sparse security information in the field of network security knowledge graphs. The limited information provided by traditional text encoding models leads to insufficient reasoning ability, greatly restricting the development of this field. Starting from text encoding, this paper first addresses the issue of the inadequate capabilities of traditional models using a deep learning model for assistance. It designs a vBiLSTM model based on a word2vec and BiLSTM combination to process network security texts. By utilizing word vector models to retain semantic information in entities and extract key features to input processed data into BiLSTM networks for extracting higher-level features that better capture and express their deeper meanings, this design significantly enhances understanding and expression capabilities toward complex semantics in long sentences before inputting final feature vectors into the KGC-N model. The KGC-N model uses feature vectors combined with graph structure information to fuse forward and reverse domain features and then utilizes a Transformer decoder to decode predictions and complete missing information within the network security knowledge map. Compared with other models using evaluation metrics such as MR, MRR demonstrates that employing our proposed method effectively improves performance on completion tasks and increases comprehension abilities toward complex relations, thereby enhancing accuracy and efficiency when completing knowledge graphs.

Physical Layer Security in Random NOMA-Enabled Heterogeneous Networks

Physical Layer Security in Random NOMA-Enabled Heterogeneous Networks

HetNet security solution using femtocell network architecture and UMTS technology in the millimetre range

Heterogeneous network security is a prime concern in the era where billions of human beings and physical devices will get connected through next-generation technologies like millimetre-wave wireless, the internet of things, artificial intelligence and machine learning. Higher bandwidth in the millimetre range will help to achieve seamless connectivity and tens of gigabits per second data rate. In femtocells, handoff security will play a vital role to avoid call drops. This article focuses on how new handoff security algorithm deployed in the femtocells network by a method that uses Call Admission Control protocol and UMTS technology. Call admission control protocol gives variable quality of services by efficient utilisation of network resources and available bandwidth. The number of handoffs and probability of call drops is less when the user moves from femtocells to macro-cells. Dynamic threshold time reduces number of handoffs and improves handoff security.

Analysis of Security Issues in Wireless Body Area Networks in Heterogeneous Networks.

Body Area Network (BAN) is one of the most important techniques for observing patient health in real time and identifying and analyzing diseases. For effective implementation of this technology in practice and to benefit from it, there are some key issues which are to be addressed, and among those issues, security is highly critical. WBAN will have to operate in a cooperative networking model of multiple networks such as those of homogeneous networks, for the purpose of performance and reliability, or those of heterogeneous networks, for the purpose of data transfer and processing from application point of view, with the other networks such as the networks of hospitals, clinics, medical experts, etc. and the patient himself/herself, who may be moving from one network to another. This paper brings out the issues related to security in WBAN in separate networks as well as in multiple networks. For WBAN working in a separate network, the IEEE 802.15.6 standard is considered. For WBANs working in multiple networks, especially heterogeneous networks, the security issues are considered. Considering the advancements of artificial intelligence (AI), the paper describes how AI is addressing some challenges faced by WBAN. The paper describes possible approaches which can be taken to address these issues by modeling a security mechanism using various artificial intelligence techniques. The paper proposes game theory with Stackelberg security equilibrium (GTSSE) for modeling security in heterogeneous networks in WBAN and describes the experiments conducted by the authors and the results proving the suitability of the modeling using GTSSE.

Novel Machine Learning (ML) Algorithms to Classify IPv6 Network Traffic in Resource-Limited Systems

Providing machine learning (ML) based security in heterogeneous IoT networks including resource-constrained devices is a challenge because of the fact that conventional ML algorithms require heavy computations. Therefore, in this paper, lightweight ProtoNN, CMSIS-NN, and Bonsai tree ML algorithms were evaluated by using performance metrics such as testing accuracy, precision, F1 score and recall to test their classification ability on the IPv6 network dataset generated on resource-scarce embedded devices. The Bonsai tree algorithm provided the best performance results in all metrics (98.8 in accuracy, 98.9% in F1 score, 99.2% in precision, and 98.8% in recall) compared to the ProtoNN, and CMSIS-NN algorithms.

