Zeyong Sun¹, Guo Ran², Zilong Jin^1,3,*

Journal on Internet of Things, Vol.4, No.2, pp. 99-111, 2022, DOI:10.32604/jiot.2022.037416 - 28 March 2023

Abstract Intrusion detection is a hot field in the direction of network security. Classical intrusion detection systems are usually based on supervised machine learning models. These offline-trained models usually have better performance in the initial stages of system construction. However, due to the diversity and rapid development of intrusion techniques, the trained models are often difficult to detect new attacks. In addition, very little noisy data in the training process often has a considerable impact on the performance of the intrusion detection system. This paper proposes an intrusion detection system based on active incremental learning with… More >

J. Pavithra^*, S. Selvakumarasamy

Journal of Cyber Security, Vol.4, No.3, pp. 135-151, 2022, DOI:10.32604/jcs.2022.031889 - 01 February 2023

Abstract Machine learning (ML) is often used to solve the problem of malware detection and classification, and various machine learning approaches are adapted to the problem of malware classification; still acquiring poor performance by the way of feature selection, and classification. To address the problem, an efficient novel algorithm for adaptive feature-centered XG Boost Ensemble Learner Classifier “AFC-XG Boost” is presented in this paper. The proposed model has been designed to handle varying data sets of malware detection obtained from Kaggle data set. The model turns the XG Boost classifier in several stages to optimize performance.… More >

Yok-Yen Nguwi^*

Journal on Artificial Intelligence, Vol.4, No.3, pp. 189-199, 2022, DOI:10.32604/jai.2022.034064 - 01 December 2022

Abstract The departure of good employee incurs direct and indirect cost and impacts for an organization. The direct cost arises from hiring to training of the relevant employee. The replacement time and lost productivity affect the running of business processes. This work presents the use of ensemble classifier to identify important attributes that affects attrition significantly. The data consists of attributes related to job function, education level, satisfaction towards work and working relationship, compensation, and frequency of business travel. Both bagging and boosting classifiers were used for testing. The results show that the selected features (nine More >

Doaa Sami Khafaga¹, El-Sayed M. El-kenawy^2,3, Faten Khalid Karim^1,*, Sameer Alshetewi⁴, Abdelhameed Ibrahim⁵, Abdelaziz A. Abdelhamid^6,7

CMC-Computers, Materials & Continua, Vol.73, No.3, pp. 5771-5788, 2022, DOI:10.32604/cmc.2022.032229 - 28 July 2022

Abstract Metamaterial Antennas are a type of antenna that uses metamaterial to enhance performance. The bandwidth restriction associated with small antennas can be solved using metamaterial antennas. Machine learning is gaining popularity as a way to improve solutions in a range of fields. Machine learning approaches are currently a big part of current research, and they’re likely to be huge in the future. The model utilized determines the accuracy of the prediction in large part. The goal of this paper is to develop an optimized ensemble model for forecasting the metamaterial antenna’s bandwidth and gain. The… More >

Gürcan Çetin^*

CMC-Computers, Materials & Continua, Vol.73, No.3, pp. 4519-4539, 2022, DOI:10.32604/cmc.2022.031734 - 28 July 2022

Abstract Recently, machine learning algorithms have been used in the detection and classification of network attacks. The performance of the algorithms has been evaluated by using benchmark network intrusion datasets such as DARPA98, KDD’99, NSL-KDD, UNSW-NB15, and Caida DDoS. However, these datasets have two major challenges: imbalanced data and high-dimensional data. Obtaining high accuracy for all attack types in the dataset allows for high accuracy in imbalanced datasets. On the other hand, having a large number of features increases the runtime load on the algorithms. A novel model is proposed in this paper to overcome these… More >

Muhammad Waqas^1,3,*, Shehr Bano², Fatima Hassan², Shanshan Tu¹, Ghulam Abbas², Ziaul Haq Abbas⁴

CMC-Computers, Materials & Continua, Vol.73, No.3, pp. 4489-4499, 2022, DOI:10.32604/cmc.2022.029539 - 28 July 2022

