Статьи журнала - International Journal of Wireless and Microwave Technologies
Все статьи: 532
Efficient Homomorphic Hashing Approach for Secure Reprogramming in Wireless Sensor Networks
Статья научная
While existing solutions can provide authentication services, they are insufficient for a new generation of network coding-based reprogramming protocols in wireless sensor networks. We present a security approach that is able to defend pollution attack against reprogramming protocols based on network coding. It employs a homomorphic hashing function and an identity-based aggregate signature to allow sensor nodes to check packets on-the-fly before they accept incoming encoded packets, and introduces an efficient mechanism to reduce the computation overhead at each node and to eliminate bad packets quickly. Castalia simulations show that when the 5% of the nodes in a network of 100 nodes are rogue, using our approach, the efficiency of the secure reprogramming protocol based on network coding improves almost ten-fold for a checking probability of 2%.
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Efficient Proxy Re-encryption with Private Searching in the Untrusted Cloud
Статья научная
As promising as cloud computing is, this paradigm brings forth new security and privacy challenges when operating in the untrusted cloud scenarios. In this paper, we propose a new cryptographic primitive Proxy Re-encryption with Private Searching (PRPS for short). The PRPS scheme enables the data users and owners efficiently query and access files storaged in untrusted cloud, while keeping query privacy and data privacy from the cloud providers. The concrete construction is based on proxy re-encryption, public key encryption with keyword search and the dual receiver cryptosystem. The scheme is semantically secure under the BDH assumption.
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Статья научная
The formation of P2P logical networks oblivious to the structure of physical topology results in large amount of redundant network traffic. In addition to this mismatch problem, there exists a skew in properties of the participating peers which degrade the performance of P2P networks. So the current P2P systems call for effective overlay formation taking into consideration the underlying physical network topological properties and also inbuilt heterogeneity in participating peers. The heterogeneity of peers in the network can effectively used to bias neighbor selection and improve network performance by assigning more responsibility to nodes with higher capabilities. This paper presents two techniques to solve the problems of topology mismatch and heterogeneity. The proposed methods make use of bandwidth of peers and distance measures for overlay formation in the Gnutella network. The designed systems are tested with proper analysis and simulations to verify the correctness of the methods.
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Efficient low-overhead channel estimation for 5g lens based millimeter-wave massive MIMO systems
Статья научная
Beamspace MIMO performs beam-selection which can substantially reduce the number of power-consuming radio frequency (RF) chains without perceptible performance deterioration. However, for capacity-approaching performance, accurate information of the beamspace-channel of large-size is required for beam-selection, which is contesting in case of little number of RF-chains. To overcome such problem, I proposed an efficient support-detection (SD) algorithm for channel-estimation with low pilot-overhead and short number of RF chains. The key idea of SD-algorithm is to divide the whole issue of beamspace channel-estimation into a series of sub-issues, where each of them considers only one sparse channel-component. The support of each channel component is detected reliably by deploying the sparse structure attributes of the beamspace-channel. The effect of this channel-component is eliminated from the whole channel-estimation issue. Thus, the sparse beamspace-channel can be estimated with low pilot-overhead. Simulation Results shows that the proposed schemes perform much better than the conventional compressed-sensing (CS) schemes.
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Empirical Network Performance Evaluation of Security Protocols on Operating Systems
Статья научная
Securing data transmission is currently a widely researched topic. There are numerous facades in data security. Virtual Private Network (VPN) is one such strand that provides security for data that is in motion. Performance of a network that has VPN implementation is at the forefront of network design and choice of the operating systems and cryptographic algorithms is critical to enhancing network performance. In this research undertaking, three VPN techniques, namely DES, 3DES and AES, which are commonly used to implement IPSec VPNs, are performance analyzed on test-bed setup. These are implemented on a network with Linux Fedora and a router and Windows desktop operating systems on another node. The VPN algorithms tested show that there may be performance differences when implemented with different operating system combinations.
