Classical cryptography is the process of hiding information and it manages the secret knowledge by encrypting the plain text message through the translation of it to an unintelligible message. Quantum cryptography is also hiding the plain text through encryption and it works based on the law of quantum physics for providing absolute security of data communication. It uses the idea of quantum mechanics to gadget a cryptographic system and the key problems are solved by intrusion detection including the Eavesdropping detection using quantum expertise. On the contrary, principles of secured communication protocol systems are demonstrated with classical quantum cryptography where the keys are distributed securely and are applied in quantum key distribution as well. In this paper, we provide a survey of works on classical cryptography and quantum cryptography and compare them with respect to time, security level and the classification of the data. Moreover, we perform a concise analysis of perspective classical cryptography and the conception of Quantum cryptography with various protocols and highlight the benefits of classical cryptography and Quantum cryptography in different applications. Finally, we provide a set of recommendations for selecting the suitable encryption model for securing the communication.

Abstract A clinical decision support system is a computer-based system that is designed to assist healthcare providers with clinical decision-making by analyzing electronic health records and other healthcare information systems to provide real-time support to clinicians at the point of care. A novel classification framework for clinical datasets in which the relevant features are selected by ranking them based on Fisher’s Score and a wrapper-based Satin Bower Bird Optimization algorithm with the combination of accuracy, G-mean and F-Score measured by support vector machine (SVM) as the fitness function is proposed. The classification is performed using an SVM classifier in which the hyperparameters of the SVM classifier are optimized using the Satin Bower Bird Optimization that improves the classification performance. In the context of statistical analysis, the research undergoes a non-parametric Friedman Test. This study selects relevant attributes from three clinical datasets from the Machine Learning Repository maintained by the University of California Irvine and achieved an accuracy of 86% for the Breast Cancer Wisconsin (Diagnostic) dataset, 89% for the Diabetic Retinopathy Debrecen dataset, and 91% for the EEG Eye State dataset respectively. When compared with other machine learning classifiers the proposed approach performed well with feature selection compared with other machine learning classifiers.

Abstract Secure routing and communication with confidentiality based on encryption of texts in multiple natural languages are challenging issues in wireless sensor networks which are widely used in recent applications. The existing works on Elliptic Curve Cryptography based secured routing algorithms are focused only on the encryption and decryption of single language text encrypted over a Prime finite field. In this article, a new algorithm called Multi‐Language ECC encrypted Secure Routing algorithm with trust management is proposed, in order to ensure confidentiality and integrity which focuses on the encryption of plain text using Riemann's zeta function and Elliptic Curve Cryptography for improving the key strength which is applied for encryption over a range of multi languages namely Tamil, English, Hindi French and German which are supported by Unicode and routing the text security. From the experiments conducted using the proposed multi‐lingual encryption algorithm with network routing, we prove that the suggested method provides greater security than the current secure routing algorithms due to the use of Zeta function and Gamma function with ECC key and trust management. but also boasts reduced complexity compared to other existing multi‐lingual encryption algorithms.

Summary Trust‐aware clustering plays a vital role in addressing the security issues faced by mobile ad‐hoc networks (MANETs). The trust worthiness of each participated node is to be estimated for ensuring the secure communications in MANETs. In this article, we propose a new trust‐based secure energy‐aware clustering (TSEAC) model to mitigate the malicious nodes from the network and form secure energy aware clusters with a cluster head which is more stable and trustworthy. Moreover, two novel algorithms namely energy‐efficient trust‐aware secure clustering algorithm and filtering untrustworthy recommendation (FUR) algorithm are proposed in this work. Here, the trust value of a node is measured by both direct trust estimation and indirect trust estimation methods using the Beta distribution technique. The trust value of the node is estimated in terms of the behavior of the node. The role of the FUR algorithm is to enhance the clustering process by mitigating the trust‐distortion attack. Thus, from the simulation results it is observed that the proposed work, TSEAC outperforms in improving the lifetime of the network by 38% than the other existing work such as CBTRP, AOTDV, and CBRP. Furthermore, TSEAC shows an improvement of 20% to 24% when compared to CBTRP, 27% to 31% in contrast to AOTDV and 35% to 42% superior to CBRP in terms of packet delivery ratio. Similarly, TSEAC shows 22% to 26%, 28% to 33%, and 34% to 38% better throughput in contrast to CBTRP, AOTDV, and CBRP, respectively.

Intrusion detection systems assume a noteworthy job in the provision of security in wireless Sensor networks. The existing intrusion detection systems focus only on the detection of the known types of attacks. However, it neglects to recognise the new types of attacks, which are introduced by malicious users leading to vulnerability and information loss in the network. In order to address this challenge, a new intrusion detection system, which detects the known and unknown types of attacks using an intelligent decision tree classification algorithm, has been proposed. For this purpose, a novel feature selection algorithm named dynamic recursive feature selection algorithm, which selects an optimal number of features from the data set is proposed. In addition, an intelligent fuzzy temporal decision tree algorithm is also proposed by extending the decision tree algorithm and integrated with convolution neural networks to detect the intruders effectively. The experimental analysis carried out using KDD cup data set and network trace data set demonstrates the effectiveness of this proposed approach. It proved that the false positive rate, energy consumption, and delay are reduced in the proposed work. In addition, the proposed system increases the network performance through increased packet delivery ratio.