Abstract Cyber-physical wireless systems have surfaced as an important data communication and networking research area. It is an emerging discipline that allows effective monitoring and efficient real-time communication between the cyber and physical worlds by embedding computer software and integrating communication and networking technologies. Due to their high reliability, sensitivity and connectivity, their security requirements are more comparable to the Internet as they are prone to various security threats such as eavesdropping, spoofing, botnets, man-in-the-middle attack, denial of service (DoS) and distributed denial of service (DDoS) and impersonation. Existing methods use physical layer authentication (PLA), the… More >

Zia Ullah¹, Muhammad Ismail Mohmand¹, Sadaqat ur Rehman^2,*, Muhammad Zubair³, Maha Driss⁴, Wadii Boulila⁵, Rayan Sheikh², Ibrahim Alwawi⁶

CMC-Computers, Materials & Continua, Vol.73, No.3, pp. 4465-4487, 2022, DOI:10.32604/cmc.2022.029101 - 28 July 2022

Abstract Facial expression recognition has been a hot topic for decades, but high intraclass variation makes it challenging. To overcome intraclass variation for visual recognition, we introduce a novel fusion methodology, in which the proposed model first extract features followed by feature fusion. Specifically, RestNet-50, VGG-19, and Inception-V3 is used to ensure feature learning followed by feature fusion. Finally, the three feature extraction models are utilized using Ensemble Learning techniques for final expression classification. The representation learnt by the proposed methodology is robust to occlusions and pose variations and offers promising accuracy. To evaluate the efficiency More >

Yong-Woon Kim¹, Yung-Cheol Byun^2,*, Dong Seog Han³, Dalia Dominic¹, Sibu Cyriac¹

CMC-Computers, Materials & Continua, Vol.73, No.3, pp. 4743-4762, 2022, DOI:10.32604/cmc.2022.028632 - 28 July 2022

Abstract A wide range of camera apps and online video conferencing services support the feature of changing the background in real-time for aesthetic, privacy, and security reasons. Numerous studies show that the Deep-Learning (DL) is a suitable option for human segmentation, and the ensemble of multiple DL-based segmentation models can improve the segmentation result. However, these approaches are not as effective when directly applied to the image segmentation in a video. This paper proposes an Adaptive N-Frames Ensemble (AFE) approach for high-movement human segmentation in a video using an ensemble of multiple DL models. In contrast… More >

Aiming Wu¹, Shanshan Tu^1,*, Muhammad Wagas^1,2,3, Yongjie Yang¹, Yihe Zhang¹, Xuetao Bai¹

Journal of Information Hiding and Privacy Protection, Vol.4, No.1, pp. 25-39, 2022, DOI:10.32604/jihpp.2022.029922 - 17 June 2022

Abstract With the rapid development of the Internet of Things (IoT), all kinds of data are increasing exponentially. Data storage and computing on cloud servers are increasingly restricted by hardware. This has prompted the development of fog computing. Fog computing is to place the calculation and storage of data at the edge of the network, so that the entire Internet of Things system can run more efficiently. The main function of fog computing is to reduce the burden of cloud servers. By placing fog nodes in the IoT network, the data in the IoT devices can… More >

Vasumathi Devi Majety¹, N. Sharmili², Chinmaya Ranjan Pattanaik³, E. Laxmi Lydia⁴, Subhi R. M. Zeebaree⁵, Sarmad Nozad Mahmood⁶, Ali S. Abosinnee⁷, Ahmed Alkhayyat^8,*

CMC-Computers, Materials & Continua, Vol.73, No.2, pp. 4393-4406, 2022, DOI:10.32604/cmc.2022.031109 - 16 June 2022

Abstract Histopathology is the investigation of tissues to identify the symptom of abnormality. The histopathological procedure comprises gathering samples of cells/tissues, setting them on the microscopic slides, and staining them. The investigation of the histopathological image is a problematic and laborious process that necessitates the expert’s knowledge. At the same time, deep learning (DL) techniques are able to derive features, extract data, and learn advanced abstract data representation. With this view, this paper presents an ensemble of handcrafted with deep learning enabled histopathological image classification (EHCDL-HIC) model. The proposed EHCDL-HIC technique initially performs Weiner filtering based… More >