Jialin Wan¹, Muhammad Waqas^1,2, Shanshan Tu^1,*, Syed Mudassir Hussain³, Ahsan Shah², Sadaqat Ur Rehman⁴, Muhammad Hanif²

CMC-Computers, Materials & Continua, Vol.68, No.1, pp. 267-281, 2021, DOI:10.32604/cmc.2021.016260

Abstract Fog computing paradigm extends computing, communication, storage, and network resources to the network’s edge. As the fog layer is located between cloud and end-users, it can provide more convenience and timely services to end-users. However, in fog computing (FC), attackers can behave as real fog nodes or end-users to provide malicious services in the network. The attacker acts as an impersonator to impersonate other legitimate users. Therefore, in this work, we present a detection technique to secure the FC environment. First, we model a physical layer key generation based on wireless channel characteristics. To generate the secret keys between the… More >

Yonghua Huo¹, Yi Cao², Zhihao Wang¹, Yu Yan³, Zhongdi Ge³, Yang Yang^3,*

CMES-Computer Modeling in Engineering & Sciences, Vol.126, No.3, pp. 1053-1091, 2021, DOI:10.32604/cmes.2021.013045

Abstract In recent years, with the continuous development of information technology and the rapid growth of network scale, network monitoring and management become more and more important. Network traffic is an important part of network state. In order to ensure the normal operation of the network, improve the availability of the network, find network faults in time and deal with network attacks; it is necessary to detect the abnormal traffic in the network. Abnormal traffic detection is of great significance in the actual network management. Therefore, in order to improve the accuracy and efficiency of network traffic anomaly detection, this paper… More >

Hyun Kyu Shin¹, Si Woon Lee², Goo Pyo Hong³, Lee Sael², Sang Hyo Lee⁴, Ha Young Kim^5,*

CMC-Computers, Materials & Continua, Vol.66, No.3, pp. 2493-2507, 2021, DOI:10.32604/cmc.2021.014170

Abstract The demand for defect diagnoses is gradually gaining ground owing to the growing necessity to implement safe inspection methods to ensure the durability and quality of structures. However, conventional manpower-based inspection methods not only incur considerable cost and time, but also cause frequent disputes regarding defects owing to poor inspections. Therefore, the demand for an effective and efficient defect-diagnosis model for concrete structures is imminent, as the reduction in maintenance costs is significant from a long-term perspective. Thus, this paper proposes a deep learning-based image object-identification method to detect the defects of paint peeling, leakage peeling, and leakage traces that… More >

Xin Liu^*, Xiao Chen

Journal of Information Hiding and Privacy Protection, Vol.2, No.2, pp. 87-94, 2020, DOI:10.32604/jihpp.2020.09839

Abstract In recent years, with the rapid growth of generative adversarial networks (GANs), a photo-realistic face can be easily generated from a random vector. Moreover, the faces generated by advanced GANs are very realistic. It is reasonable to acknowledge that even a well-trained viewer has difficulties to distinguish artificial from real faces. Therefore, detecting the face generated by GANs is a necessary work. This paper mainly introduces some methods to detect GAN-generated fake faces, and analyzes the advantages and disadvantages of these models based on the network structure and evaluation indexes, and the results obtained in the respective data sets. On… More >

Sa’ed Abed^∗, Mohammed H. Ali^†, Mohammad Al-Shayeji^‡

Computer Systems Science and Engineering, Vol.35, No.1, pp. 21-37, 2020, DOI:10.32604/csse.2020.35.021

Abstract Today, there is a growing demand for computer vision and image processing in different areas and applications such as military surveillance, and biological and medical imaging. Edge detection is a vital image processing technique used as a pre-processing step in many computer vision algorithms. However, the presence of noise makes the edge detection task more challenging; therefore, an image restoration technique is needed to tackle this obstacle by presenting an adaptive solution. As the complexity of processing is rising due to recent high-definition technologies, the expanse of data attained by the image is increasing dramatically. Thus, increased processing power is… More >

