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.
In this paper, we propose a new cryptosystem based on matrix translation and Elliptic curve cryptography for developing a secure routing algorithm to provide energy efficient and secured data ...communications in Wireless Sensor Networks. Moreover, the newly proposed techniques are implemented by decomposing the process into the key generation phase, encryption phase, cluster based secure routing phase and decryption phase. For this purpose, we introduce two new tables namely space reference table and String Position based ASCII value and Prime number generation Table. Here, the Space reference table is used to assign the values for the spaces that are occurring in the sentence before the encryption and decryption process. Next, it uses the String Position based ASCII value and Prime number generation Table developed in this work to convert the strings into numerical digits and to allocate the nearest prime number for the generated numerical digits. In addition, we propose two new algorithms namely ASCII AND PRIME NUMBER based Encryption/Decryption Algorithm and a Secure Routing Algorithm using cipher text conversion and distance vectors called Matrix Translation and Elliptic Curve based Cryptosystem for Secure Routing Algorithm for performing cluster based and energy efficient secure routing. The major advantages of the proposed secure routing system include the increase in security, packet delivery ratio and overall network performance and also decrease in energy consumption and delay. This work has been implemented using NS2 simulator and Java.
Preserving the integrity of log data and using the same for forensic analysis is one of the prime concerns of cloud-oriented applications. Since log data collates sensitive information, providing ...confidentiality and privacy is of at most importance. For data auditors, maintaining the integrity of the log data is a prime concern. Existing models focus on providing models and frameworks that relies on any third-party entity or the cloud service provider (CSP) to handle the logs, which lacks in securing the integrity due to the presence of the external entities. Sole dependence on CSP is a major flaw together with a drawback, since the CSP itself is prone to data theft alliance. In this paper, we instantiate a mechanism which maintains the integrity of the log without compromising the performance efficiency of the system. The influence of machine learning classification techniques is leveraged in order to efficiently classify the log data before it is processed. Progressively the log data integrity is maintained through the proposed Propagated Chain of Log Blocks (PCLB), the Hybrid Vector Committed BST (HVCBST) and lightweight Multikey Hybrid Storage (MKHS) structures. The results of the implemented systems have proven to be efficient and tamper proof compared to the existing systems and can be easily rendered in any private or public cloud deployments.
Energy consumption and security are two important aspects of Mobile Ad Hoc Networks (MANETs). In MANET, application security can be provided using trust management, key management, firewalls and ...intrusion detection. Moreover, it is often necessary to communicate secret information in military applications where urgent and reliable communication is more important. However, most of the existing routing algorithms do not focus on the energy and security aspects while routing. Since energy and security are important criteria for reliable communication in MANET, it is essential to consider the energy and security aspects in routing algorithms. The prevention of security attacks on routing protocols and cluster based routing automatically reduces the energy consumption. Hence, we propose a new secured routing protocol called Cluster based Energy Efficient Secure Routing Algorithm (CEESRA) in this paper which is energy efficient and uses cluster based routing in which the trust scores on nodes are used to detect the intruders effectively. This routing algorithm reduces the Denial of Service attacks more efficiently by using intelligent agents for effective decision making in routing. From the experiments conducted with this trust based secured routing algorithm, it has been observed that this proposed routing algorithm not only enhances the security but also reduces the energy consumption and routing delay.
Spam is characterized as unnecessary and garbage E-mails. Due to the increasing of unsolicited E-mails, it is becoming more and more crucial for mail users to utilize a trustworthy spam E-mail ...filter. The shortcomings of spam classifier are defined by their increasing inability to manage large amounts of relevant messages and to effectively detect and effectively detect spam messages. Numerous characteristics in spam classifications are problematic. Given that selecting features is one of the most often used and successful techniques for feature reduction, it is a crucial duty in the identification of keyword content. As a result, features that are unnecessary and pointless yet potentially harm effciency would be removed. In this study, we present SGNNCNN (Semantic Graph Neural Network With CNN) as a solution to tackle the diffcult task of mail identification. By projections E-mails onto a graph and by using the SGNN-CNN model for classifications, this technique transforms the E-mail classification issue into a graph classification challenge. There is no need to integrate the word into a representation since the E-mail characteristics are produced from the semantic network. On several open databases, the technique's effectiveness is evaluated. Some few public databases were used in experiments to demonstrate the high accuracy of the proposed approach for classifying E-mails. In term of spam classification, the performance is superior to state-of-the-art deep learning-based methods.
