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  • Open Access

    ARTICLE

    A Robust Approach for Multi Classification-Based Intrusion Detection through Stacking Deep Learning Models

    Samia Allaoua Chelloug*

    CMC-Computers, Materials & Continua, Vol.79, No.3, pp. 4845-4861, 2024, DOI:10.32604/cmc.2024.051539

    Abstract Intrusion detection is a predominant task that monitors and protects the network infrastructure. Therefore, many datasets have been published and investigated by researchers to analyze and understand the problem of intrusion prediction and detection. In particular, the Network Security Laboratory-Knowledge Discovery in Databases (NSL-KDD) is an extensively used benchmark dataset for evaluating intrusion detection systems (IDSs) as it incorporates various network traffic attacks. It is worth mentioning that a large number of studies have tackled the problem of intrusion detection using machine learning models, but the performance of these models often decreases when evaluated on… More >

  • Open Access

    ARTICLE

    A Dual Domain Robust Reversible Watermarking Algorithm for Frame Grouping Videos Using Scene Smoothness

    Yucheng Liang1,2,*, Ke Niu1,2,*, Yingnan Zhang1,2, Yifei Meng1,2

    CMC-Computers, Materials & Continua, Vol.79, No.3, pp. 5143-5174, 2024, DOI:10.32604/cmc.2024.051364

    Abstract The proposed robust reversible watermarking algorithm addresses the compatibility challenges between robustness and reversibility in existing video watermarking techniques by leveraging scene smoothness for frame grouping videos. Grounded in the H.264 video coding standard, the algorithm first employs traditional robust watermark stitching technology to embed watermark information in the low-frequency coefficient domain of the U channel. Subsequently, it utilizes histogram migration techniques in the high-frequency coefficient domain of the U channel to embed auxiliary information, enabling successful watermark extraction and lossless recovery of the original video content. Experimental results demonstrate the algorithm’s strong imperceptibility, with… More >

  • Open Access

    ARTICLE

    A Distributionally Robust Optimization Scheduling Model for Regional Integrated Energy Systems Considering Hot Dry Rock Co-Generation

    Hao Qi1, Mohamed Sharaf2, Andres Annuk3, Adrian Ilinca4, Mohamed A. Mohamed5,*

    CMES-Computer Modeling in Engineering & Sciences, Vol.140, No.2, pp. 1387-1404, 2024, DOI:10.32604/cmes.2024.048672

    Abstract Hot dry rock (HDR) is rich in reserve, widely distributed, green, low-carbon, and has broad development potential and prospects. In this paper, a distributionally robust optimization (DRO) scheduling model for a regionally integrated energy system (RIES) considering HDR co-generation is proposed. First, the HDR-enhanced geothermal system (HDR-EGS) is introduced into the RIES. HDR-EGS realizes the thermoelectric decoupling of combined heat and power (CHP) through coordinated operation with the regional power grid and the regional heat grid, which enhances the system wind power (WP) feed-in space. Secondly, peak-hour loads are shifted using price demand response guidance More >

  • Open Access

    ARTICLE

    Robust Information Hiding Based on Neural Style Transfer with Artificial Intelligence

    Xiong Zhang1,2, Minqing Zhang1,2,3,*, Xu An Wang1,2,3, Wen Jiang1,2, Chao Jiang1,2, Pan Yang1,4

    CMC-Computers, Materials & Continua, Vol.79, No.2, pp. 1925-1938, 2024, DOI:10.32604/cmc.2024.050899

    Abstract This paper proposes an artificial intelligence-based robust information hiding algorithm to address the issue of confidential information being susceptible to noise attacks during transmission. The algorithm we designed aims to mitigate the impact of various noise attacks on the integrity of secret information during transmission. The method we propose involves encoding secret images into stylized encrypted images and applies adversarial transfer to both the style and content features of the original and embedded data. This process effectively enhances the concealment and imperceptibility of confidential information, thereby improving the security of such information during transmission and… More >

  • Open Access

    ARTICLE

    Byzantine Robust Federated Learning Scheme Based on Backdoor Triggers

    Zheng Yang, Ke Gu*, Yiming Zuo

    CMC-Computers, Materials & Continua, Vol.79, No.2, pp. 2813-2831, 2024, DOI:10.32604/cmc.2024.050025

    Abstract Federated learning is widely used to solve the problem of data decentralization and can provide privacy protection for data owners. However, since multiple participants are required in federated learning, this allows attackers to compromise. Byzantine attacks pose great threats to federated learning. Byzantine attackers upload maliciously created local models to the server to affect the prediction performance and training speed of the global model. To defend against Byzantine attacks, we propose a Byzantine robust federated learning scheme based on backdoor triggers. In our scheme, backdoor triggers are embedded into benign data samples, and then malicious More >

