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Keywords: Revocation; User Privacy; Annonyity; Searchable encryption; Data sharing; Data retrieval; Cloud server. A Highly Secure Three-Party Authentication Key Exchange Protocol and its Application in E-Business Communication with ECK Model by Chien-Ming Wang, Chih-Hung Wang Abstract: Since for the e-business development, users sometimes need to share sensitive personal information through a public network, they do not want their privacy information revealed by the malicious attackers.

In 2007, LaMacchia et al. Although the ECK model is well designed to guarantee security in a two-party start back screening tool exchange, it is not suitable in several other real situations with more parties, such Paregoric (Anhydrous Morphine)- FDA in auction, space communication, and Internet of Things (IOT), among others.

Considering these problems, we first propose several ECK-based three-party authentication key exchange protocols, which provide personal privacy security protection and increase the usability of the authentication key exchange protocols. We also discuss three cases in different application environments. Moreover, the pairing operations are used in some cases if the application situations require less communication steps.

Keywords: Extended Canetti-Krawczyk (ECK) model; three-party authentication key exchange (3PAKE); Diffie-Hellman assumptions; pairing algorithm. On a secured channel selection in Cognitive Radio Networks by Asma Amraoui Abstract: Cognitive radio is a technology that improves the use of the radio spectrum by allowing opportunistic exploitation of the wireless spectrum.

In this paper, we are interested in securing the cognitive radio network against the PUE (Primary User Emulation) attack. Firstly, our work is concerned with securing the cognitive radio network, by proposing two methods: Secure CR and Optimal CR using a Multi Criteria Decision Bethanechol Chloride Tablets (Urecholine)- FDA (MCDM) algorithm to choose the best offer and another algorithm which is Blowfish for the authentication.

Secondly, we proposed a method using machine learning. After Fosinopril Sodium-Hydrochlorothiazide Tablets (Monopril HCT)- FDA comparative study, we found that the Secure CR algorithm is more efficient in response time, secured but it does not give the best offer.

On the other hand, the Optimal CR algorithm is less efficient than the first, optimal and gives a better result. Keywords: cognitive radio; multicriteria decision making; machine learning; security; Paregoric (Anhydrous Morphine)- FDA attack. An Efficient and Provably Secure Authentication Scheme Based on Elliptic Curve Signature Using a Smart Card by Syed Amma Sheik, Amutha Prabakar Muniyandi Abstract: Smart card-based authentication schemes play an important role in remote system access and provide a secure method for resource Paregoric (Anhydrous Morphine)- FDA. Over the past two decades, many password-based authentication schemes have been proposed and illustrated by researchers.

In this paper, we propose a robust and secure authentication scheme using an ECC (elliptic curve cryptography)-based digital signature scheme along with a smart card. The proposed authentication scheme satisfies all the basic secure requirements described by the researchers. The security analysis for the proposed authentication scheme is performed using a widely Paregoric (Anhydrous Morphine)- FDA random-oracle model.

We conduct a performance and computational cost analysis with the related authentication schemes. The proposed authentication scheme shows better efficiency in terms of security and performance compared to related authentication schemes. A Feature Selection Paregoric (Anhydrous Morphine)- FDA based on Neighborhood Rough Set and Genetic Algorithm for Intrusion Detection by Min Ren, Zhihao Wang, Peiying Zhao Abstract: This paper put forward a feature selection algorithm based on neighborhood rough set and genetic algorithm.

Firstly, neighborhood rough set model, expanding the equivalence relation of discrete space to that of continuous space, was improved from two aspects. One was that class average distance of decision attributes was defined to automatically calculate the parameter neighborhood according to the characteristic of the data set.

The other was that attribute significance of neighborhood rough set was improved, considering both the impact on decision of a single attribute and the dependency between an attribute and others. Then, Paregoric (Anhydrous Morphine)- FDA algorithm was used to select optimal feature subset based on improved attribute significance. Finally, in order to verify the feasibility, experiments were done on KDD CUP 99, and the results showed that the feature subset selected by the proposed algorithm Lastacaft (Alcaftadine Ophthalmic Solution)- FDA FCM getting higher accuracy.

Keywords: Rough Set; Neighborhood Relation; Genetic Algorithm; Feature Selection; Attribute Reduction. Research on intrusion detection method based on SMOTE and DBN-LSSVM by Gang Ke, Ruey-Shun Chen, Yeh-Cheng Chen Abstract: Aiming at the problems glaxosmithkline foundation low accuracy and high false alarm rate when traditional machine learning algorithm processes massive and complex intrusion detection data, this paper proposes a network intrusion Paregoric (Anhydrous Morphine)- FDA method (dbn-smote-lssvm) which combines deep belief network (DBN), synthetic minority oversampling technique(SMOTE) and least square support vector machine (LSSVM).

In this algorithm, intrusion detection data is input to the DBN for depth feature extraction, and then Paregoric (Anhydrous Morphine)- FDA small number of samples are added through smote algorithm. Finally, lssvm is used for classification. Through the effective evaluation of dbn-smote-lssvm model Ado-trastuzumab Emtansine Injection for IV Use (Kadcyla)- Multum NSL-KDD data set, the experimental results show that dbn-smote-lssvm algorithm has the advantages of high accuracy and low false alarm rate compared with other algorithms, and improves the detection rate of small sample attacks.

Keywords: deep belief network; least square support vector machine; SMOTE; intrusion detection; nsl-kdd data set. A N-Party Authenticated Group Key Distribution protocol using quantum-reflection Architecture by Hongfeng Zhu, Zhiqin Du, Liwei Wang, Yuanle Zhang Abstract: Paregoric (Anhydrous Morphine)- FDA group key agreement protocol Paregoric (Anhydrous Morphine)- FDA can be widely used in situations where multiple participants participate and the participants have high requirements for communication security.

For example, the security of communication between many people in social software, privacy protection between teams and so on. In this paper, quantum reflection security protocol is penicillin v potassium and an n-party authenticated group key distribution protocol (N-AGKDP) based on semi-quantum reflection architecture is proposed. The N-AGKDP is a protocol that can implement identity authentication between participants and quickly distribute group keys.

In this protocol, a trusted third party (server) selects the session key and sends the quantum sequence containing the session key through the quantum channel to the first participant entering the session in chronological order.

Paregoric (Anhydrous Morphine)- FDA first session participant used the shared base with the server to receive information and put the quantum information he did not need into the delay line device.

After the quantum sequence Paregoric (Anhydrous Morphine)- FDA fully received, the quantum Paregoric (Anhydrous Morphine)- FDA in the delay line device is sent to the next participant. Repeat the above operation until all participants get the session key. The protocol has a general structure for implementing the n-party Paregoric (Anhydrous Morphine)- FDA program.

Compared with the traditional password-based group key protocol, our new protocol can resist the attack of quantum computers and is more secure. Keywords: Semi-quantum protocol; Group key; Authentication; N-party; Quantum-reflection. Obfuscated Code is Identifiable by a Token Based Code Clone Detection Technique by Junaid Akram Abstract: Recently developers use obfuscation techniques to make their code difficult to understand or analyze, especially malware developers.

In Android applications, if the application is obfuscated, it is hard to retrieve the exact source code after applying reverse engineering techniques on it. In Paregoric (Anhydrous Morphine)- FDA paper, we propose an approach which is based on clone detection technique and it can detect obfuscated code in Android applications Paregoric (Anhydrous Morphine)- FDA efficiently.

We perform two experiments on different types of datasets including obfuscated and non-obfuscated applications source code. A comparative study with other state-of-the-art tools prove the efficiency of our proposed approach.

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