Abstract
The keyword searching algorithm plays a vital role in cloud computing Architecture. Here, the searching technique is applied as a tool to encrypted data set which is outsourced. This ...technique works well, by either directed on multi keyword findings with exact match or one keyword search with fuzzy logic with a result of true or false. Though the existing techniques come across sensible difficultyin execution upon the encrypted data set, in the execution of multiple keyword based searching, keyword correction is difficult to achieve. To overcome with this problem, we construct a highly efficient, multiple corrections in keyword searching algorithm using ranking scheme. Multiple corrections like spell checks and errors. In the existing system of Author Wang and others scheme, they were unable to address the aforementioned problems, But in our method of multiple keyword searching technique, the Fuzzy Gramm algorithm helps to detect and handle the spelling errors by making a function call. Additionally the keywords with similar ancestry can be found by making use of stemming procedure. Alongside with thatrank keyword is used to achieve accurate checking with matched text file content. Thus the proposed approach enhances security and reliability to the maximum extent compared to the existing system.
Heterogeneous and multi-core server processors are connected across the clouds and cloud data centers in cloud networks. In such a scenario, the overall performance of the cloud system must be ...optimized for providing fast and effective services by proposing new techniques for load balancing, scheduling, secured storage and effective retrieval. Therefore in this paper, new algorithms are proposed to optimize the power and to improve the performance based on better load distribution using load balancing techniques in cloud networks. These proposed algorithms provide better performance by optimizing the processing speed, time, energy and security level using temporal reasoning. The proposed techniques have been implemented using a public cloud environment and the effectiveness of the proposed techniques are compared with other existing works and it is observed that the storage time and energy are minimized and the security is improved.
Social networks build and maintain relationships between individuals. Sentiment analysis is important in social network analysis for extracting user’s interest from product preferences based on ...reviews to determine whether it is positive, negative or neutral review. Moreover, sentiment analysis is used to predict the sentiment of users on specific service or product received by them. In this paper, a new technique called sentiment-based rating prediction method is proposed for developing a recommendation system in which the newly introduced technique is capable of mining valuable information from social user reviews in order to predict the accurate items liked by people based on their rating. In this model, a sentiment dictionary is used to calculate the sentiments of individual users on an item. Moreover, reputations of items are computed based on the three sentiments to predict and provide accurate recommendations. In order to increase the accuracy of the outcome, the
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-gram methodology is added as a new feature in syntax and semantic analysis along with support vector machines for effective classification of social media data. The main advantage of the proposed model is that it considers semantics and sentiments to predict user interest and hence provides more accurate recommendations.
Cloud databases provide facilities for large scale data storage and retrieval of distributed data. However, the current access control techniques provided in database systems for maintaining security ...are not sufficient to secure the private data stored in public cloud databases. In this paper, a new secured data storage algorithm for effective maintenance of confidential data is proposed. To perform storage and retrieval operations of data in the cloud data storage effectively, map reduce algorithms are developed in this work which performs data reduction and fast processing. In order to consider the temporal nature of documents to be retrieved, we propose a new algorithm called Temporal Secured Cloud Map Reduced Algorithm which integrates temporal constraints with map reduce algorithms and also the chaining Hill Cipher encryption algorithms which is proposed newly in this work. The main advantages of the proposed algorithm is that they reduce the processing time and maintains security effectively. The experimental results obtained from this work depict that the proposed model is optimizing cost and it ensures data security.
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