  • Open Access

    ARTICLE

    Upper and Lower Bounds of the α-Universal Triple I Method for Unified Interval Implications

    Yiming Tang1,2, Jianwei Gao1,*, Yifan Huang1

    CMC-Computers, Materials & Continua, Vol.79, No.1, pp. 1063-1088, 2024, DOI:10.32604/cmc.2024.049341

    Abstract The α-universal triple I (α-UTI) method is a recognized scheme in the field of fuzzy reasoning, which was proposed by our research group previously. The robustness of fuzzy reasoning determines the quality of reasoning algorithms to a large extent, which is quantified by calculating the disparity between the output of fuzzy reasoning with interference and the output without interference. Therefore, in this study, the interval robustness (embodied as the interval stability) of the α-UTI method is explored in the interval-valued fuzzy environment. To begin with, the stability of the α-UTI method is explored for the… More >

  • Open Access

    ARTICLE

    Robust Malicious Executable Detection Using Host-Based Machine Learning Classifier

    Khaled Soliman1,*, Mohamed Sobh2, Ayman M. Bahaa-Eldin2

    CMC-Computers, Materials & Continua, Vol.79, No.1, pp. 1419-1439, 2024, DOI:10.32604/cmc.2024.048883

    Abstract The continuous development of cyberattacks is threatening digital transformation endeavors worldwide and leads to wide losses for various organizations. These dangers have proven that signature-based approaches are insufficient to prevent emerging and polymorphic attacks. Therefore, this paper is proposing a Robust Malicious Executable Detection (RMED) using Host-based Machine Learning Classifier to discover malicious Portable Executable (PE) files in hosts using Windows operating systems through collecting PE headers and applying machine learning mechanisms to detect unknown infected files. The authors have collected a novel reliable dataset containing 116,031 benign files and 179,071 malware samples from diverse… More >

  • Open Access

    ARTICLE

    Robust Color Fastness of Dyed Silk Fibroin Film By Coupling Modification Dyeing with Aniline Diazonium Salt

    ZONGQIAN WANG, HAIWEI YANG, JIAN XING, ZHI LIU*

    Journal of Polymer Materials, Vol.36, No.2, pp. 149-159, 2019, DOI:10.32381/JPM.2019.36.02.4

    Abstract Colored silk fibroin (SF) film can change the proportion of transmitted light composition and therefore shows potential application in optoelectronics area. However, few methods are reported for the preparation of colored SF film with good color fastness. Herein, colored SF film was prepared by coupling modification dyeing (CM-dyeing) of SF tyrosine residues in solution. SF film shows excellent color fastness due to the formation of the azo covalent bond formed by electrophilic substitution reaction between SF tyrosine residues and aniline diazonium salt. Furthermore, compared with the undyed film, the dyed SF film is effectively preserved More >

  • Open Access

    ARTICLE

    Video Summarization Approach Based on Binary Robust Invariant Scalable Keypoints and Bisecting K-Means

    Sameh Zarif1,2,*, Eman Morad1, Khalid Amin1, Abdullah Alharbi3, Wail S. Elkilani4, Shouze Tang5

    CMC-Computers, Materials & Continua, Vol.78, No.3, pp. 3565-3583, 2024, DOI:10.32604/cmc.2024.046185

    Abstract Due to the exponential growth of video data, aided by rapid advancements in multimedia technologies. It became difficult for the user to obtain information from a large video series. The process of providing an abstract of the entire video that includes the most representative frames is known as static video summarization. This method resulted in rapid exploration, indexing, and retrieval of massive video libraries. We propose a framework for static video summary based on a Binary Robust Invariant Scalable Keypoint (BRISK) and bisecting K-means clustering algorithm. The current method effectively recognizes relevant frames using BRISK… More >

  • Open Access

    ARTICLE

    A Robust Method of Bipolar Mental Illness Detection from Facial Micro Expressions Using Machine Learning Methods

    Ghulam Gilanie1,*, Sana Cheema1, Akkasha Latif1, Anum Saher1, Muhammad Ahsan1, Hafeez Ullah2, Diya Oommen3

    Intelligent Automation & Soft Computing, Vol.39, No.1, pp. 57-71, 2024, DOI:10.32604/iasc.2024.041535

    Abstract Bipolar disorder is a serious mental condition that may be caused by any kind of stress or emotional upset experienced by the patient. It affects a large percentage of people globally, who fluctuate between depression and mania, or vice versa. A pleasant or unpleasant mood is more than a reflection of a state of mind. Normally, it is a difficult task to analyze through physical examination due to a large patient-psychiatrist ratio, so automated procedures are the best options to diagnose and verify the severity of bipolar. In this research work, facial micro-expressions have been… More >

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