Alberto Fernández Oliva¹, Francisco Maciá Pérez², José Vicente Berná-Martinez^2,*, Miguel Abreu Ortega³

Computer Systems Science and Engineering, Vol.34, No.3, pp. 131-144, 2019, DOI:10.32604/csse.2019.34.131

Abstract This study presents a method for the detection of outliers based on the Variable Precision Rough Set Model (VPRSM). The basis of this model is the generalisation of the standard concept of a set inclusion relation on which the Rough Set Basic Model (RSBM) is based. The primary contribution of this study is the improvement in detection quality, which is achieved due to the generalisation allowed by the classification system that allows a certain degree of uncertainty. From this method, a computationally efficient algorithm is proposed. The experiments performed with a real scenario and a comparison of the results with… More >

Jianhui Wu^1,2, Feng Huang^1,2, Wenjing Hu²,Wei He^1,2, Bing Tu^1,2, Longyuan Guo^1,2, Xianfeng Ou^1,2,*

Computer Systems Science and Engineering, Vol.33, No.5, pp. 327-334, 2018, DOI:10.32604/csse.2018.33.327

Abstract Face detection is very important in video surveillance of public safety. This paper proposed a face detection method based on the best optimization convex grouping to detect the face regions from different face shape images at actual conditions. Firstly, the basic principle of convex grouping was discussed, the main rules of convex and the structure of the convex polygons was described. And then the best optimization convex grouping algorithm of the convex polygons was designed. At last, all of the algorithms, which used the best optimization convex grouping to detect the face region on the data set of MIT single… More >

Lufeng Yuan^∗, Erlin Yao, Guangming Tan

Computer Systems Science and Engineering, Vol.33, No.2, pp. 105-113, 2018, DOI:10.32604/csse.2018.33.105

Abstract This paper proposes a machine learning based method which can detect certain events automatically and precisely in biomedical imaging. We detect one important and not well-defined event, which is called flash, in fluorescence images of Escherichia coli. Given a time series of images, first we propose a scheme to transform the event detection on region of interest (ROI) in images to a classification problem. Then with supervised human labeling data, we develop a feature selection technique to utilize support vector machine (SVM) to solve this classification problem. To reduce the time in training SVM model, a parallel version of SVM… More >

Yue Li¹, Guangquan Xu^2,3, Hequn Xian^1,*, Longlong Rao³, Jiangang Shi^4,*

Intelligent Automation & Soft Computing, Vol.25, No.3, pp. 637-647, 2019, DOI:10.31209/2019.100000118

Abstract In order to prevent the spread of Android malware and protect privacy information from being compromised, this study proposes a novel multidimensional hybrid features extraction and analysis method for Android malware detection. This method is based primarily on a multidimensional hybrid features vector by extracting the information of permission requests, API calls, and runtime behaviors. The innovation of this study is to extract greater amounts of static and dynamic features information and combine them, that renders the features vector for training completer and more comprehensive. In addition, the feature selection algorithm is used to further optimize the extracted information to… More >

Ye Wang¹, Bixin Liu², Hongjia Wu¹, Shan Zhao¹, Zhiping Cai^{1, *}, Donghui Li^{3, *}, Cheang Chak Fong⁴

CMC-Computers, Materials & Continua, Vol.65, No.1, pp. 355-367, 2020, DOI:10.32604/cmc.2020.09835

Abstract With the continuous development of e-commerce, consumers show increasing interest in posting comments on consumption experience and quality of commodities. Meanwhile, people make purchasing decisions relying on other comments much more than ever before. So the reliability of commodity comments has a significant impact on ensuring consumers’ equity and building a fair internet-trade-environment. However, some unscrupulous online-sellers write fake praiseful reviews for themselves and malicious comments for their business counterparts to maximize their profits. Those improper ways of self-profiting have severely ruined the entire online shopping industry. Aiming to detect and prevent these deceptive comments effectively, we construct a model… More >