3D Information Visualization Interactive Technology and Its Application in Network Security

With the increase of network complexity, network security incidents are showing a trend of rapid growth. Aiming at the low efficiency of large-scale network security data visual analysis, this paper proposes a heterogeneous tree network security data organization method, which improves the real-time performance of data analysis, and designs a three-dimensional multi-layer spherical space for large-scale networks Visualization model (TMSM). The experimental results show that this method has strong analytical capabilities for large-scale, high-dispersion network security data, and can effectively identify different types of intrusions and facilitate network forensics.

User-Cell Association for Security and Energy Efficiency in Ultra-Dense Heterogeneous Networks.

The last decades have been characterized by an exponential increase in digital services. The demand is foreseen to further increase in the next years, and mobile networks will have to mandatorily supply connections to enable digital services with very different requirements, from ultra high speed to ultra low latency. The deployment and the coexistence of cells of different size, from femto to macro, will be one of the key elements for providing such pervasive wireless connection: the ultra dense networks (UDN) paradigm. How to associate users and base stations is one of the most investigated research topics. Many criteria can be drawn, from minimization of power consumption to optimization of throughput. In this paper we propose a new utility to optimize two of the most important features of future mobile connection: security and energy consumption. By using our utility it is possible to jointly select the base station to be activated in a UDN, and associate users to the base stations with the aim of maximizing the secure throughput by spending the minimum energy. Moreover, we propose a heuristic that allows to achieve performance very close to the optimal one with reduced complexity. Effectiveness of the proposed approach is proved by means of comparison with benchmark approaches.

A Data Fusion Framework of Multi-Source Heterogeneous Network Security Situational Awareness Based on Attack Pattern

In the context of the era of big data, how to quickly and accurately detect attack events from massive amounts of heterogeneous data and form effective response on time has become the main challenge facing network security today. This paper systematically summarizes the attack behavior with both attack technology mechanisms and characteristics of the attack target, thereby constructing a scalable attack behavior model. Based on the attack behavior model, a data fusion framework of the multi-source heterogeneous network security situation is constructed. The framework normalizes multi-source heterogeneous security data into threat events with an attack pattern as the core and determine the attack state by the causal chain. Finally, the feasibility and effectiveness of the framework are verified by analyzing data in real business scenarios. This framework abstracts the multi-source heterogeneous data into an analyzable attack event, which greatly reduces the amount of data to be analyzed and improves the credibility of the network security situation data, realizing the identification of attack behaviors in the big data environment.

Heterogeneous network security management

The main objective of this paper is to develop authentication delay model for inter domain or intra-domain authentications. Authentication is a security mechanism used to identify mobile nodes (MN) and prevent unauthorised usage when inter-domain or intra domain handoff happens. The latency due to authentication procedure is known as authentication delay. The authentication delay depends on message propagation time, message transmission time, message encryption/decryption time, authentication request service and waiting time at the access point, authentication request service and waiting time at the server, key encryption and decryption time, key generation time and the registration request service and waiting time, etc., In existing systems, authentication delay modelling of homogeneous network domain has been carried out and actual values are not applied for modelling the authentication delay. In the proposed system authentication delay modelling of heterogeneous network domain will be carried out and actual values will be applied for modelling the authentication delay.

Heterogeneous network security management

The main objective of this paper is to develop authentication delay model for inter domain or intra-domain authentications. Authentication is a security mechanism used to identify mobile nodes (MN) and prevent unauthorised usage when inter-domain or intra domain handoff happens. The latency due to authentication procedure is known as authentication delay. The authentication delay depends on message propagation time, message transmission time, message encryption/decryption time, authentication request service and waiting time at the access point, authentication request service and waiting time at the server, key encryption and decryption time, key generation time and the registration request service and waiting time, etc., In existing systems, authentication delay modelling of homogeneous network domain has been carried out and actual values are not applied for modelling the authentication delay. In the proposed system authentication delay modelling of heterogeneous network domain will be carried out and actual values will be applied for modelling the authentication delay.