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Enabling Trust in Single Sign-On Using DNS Based Authentication of Named Entities
Статья научная
Single Sign-On (SSO) allows the client to access multiple partner e-services through a single login session. SSO is convenient for the users as the user neither needs to set multiple login credentials nor login separately for individual services every time. SSO (single sign-on) authentication is a password-authentication approach that permits end users to login into multiple systems and websites with a single set of login credentials. SSO authentication is mainly useful for IT organizations that consist of many different commercial applications. The outstanding feature of SSO is that it gives organizations centralized control of their systems by giving different levels of access to each individual. It reduces password fatigue and increases security because users only need to remember a single username/password that grants them access to multiple systems. However, the Single Sign-on poses risks related to a single point of attack which may lead to a path for cybercrimes. This paper proposes a trust model to increase the security of Single Sign-on systems against the vulnerabilities discussed in the subsequent sections. The proposed Trust model is named as DANE-based Trust Plugin (DTP) which acts as an added security layer over DNS Based Authentication of Named entities(DANE). The DTP proposes the modified SAML XML schema which enables the DTP to counter the attacks.
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Статья научная
Cloud computing is a rapidly growing technology in this new era. Cloud is a platform where users get charged on the basis of the services and resources they have used. It enables its users to access the cloud resources from the remote locations i.e. from anywhere at any time. It needs only a working internet connection to access the cloud services. Cloud users have always been victim to the security issues and attacks which leads to the data loss. The data is not saved on the hard disk of the computer so it is highly prone to security risks. Identifying the attacks on cloud platform is a difficult task because everything on cloud is in virtual form. EDoS (Economic Denial of Sustainability) attack is a form of DDoS attacks; carried out for a long span of time and intended to put a financial burden and cause economical loss to the users of cloud. Such attacks do not exhaust the bandwidth of the user; their main aim is to put a huge financial loss or burden on the user. A technique named as SPART (Supervised Pattern Attack Recognition Technique) implemented to mitigate the EDoS attacks in cloud computing which consumes lesser energy as compared to the existing models. The experimental results have shown the less energy consumption in proposed model.
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Enhanced Techniques for Filtering of Wall Messages over Online Social Networks (OSN) User Profiles
Статья научная
Online Social Networks enables various users to connect and share their messages publicly and privately. On one hand it provides advantages to the users to connect and share but on the other hand it provides disadvantage of being attacks or post messages which contains negative or abuse words. Hence OSN provides various filtering rules for security against these wall messages. Although there are various filtering rules and classifiers implemented for the filtering of these users wall messages in popular OSN such as Twitter and Facebook. But in the proposed methodology not only filtering of these wall messages is done but the categorization of normal or negative messages are identified and hence on the basis users can be blacklisted. The proposed methodology is compared with FCM and SVM for clustering and classification of messages. This approach efficiently categorizes the messages but restricts for generating filtering rules and blacklist management. Thus the approach with FCM and J48 first initializes clustering using FCM followed by generation of rules using J48 based decision tree. Hence on the basis of the rules generated message are classified and message which doesn't contain attacks is then filtered on the basis of dictionary which contains a list of abuse words. The methodology is implemented by applying FCM and SVM and a comparison is done with FCM and J48 for the performance on the basis of accuracy to detect abnormal messages.
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Статья научная
Quantum cryptography is the most convenient resolution for information security systems that presents an ultimate approach for key distribution. Today, the most viable key distribution resolutions for information security systems are those based on quantum cryptography. It is based on the quantum rules of physics rather than the assumed computational complexity of mathematical problems. But, the initial BB84 quantum key distribution protocol which is the raw key exchange of S13 quantum key distribution protocol has weakness of disclosure of large portion of secrete key or eavesdropping. Also, it cannot make use of most of the generated random bit. This paper enhanced S13 quantum key distribution protocol by employing polarization, secrete key disclosure and non-repudiation. The use of biometric or MAC address ensures non-repudiation. The row key exchange part of the S13 quantum key distribution which is the same as BB84 is enhanced by employing polarization techniques to make use of most of the generated random bit. Then, the tentative final key generated at the end of error estimation phase should be divided into blocks, padding, inverting the last bit of each block and XORing the block to generate a totally different key from the tentative one. Also, the random bits will be from biometric or serve MAC address respectively. The enhanced S13 quantum key is evaluated using cryptanalysis which shows that the enhanced protocol ensures disclosures of large portion of secrete key to prevent eavesdropping, utilization of most of the chosen binary strings to generate strong key and safeguarding against impersonation attack.
