In this paper we propose a novel and efficient hardware implementation of an image watermarking system based on the Haar Discrete Wavelet Transform (DWT). DWT is used in image watermarking to hide secret pieces of information into a digital content with a good robustness. The main advantage of Haar DWT is the frequencies separation into four subbands (LL, LH, HL, and HH) which can be treated independently. This permits ensuring a better compromise between robustness and visibility factors. A Field Programmable Gate Array (FPGA) that is based on a very large scale integration architecture of the watermarking algorithm is developed to accelerate media authentication. A hardware cosimulation strategy using the Matlab-Xilinx system generator (XSG) was applied to prove the validity of the suggested implementation. The hardware cosimulation results show the effectiveness of the developed architecture in terms of visibility and robustness against several attacks. The proposed hardware system presents also a high performance in terms of the operating speed.

Mohamed Ali Hajjaji

Mohamed Gafsi

Abdessalem Ben Abdelali

Abdellatif Mtibaa

FPGA Implementation of Digital Images Watermarking System Based on Discrete Haar Wavelet Transform

&#xc1;ngel Mart&#xed;n del Rey

Lu-Xing Yang

Vasileios A. Karyotis

Mathematical Models for Malware Propagation

Paral and Devadas introduced a simple key generation scheme with a physically unclonable function (PUF) that requires no error correction, e.g., by using a fuzzy extractor. Their scheme, called a pattern matching key generation (PMKG) scheme, is based on pattern matching between auxiliary data, assigned at the enrollment in advance, and a substring of PUF output, to reconstruct a key. The PMKG scheme repeats a round operation, including the pattern matching, to derive a key with high entropy. Later, to enhance the efficiency and security, a circular PMKG (C-PMKG) scheme was proposed. However, multiple round operations in these schemes make them impractical. In this paper, we propose a single-round circular PMKG (SC-PMKG) scheme. Unlike the previous schemes, our scheme invokes the PUF only once. Hence, there is no fear of information leakage by invoking the PUF with the (partially) same input multiple times in different rounds, and, therefore, the security consideration can be simplified. Moreover, we introduce another hash function to generate a check string which ensures the correctness of the key reconstruction. The string enables us not only to defeat manipulation attacks but also to prove the security theoretically. In addition to its simple construction, the SC-PMKG scheme can use a weak PUF like the SRAM-PUF as a building block if our system is properly implemented so that the PUF is directly inaccessible from the outside, and, therefore, it is suitable for tiny devices in the IoT systems. We discuss its security and show its feasibility by simulations and experiments.

Yuichi Komano

Kazuo Ohta

Kazuo Sakiyama

Mitsugu Iwamoto

Ingrid Verbauwhede

Single-Round Pattern Matching Key Generation Using Physically Unclonable Function

Aiming to provide more information about the behaviors between groups or patterns between clusters in social networks, we propose a two-step differentially private method to release the distribution of clustering coefficients across communities. The DPLM algorithm improves a Louvain method to partition one network using an exponential mechanism. We introduce an absolute gain of modularity to sanitize neighboring communities. Otherwise, the algorithm is difficult to converge due to the randomness introduced. The DPCC algorithm charts the noisy distribution of clustering coefficients as a histogram, which presents the results in an intuitive manner. We conduct experiments on three real-world datasets to evaluate the proposed method. The experimental results indicate that the proposed method provides valuable distribution results while guaranteeing <span class="nowrap"><svg xmlns:xlink="http://www.w3.org/1999/xlink" xmlns="http://www.w3.org/2000/svg" style="vertical-align:-0.2063904pt" id="M1" height="6.1673pt" version="1.1" viewBox="-0.0498162 -5.96091 5.44961 6.1673" width="5.44961pt"><g transform="matrix(.013,0,0,-0.013,0,0)"><path id="g113-247" d="M387 375C387 402 357 448 257 448C172 448 82 404 82 326C82 289 108 255 156 241V239C85 223 23 181 23 116C23 39 89 -12 182 -12C265 -12 336 31 378 91L361 114C320 73 269 47 216 47C157 47 115 82 115 137C115 191 160 219 218 219C243 219 262 218 272 217L304 259L302 266C295 265 281 264 255 264C195 264 163 294 163 335C163 377 200 416 249 416C293 416 321 389 329 342C331 332 335 329 341 329C355 329 387 352 387 375Z"/></g></svg>-</span>differential privacy. Moreover, the DPLM algorithm can obtain better modularity for the networks.

Xiaoye Li

Jing Yang

Zhenlong Sun

Jianpei Zhang

Differentially Private Release of the Distribution of Clustering Coefficients across Communities

Due to the proliferation of mobile applications, mobile traffic identification plays a crucial role in understanding the network traffic. However, the pervasive unconcerned apps and the emerging apps pose great challenges to the mobile traffic identification method based on supervised machine learning, since such method merely identifies and discriminates several apps of interest. In this paper we propose a three-layer classifier using machine learning to identify mobile traffic in open-world settings. The proposed method has the capability of identifying traffic generated by unconcerned apps and zero-day apps; thus it can be applied in the real world. A self-collected dataset that contains 160 apps is used to validate the proposed method. The experimental results show that our classifier achieves over 98&#x25; precision and produces a much smaller number of false positives than that of the state of the art.

Shuang Zhao

Shuhui Chen

Yipin Sun

Zhiping Cai

Jinshu Su

Identifying Known and Unknown Mobile Application Traffic Using a Multilevel Classifier

Cloud services are cloud computing resources and applications deployed on the Internet or cloud computing platform, and users can access the required cloud services at any time. However, users face the diversity and complexity of quality of service (QoS) when evaluating and selecting cloud services. Therefore, it is important to study and establish an effective and objective trust model to improve user satisfaction and interaction success rate. In this paper, a model based on combining weights and gray correlation analysis is proposed. Firstly, direct trust, recommendation trust, and reputation together form a comprehensive trust, resulting in a more accurate overall trust. Second, rough set theory and analytic hierarchy process (AHP)-based method are used for the direct trust. Meanwhile, the degree of similarity recommendation trust is calculated by a gray relational analysis method. In order to ensure the accuracy of direct trust, this paper proposes a dynamic trust update mechanism. Finally, the simulation experiment is carried out to verify that the cloud services trust evaluation model (CSTEM) is more robust than the other three methods. It protects against malicious entities; at the same time, it can increase user satisfaction and interaction success rate.

Yubiao Wang

Junhao Wen

Xibin Wang

Bamei Tao

Wei Zhou

A Cloud Service Trust Evaluation Model Based on Combining Weights and Gray Correlation Analysis

About this journal

<p><em>Security and Communication Networks</em> provides a prestigious forum for the R&amp;D community in academia and industry working at the interdisciplinary nexus of next generation communications technologies for security implementations in all network layers.</p>


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<p>Chief Editor Dr Roberto Di Pietro&nbsp;is a Full Professor of Computer Science at KAUST, Saudi Arabia. His research interests include AI-based cybersecurity, distributed systems security, cloud security, and wireless security.&nbsp;</p>


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Security and Communication Networks - Table of contents 2019

Security and Communication Networks | Hindawi

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