Physical Layer Security in Heterogeneous Networks With Pilot Attack: A Stochastic Geometry Approach

In this paper, we investigate physical layer security in a two-tier heterogeneous network with sub-6 GHz massive multi-input multi-output (MIMO) macro cells and millimeter wave (mmWave) small cells. By considering pilot attacks from the eavesdroppers, we analyze the coverage and secrecy performance using stochastic geometry. For the sub-6 GHz tier, we show that increasing the number of BS antennas is more effective than increasing BS density in improving the coverage performance, whereas densifying BS is more effective for security enhancement. For the mmWave tier, we first derive the success probability of beam alignment based on a beam sweeping-based channel training model. It is shown that the mmWave tier may outperform the sub-6 GHz counterpart in terms of both coverage and secrecy through densifying the base stations. Our results also reveal that the mmWave small cell can provide better coverage performance in the high transmission rate region, and can achieve higher security in the low redundant rate region, which reveals the advantage of using mmWave for secure communication. Numerical results verify the analysis.

Multi-source fusion-based security detection method for heterogeneous networks

In this paper, multi-source fusion-based detection method for the security of heterogeneous network is investigated. Fusion-based detection method exploits multi-source profiles from the whole network to make decisions on whether intrusion incident happens. A game theoretic analysis method for the concerned detection strategy is presented, where the attacker and defender are thought to be rational humans and they always try their best to get their maximum payoffs. A nonzero-sum game model is established to formulate the confrontation between the defender and attacker by considering the detection threshold and attack resource allocation as their strategies. The optimal strategies are then solved by using Nash equilibrium theory. The local optimal attack allocation scheme is firstly presented for heterogeneous network, which shows that with limited resources the attacker should only launch attacks to more valuable nodes and more attack resources should be allocated to more valuable nodes also. Then a general conclusion about the existence of the Nash equilibrium is given, which indicates that the Nash equilibrium must exist despite the attacks types. After that, an iterative method is presented for the calculation of the Nash equilibrium by considering DDoS attack as an example. Numerical simulations are shown to validate our theoretical results.

Secrecy Energy Efficiency in Wireless Powered Heterogeneous Networks: A Distributed ADMM Approach

This paper investigates the physical layer security in heterogeneous networks (HetNets) supported by simultaneous wireless information and power transfer (SWIPT). We first consider a two-tier HetNet composed of a macrocell and several femtocells, where the macrocell base station (BS) serves multiple users in the presence of a malicious eavesdropper, while each femtocell BS serves a couple of Internet-of-Things (IoT) users. With regard to the energy constraint of IoT users, SWIPT is performed at the femtocell BSs, and IoT users accomplish the reception of information and energy in a time-switching manner, where information secrecy is to be protected. To enhance the secrecy performance, we inject artificial noise (AN) into the transmit beam at both macrocell and femtocell BSs, and for the sake of achieving green communications, we formulate the problem of maximizing secrecy energy efficiency while considering the fairness in a cross-tier multi-cell coordinated beamforming (MCBF) design. To handle this resulting nonconvex max–min fractional program problem, we propose an iterative algorithm by applying successive convex approximation method. Then, we further develop a decentralized solution based on alternative direction multiplier method (ADMM), which reduces the overhead of information exchange among coordinated BSs and achieves good approximation performance. Finally, simulation results demonstrate the performance of the proposed AN-aided cross-tier MCBF design and verify the validity of distributed ADMM-based approach.

Caching-Aided Physical Layer Security in Wireless Cache-Enabled Heterogeneous Networks

Caching popular contents is a promising way to offload the mobile data traffic in wireless networks, but so far the potential advantage of caching in improving physical layer security (PLS) is rarely considered. In this paper, we contribute to the design and theoretical understanding of exploiting the caching ability to improve the transmission security in a wireless heterogeneous network. Two transmission schemes, namely Symbol-level scheme and Bit-level scheme, are considered in such network to improve PLS by utilizing the content property of cached files. In Symbol-level scheme, the base station (BS) ensures the secrecy of communication by transmitting the requested file along with a pre-cached file of the user such that the eavesdropper’s channel is degraded. In Bit-level scheme, the BS encodes the requested file with a pre-cached file to fully resist the overhearing of eavesdroppers in bit level, e.g., network coding. Accordingly, the node locations of BSs, users and eavesdroppers are first modeled as mutually independent Poisson point processes and the corresponding file access protocol is developed. We consider two scenarios where each scheme is employed respectively, and then derive analytical expressions of two secrecy metrics in terms of average secrecy rate and secrecy coverage probability. Numerical results are provided to show the significant security advantages of the proposed schemes and to characterize the impacts of the cache ability and network resources on the security performance.