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Enhancing Cybersecurity through Bayesian Node Profiling and Attack Classification
Статья научная
Due to the epidemic, the majority of users and businesses turned to the internet, necessitating the necessity to preserve the populace and safeguard their data. However, after being attacked, the expense of data protection runs into the millions of dollars. The phrase "Protection is better than cure" is true. The paper deals with profiling the node for safeguarding against the cyberattack. There is a lot of research on network nodes. Here, we address the requirement to profile the node before utilizing machine learning to separate the data. In order to scan the nodes for risks and save the nature of threat as a database, node profiling is being investigated. The data is then classified using a machine learning algorithm utilizing the database. This research focuses on the application of machine learning methods, specifically Gaussian Naive Bayes and Decision Trees, for the segmentation of cyberattacks in streaming data. Given the continuous nature of cyberattack data, Gaussian Naive Bayes is introduced as a suitable approach. The research methodology involves the development and comparison of these methods in classifying detected attacks. The Bayesian method is employed to classify detected attacks, emphasizing the use of Gaussian Naive Bayes due to its adaptability to streaming data. Decision Trees are also discussed and used for comparison in the results section. The research explores the theoretical foundations of these methods and their practical implementation in the context of cyberattack classification. After classification, the paper delves into the crucial task of identifying intrusions in the streaming data. The effectiveness of intrusion detection is highlighted, emphasizing the importance of minimizing false negatives and false positives in a real-world cybersecurity setting. The implementation and results section presents empirical findings based on the application of Gaussian Naive Bayes and Decision Trees to a dataset. Precision, recall, and accuracy metrics are used to evaluate the performance of these methods. The research concludes by discussing the implications of the findings and suggests that Gaussian Naive Bayes is a suitable choice for streaming data due to its adaptability and efficiency. It also emphasizes the need for continuous monitoring and detection of cyberattacks to enhance overall cybersecurity. The paper provides insights into the practical applicability of these methods and suggests future work in the field of intrusion detection.
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Enhancing the Cloud Security through RC6 and 3DES Algorithms while Achieving Low-Cost Encryption
Статья научная
Cloud computing is a cutting-edge system that's widely considered the future of data processing, making cloud computing one of the widely used platforms worldwide. Cloud computing raises problems around privacy, security, anonymity, and availability. Because of this, it is crucial that all data transfers be encrypted. The overwhelming majority of files stored on the cloud are of little to no significance while the data of certain users may be crucial. To solve the problems around security, privacy, anonymity, and availability, so we propose a novel method for protecting the confidentiality and security of data while it is being processed by a cloud platform. The primary objective of this study is to enhance the cloud security with RC6 and 3DES algorithms while attained low cost encryption, and explore variety of information safety strategies. Inside the proposed system, RC6 and 3DES algorithms have been used to enhance data security and privacy. The 3DES has been used to data with a high level of sensitivity to encrypt the key of RC6 and this method is significant improve over the status quo since it increases data security while reduce the amount of time needed for sending and receiving data. Consequently, several metrics, such as encryption time, false positive rate, and P-value, have been determined by analyzing the data. According to the findings, the suggested system attained less encryption time in different file size by securely encrypting data in a short amount of time and it gives outperformance as compared to other methods.