Summary In cloud environment, the importance of security for the outsourced data has increased much, since the data is maintained and controlled by the semi‐trusted third‐party cloud providers. Data Security is one of the major factors to be considered in group data sharing. Using the secret key, the entire file is encrypted directly in a conventional security framework; however, in a cloud‐based environment for group mechanism, this framework cannot be applied as there is a problem of key distribution. This research paper proposes an efficient hierarchical‐based group key mechanism for a cloud‐based environment. This proposed system relies on Key Distribution Server (KDS), which performs cryptographic key operations for securing the data in the cloud. Also, this system uses logical key hierarchy (LKH) protocol to maintain hierarchical tree for scalability. The group key is generated using the group member secret values and a secret value assigned by the KDS server. Performance analysis of this system shows that the proposed key management system is more efficient and much suitable for cloud environment.

Summary There are lot of research works carried out in the field of Information Technology (IT), which have more impact on the education throughout the world. The main contribution from IT in today's education world is e‐learning. There are several institutions implemented the mechanism and several technologies of e‐learning in different countries. E‐learning provides more flexibility in education. Although there are several organizations and institutions had come up with the e‐learning system, the cost of investment involved for the infrastructure setup is much higher for e‐learning applications. Cloud Computing is one of the important technology, which provides different services, which plays a vital role in the education domain and e‐learning mechanism. In addition, the security factor in sharing the content is also much important nowadays as the content is shared among different countries with multiple source environments. In this paper, cloud‐based e‐learning is implemented using access control mechanism, which prevents the cloud resources from illegal user access. The key management schemes combined with access control technique is discussed for secure content sharing and to protect the e‐learning environment. The traditional e‐learning mechanism is compared with the cloud e‐learning for the better understanding of the cloud usage and advantages. Findings indicate that cloud‐based e‐learning utilizes cloud services in a secure way and also more flexible and scalable in accessing the e‐learning content.

Vehicular ad hoc networks (VANETs) are an important communication paradigm in modern-day mobile computing for exchanging live messages regarding traffic congestion, weather conditions, road conditions, and targeted location-based advertisements to improve the driving comfort. In such environments, security and intelligent decision making are two important challenges needed to be addressed. In this paper, a trusted authority (TA) is designed to provide a variety of online premium services to customers through VANETs. Therefore, it is important to maintain the confidentiality and authentication of messages exchanged between the TA and the VANET nodes. Hence, we address the security problem by focusing on the scenario where the TA classifies the users into primary, secondary, and unauthorized users. In this paper, first, we present a dual authentication scheme to provide a high level of security in the vehicle side to effectively prevent the unauthorized vehicles entering into the VANET. Second, we propose a dual group key management scheme to efficiently distribute a group key to a group of users and to update such group keys during the users' join and leave operations. The major advantage of the proposed dual key management is that adding/revoking users in the VANET group can be performed in a computationally efficient manner by updating a small amount of information. The results of the proposed dual authentication and key management scheme are computationally efficient compared with all other existing schemes discussed in literature, and the results are promising.

Domain - specific search focuses on one area of knowledge. Applying broad based ranking algorithms to vertical search domains is not desirable. The broad based ranking model builds upon the data from multiple domains existing on the web. Vertical search engines attempt to use a focused crawler that index only relevant web pages to a predefined topic. With Ranking Adaptation Model, one can adapt an existing ranking model of a unique new domain. The binary classifiers classify the members of a given set of objects into two groups on the basis of whether they have some property or not. If it is property of relevancy, it is returned to the search query of that particular domain vertical. Sponsored ads are then placed alongside the organic search results and they are ranked with the help of bid, budget and quality score. The ad with the highest bid is placed first in the ad listings. Later, the ad with a maximum quality score is found by click through logs which is replaced in first position. Thus, both organic search and sponsored ads are returned for the specific domain, making it easy for the users to get access to real time ads and connect directly with advertisers as well as to get information on the search query.

Designing a centralised group key management with minimal computation complexity to support dynamic secure multicast communication is a challenging issue in secure multimedia multicast. In this study, the authors propose a Chinese remainder theorem‐based group key management scheme that drastically reduces computation complexity of the key server. The computation complexity of key server is reduced to O (1) in this proposed algorithm. Moreover, the computation complexity of group member is also minimised by performing one modulo division operation when a user join or leave operation is performed in a multicast group. The proposed algorithm has been implemented and tested using a key‐star‐based key management scheme and has been observed that this proposed algorithm reduces the computation complexity significantly.

The traditional process of focused web crawler is to harvest a collection of web documents that are focused on the topical subspaces. The intricacy of focused crawlers is identifying the next most important and relevant link to follow. Focused Crawlers mostly rely on probabilistic models for predicting the relevancy of the documents. The Web documents are well characterized by the hypertext and the hypertext can be used to determine the relevance of the document to the search domain. The semantics of the link characterizes the semantics of the document referred. In this article, a novel, and distinctive focused crawler named LSCrawler has been proposed. This LSCrawler system retrieves documents by speculating the relevancy of the document based on the keywords in the link and the surrounding text of the link. The relevancy of the documents is reckoned measuring the semantic similarity between the keywords in the link and the taxonomy hierarchy of the specific domain. The system exhibits better recall as it exploits the semantic of the keywords in the link.