Joint Power Allocation and Match Access for Physical Layer Security of Heterogeneous Network

In this paper, we investigate the Physical Layer Security (PLS) of a multiple friendly jammers and multiple users wireless system in Device-to-Device (D2D) communications underlaying cellular networks with an eavesdropper. The aim is to maximize the increase of secrecy capacity of the entire system through optimal power allocation and jamming matching under various practical constraints. Firstly, the total increase secrecy capacity of D2D pairs with the help of jamming is derived, then we obtain optimal jamming power for each D2D pair and formulate the D2D pairs and jammers matching problem in the weighted bipartite graph via designing the Kuhn-Munkres (KM) algorithm. Finally, numerical results demonstrate that our scheme could greatly improve the secrecy performance of heterogeneous D2D and cellular networks.

Physical Layer Security in Heterogeneous Networks With Jammer Selection and Full-Duplex Users

In this paper, we enhance physical layer security for downlink heterogeneous networks by using friendly jammers and full-duplex users. The jammers are selected to transmit jamming signal if their interfering power on the scheduled users is below a threshold, meanwhile the scheduled users confound the eavesdroppers using artificial noise by full-duplexing. Using the tools of stochastic geometry, we derive the expressions of connection probability and secrecy probability. In particular, the locations of active jammers are modeled by a modified Poisson hole process. Determining the jammer selection threshold is further investigated for connection probability maximization subject to the security constraints. A greedy algorithm is proposed to efficiently solve this problem. The accuracy of the theoretical analysis and the efficiency of the proposed algorithm are evaluated by numerical simulations.

Physical layer security in multi-antenna cognitive heterogeneous cellular networks: a unified secrecy performance analysis

Cognitive heterogeneous cellular networks (CHCNs) are emerging as a promising approach to next-generation wireless communications owing to their seamless coverage and high network throughput. In this paper, we describe our reliance on multi-antenna technology and a secrecy transmission protocol to ensure the reliability and security of downlink underlay CHCNs. First, we introduce a two-tier CHCN model using a stochastic geometry framework, and derive the probability distribution of the indicator function for a secrecy transmission scheme. We then investigate the connection outage probability, secrecy outage probability (SOP), and transmission SOP of both primary and cognitive users under a secrecy guard scheme and a threshold-based scheme. Furthermore, we reveal some insights into the secrecy performance by properly setting the predetermined access threshold and the radius of detection region for the secrecy transmission scheme. Finally, simulation results are provided to show the influence of the antenna system, eavesdropper density, predetermined access threshold, and radius of the detection region on the reliability and security performance of a CHCN.

Genetic algorithm based bacterial foraging optimization with three-pass protocol concept for heterogeneous network security enhancement

Most of the currently available Heterogeneous protocols operate on the basis of reliable and secured communication environment. These protocols are vulnerable to modern intruding techniques that include different types of raised attacks. Routing attacks are very atrocious because of the dynamic nature of heterogeneous network Routing as well as member nodes with different Computational powers. Heterogeneous Network architectures plays a vital role in modern world communication that is virtually all communications and secret resource transactions are widely depending on the heterogeneous network architecture. GABFO has been concerned to optimize the parameters in the dynamic nature of environment for update itself by its genitival processes.TPP can afford high security, without exchange of mean value. The combination of both GABFOTPP ensures high security against attacks and leads best Qos improvement. The Planned work is an effort to improve the existing security of the heterogeneous network routing protocols, using Genetic Algorithm based Bacterial Foraging Optimization (GA-BFO) with Three-pass protocol concept against different types of attacks and it can be most useful in secured IoT Communication.