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Enlightenment on Computer Network Reliability From Transportation Network Reliability
Статья научная
Referring to transportation network reliability problem, five new computer network reliability definitions are proposed and discussed. They are computer network connectivity reliability, computer network time reliability, computer network capacity reliability, computer network behavior reliability and computer network potential reliability. Finally strategies are suggested to enhance network reliability.
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Enterprise Private Cloud Platforms: A Systematic Review of Key Vendors
Статья научная
Cloud computing has revolutionized the way organizations manage and deploy their Information Technology infrastructure. With an increasing emphasis on data security and residency, regulatory compliance, and the need for customization, private cloud platforms have emerged as a pivotal solution in the enterprise Information Technology landscape. This paper presents a comprehensive review of private cloud technologies, delineating their key features, advantages, and capabilities. Through an exhaustive research methodology, we explore various private cloud solutions, ranging from open-source offerings to proprietary systems. A reference architecture is formulated to provide a holistic understanding of the essential components and interactions inherent to a private cloud platform. Furthermore, 18 categories and 43 subcategories of features and capabilities for the 13 most popular private cloud solutions are identified to assist organizations in evaluating and selecting the most suitable platform based on their specific requirements. This study aims to offer valuable insights to enterprises navigating their cloud adoption journey, emphasizing the significance of making informed decisions in the rapidly evolving cloud computing domain.
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Статья научная
Wireless Sensor Network (WSN) is a group of sensors connected within a geographical area to communicate with each other through wireless media. Although WSN is very important in data collection in the world today, error may occur at any stage of data processing and transmission within WSNs due to its architecture. This study presents error detection and correction in WSNs using a proposed ‘pair wise’ Residue Number System (RNS) reverse converter in a health care delivery system. The proposed RNS reverse converter required (10n+3)_FAbit hardware resources for its implementation making it suitable for sensors. The proposed scheme outperformed Weighted Function and Base Extension algorithms and Field Programmable Analog Arrays using Kalman-filter algorithm schemes in terms of its hardware requirements.
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Estimate BER Distributions of Turbo Codes
Статья научная
Based on the union bound, formulas to estimate the BER distribution of channel codes are derived. By using these formulas, the BER for every position in the information sequence can be estimated. Appling the formulas to Turbo codes, several examples were given, and the results are also compared with simulation results. The results show that the derived formulas can give out good estimations of the BER distributions for Turbo codes. Therefore this would be helpful for the BER analysis, especially the unequal error protection analysis of Turbo codes.
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Статья научная
Dimensionality reduction is an essential ingredient of machine learning modelling that seeks to improve the performance of such models by extracting better quality features from data while removing irrelevant and redundant ones. The technique aids reduce computational load, avoiding data over-fitting, and increasing model interpretability. Recent studies have revealed that dimensionality reduction can benefit from labeled information, through joint approximation of predictors and target variables from a low-rank representation. A multiplicity of linear and non-linear dimensionality reduction techniques are proposed in the literature contingent on the nature of the domain of interest. This paper presents an evaluation of the performance of a hybrid deep learning model using feature extraction techniques while being applied to a benchmark network intrusion detection dataset. We compare the performance of linear and non-linear feature extraction methods namely, the Principal Component Analysis and Isometric Feature Mapping respectively. The Principal Component Analysis is a non-parametric classical method normally used to extract a smaller representative dataset from high-dimensional data and classifies data that is linear in nature while preserving spatial characteristics. In contrast, Isometric Feature Mapping is a representative method in manifold learning that maps high-dimensional information into a lower feature space while endeavouring to maintain the neighborhood for each data point as well as the geodesic distances present among all pairs of data points. These two approaches were applied to the CICIDS 2017 network intrusion detection benchmark dataset to extract features. The extracted features were then utilized in the training of a hybrid deep learning-based intrusion detection model based on convolutional and a bi-direction long short term memory architecture and the model performance results were compared. The empirical results demonstrated the dominance of the Principal Component Analysis as compared to Isometric Feature Mapping in improving the performance of the hybrid deep learning model in classifying network intrusions. The suggested model attained 96.97% and 96.81% in overall accuracy and F1-score, respectively, when the PCA method was used for dimensionality reduction. The hybrid model further achieved a detection rate of 97.91% whereas the false alarm rate was reduced to 0.012 with the discriminative features reduced to 48. Thus the model based on the principal component analysis extracted salient features that improved detection rate and reduced the false alarm rate.
