Weifei Wang¹, Jinguo Li^1,*, Na Zhao², Min Liu¹

CMC-Computers, Materials & Continua, Vol.76, No.1, pp. 471-487, 2023, DOI:10.32604/cmc.2023.039733

Abstract With the advancement of network communication technology, network traffic shows explosive growth. Consequently, network attacks occur frequently. Network intrusion detection systems are still the primary means of detecting attacks. However, two challenges continue to stymie the development of a viable network intrusion detection system: imbalanced training data and new undiscovered attacks. Therefore, this study proposes a unique deep learning-based intrusion detection method. We use two independent in-memory autoencoders trained on regular network traffic and attacks to capture the dynamic relationship between traffic features in the presence of unbalanced training data. Then the original data is fed into the triplet network… More >

Xiong Xu¹, Chun Zhou^2,*, Chenggang Wang¹, Xiaoyan Zhang², Hua Meng²

Intelligent Automation & Soft Computing, Vol.37, No.1, pp. 815-831, 2023, DOI:10.32604/iasc.2023.034656

Abstract Clustering analysis is one of the main concerns in data mining. A common approach to the clustering process is to bring together points that are close to each other and separate points that are away from each other. Therefore, measuring the distance between sample points is crucial to the effectiveness of clustering. Filtering features by label information and measuring the distance between samples by these features is a common supervised learning method to reconstruct distance metric. However, in many application scenarios, it is very expensive to obtain a large number of labeled samples. In this paper, to solve the clustering… More >

Nagwa Elaraby^*, Sherif Barakat, Amira Rezk

CMC-Computers, Materials & Continua, Vol.74, No.1, pp. 1837-1854, 2023, DOI:10.32604/cmc.2023.032288

Abstract Deep metric learning is one of the recommended methods for the challenge of supporting few/zero-shot learning by deep networks. It depends on building a Siamese architecture of two homogeneous Convolutional Neural Networks (CNNs) for learning a distance function that can map input data from the input space to the feature space. Instead of determining the class of each sample, the Siamese architecture deals with the existence of a few training samples by deciding if the samples share the same class identity or not. The traditional structure for the Siamese architecture was built by forming two CNNs from scratch with randomly… More >

S. Prasanna Bharathi^1,2, S. Srinivasan^1,*, G. Chamundeeswari¹, B. Ramesh¹

Computer Systems Science and Engineering, Vol.45, No.1, pp. 579-594, 2023, DOI:10.32604/csse.2023.027592

Abstract Now a days, Remote Sensing (RS) techniques are used for earth observation and for detection of soil types with high accuracy and better reliability. This technique provides perspective view of spatial resolution and aids in instantaneous measurement of soil’s minerals and its characteristics. There are a few challenges that is present in soil classification using image enhancement such as, locating and plotting soil boundaries, slopes, hazardous areas, drainage condition, land use, vegetation etc. There are some traditional approaches which involves few drawbacks such as, manual involvement which results in inaccuracy due to human interference, time consuming, inconsistent prediction etc. To… More >

Yujiang Li^1,2, Chun Ding^1,2, Zhili Zhou^1,2,*

Journal of New Media, Vol.4, No.1, pp. 51-58, 2022, DOI:10.32604/jnm.2022.028775

Abstract With the development of new media technology, vehicle matching plays a further significant role in video surveillance systems. Recent methods explored the vehicle matching based on the feature extraction. Meanwhile, similarity metric learning also has achieved enormous progress in vehicle matching. But most of these methods are less effective in some realistic scenarios where vehicles usually be captured in different times. To address this cross-domain problem, we propose a cross-domain similarity metric learning method that utilizes the GAN to generate vehicle images with another domain and propose the two-channel Siamese network to learn a similarity metric from both domains (i.e.,… More >

Runsheng Wang¹, Hefei Ling^1,*, Ping Li¹, Yuxuan Shi¹, Lei Wu¹, Jialie Shen²

CMC-Computers, Materials & Continua, Vol.68, No.3, pp. 3045-3060, 2021, DOI:10.32604/cmc.2021.017275

Abstract Gait recognition is a biometric technique that captures human walking pattern using gait silhouettes as input and can be used for long-term recognition. Recently proposed video-based methods achieve high performance. However, gait covariates or walking conditions, i.e., bag carrying and clothing, make the recognition of intra-class gait samples hard. Advanced methods simply use triplet loss for metric learning, which does not take the gait covariates into account. For alleviating the adverse influence of gait covariates, we propose cross walking condition constraint to explicitly consider the gait covariates. Specifically, this approach designs center-based and pair-wise loss functions to decrease discrepancy of… More >

Mo Chen¹, Xiaojuan Wang^{1, *}, Mingshu He¹, Lei Jin¹, Khalid Javeed², Xiaojun Wang³

CMC-Computers, Materials & Continua, Vol.64, No.2, pp. 941-959, 2020, DOI:10.32604/cmc.2020.09802

Abstract Attacks on websites and network servers are among the most critical threats in network security. Network behavior identification is one of the most effective ways to identify malicious network intrusions. Analyzing abnormal network traffic patterns and traffic classification based on labeled network traffic data are among the most effective approaches for network behavior identification. Traditional methods for network traffic classification utilize algorithms such as Naive Bayes, Decision Tree and XGBoost. However, network traffic classification, which is required for network behavior identification, generally suffers from the problem of low accuracy even with the recently proposed deep learning models. To improve network… More >

Ming He^1,2, Hongbin Wang^1,*, Lianke Zhou¹, Pengming Wang³, Andrew Ju⁴

CMC-Computers, Materials & Continua, Vol.57, No.3, pp. 521-532, 2018, DOI:10.32604/cmc.2018.03710

Abstract An important issue for deep learning models is the acquisition of training of data. Without abundant data from a real production environment for training, deep learning models would not be as widely used as they are today. However, the cost of obtaining abundant real-world environment is high, especially for underwater environments. It is more straightforward to simulate data that is closed to that from real environment. In this paper, a simple and easy symmetric learning data augmentation model (SLDAM) is proposed for underwater target radiate-noise data expansion and generation. The SLDAM, taking the optimal classifier of an initial dataset as… More >