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Evaluating the Capacities and Limitations of 5G and 4G Networks: An Analysis Approach
Статья научная
The utilization of millimeter waves in 5G technology has led to key differences in the capacities and performance of radio communications. Examining the advantages and challenges of this technology and comparing it with an established technology like 4G can provide a deeper understanding of these changes. Overall, this study conducts examinations to provide the characteristics of 5G and 4G technologies. In this study, the performance of 5G was evaluated and compared to 4G, under fair conditions, by analyzing the effect of increasing the distance of antennas, the number of users, and bandwidth on signal power, delay, throughput, channel quality, and modulation metrics. The analysis demonstrates the superiority of 5G in terms of speed and its ability to support more users compared to 4G. The higher data rates and enhanced capacity of 5G are evident in the results. However, it's worth noting that 4G offers a wider coverage area compared to 5G, making it more suitable for certain scenarios where extended coverage is essential. Additionally, it was observed that 5G signals are more susceptible to noise and obstacles compared to 4G, which can impact signal quality and coverage in certain environments. The presented results suggest that using 5G antennas in geographically limited and densely populated areas, such as rural regions, would be more cost-effective compared to using 4G antennas. This is because fewer antennas are required to serve more users without the need for extensive coverage. Additionally, numerous obstacles in urban areas pose challenges to 5G technology, thus requiring a greater number of antennas to achieve satisfactory accessibility.
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Evaluation of Performance for Wireless Sensor Networks Based on Gray Theory
Статья научная
A performance evaluation method of wireless sensor networks based on gray theory is proposed. Firstly the influence factors of performance are analyzed, and the index set in evaluation of wireless sensor networks' performance is built which include index of key performance and reliable characteristics. Based on AHP and gray theory, a model of evaluation of wireless sensor networks performance is given. Finally the results of example show that the evaluation model is rationality and feasibility.
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Evaluation the performance of DMZ
Статья научная
Local area networks are built mainly for two essential goals, the first one is to support the framework’s business functionality such as email, file transferring, procurement systems, internet browsing, and so forth. Second, these common networks should be built using secure strategies to protect their components. Recent developments in network communication have heightened the need for both secure and high performance network. However, the performance of network sometime is effected by applying security rules. Actually, network security is an essential priority for protecting applications, data, and network resources. Applying resources isolation rules are very important to prevent any possible attack. This isolation can be achieved by applying DMZ (Demilitarized Zone) design. A DMZ extremely enhance the security of a network. A DMZ is used to add an extra layer of protection to the network. It is also used to protect a private information. A DMZ should be properly configured to increase the network’s security. This work reviewed DMZ with regard to its importance, its design, and its effect on the network performance. The main focus of this work was to explore a means of assessing DMZ effectiveness related to network performance with simulation under OpNet simulator.
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Статья научная
The demand for cloud computing systems has increased tremendously in the IT sector and various business applications due to their high computation and cost-effective solutions to various computing problems. This increased demand has raised several challenges such as load balancing and security in cloud systems. Numerous approaches have been presented for load balancing but providing security and maintaining integrity and privacy remains a less explored research area. Intrusion detection systems have emerged as a promising solution to predict attacks. In this work, we develop a deep learning-based scheme that contains data pre-processing, convolution operations, BiLSTM model, attention layer, and CRF modeling. The current study employs a machine learning-based approach to detect intrusions based on the attackers' historical behavior. Deep learning algorithms were used to extract features from the image and determine the significance of dense packets to generate the salient fine-grained feature that can be used to detect malicious traffic and presents the final classification using fused features.
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