Weiwu Ren^1,*, Xiaoqiang Di¹, Zhanwei Du², Jianping Zhao¹

CMC-Computers, Materials & Continua, Vol.68, No.2, pp. 2057-2073, 2021, DOI:10.32604/cmc.2021.016678 - 13 April 2021

Abstract CFSFDP (Clustering by fast search and find of density peak) is a simple and crisp density clustering algorithm. It does not only have the advantages of density clustering algorithm, but also can find the peak of cluster automatically. However, the lack of adaptability makes it difficult to apply in intrusion detection. The new input cannot be updated in time to the existing profiles, and rebuilding profiles would waste a lot of time and computation. Therefore, an adaptive anomaly detection algorithm based on CFSFDP is proposed in this paper. By analyzing the influence of new input… More >

Kunkun Wang^1,2, Xianda Liu^2,3,4,*

Journal on Artificial Intelligence, Vol.3, No.1, pp. 9-19, 2021, DOI:10.32604/jai.2021.016706 - 02 April 2021

Abstract With the development of Internet technology, the computing power of data has increased, and the development of machine learning has become faster and faster. In the industrial production of industrial control systems, quality inspection and safety production of process products have always been our concern. Aiming at the low accuracy of anomaly detection in process data in industrial control system, this paper proposes an anomaly detection method based on stacking integration using the machine learning algorithm. Data are collected from the industrial site and processed by feature engineering. Principal component analysis (PCA) and integrated rule… 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 - 19 February 2021

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… More >

Sinil Mubarak¹, Mohamed Hadi Habaebi^1,*, Md Rafiqul Islam¹, Farah Diyana Abdul Rahman, Mohammad Tahir²

Computer Systems Science and Engineering, Vol.37, No.1, pp. 33-46, 2021, DOI:10.32604/csse.2021.014384 - 05 February 2021

Abstract An Intrusion Detection System (IDS) provides a front-line defense mechanism for the Industrial Control System (ICS) dedicated to keeping the process operations running continuously for 24 hours in a day and 7 days in a week. A well-known ICS is the Supervisory Control and Data Acquisition (SCADA) system. It supervises the physical process from sensor data and performs remote monitoring control and diagnostic functions in critical infrastructures. The ICS cyber threats are growing at an alarming rate on industrial automation applications. Detection techniques with machine learning algorithms on public datasets, suitable for intrusion detection of More >

Xiaoyu Duan¹, Shi Ying^1,*, Wanli Yuan¹, Hailong Cheng¹, Xiang Yin²

Computer Systems Science and Engineering, Vol.37, No.1, pp. 135-148, 2021, DOI:10.32604/csse.2021.014030 - 05 February 2021

Abstract Detecting anomaly logs is a great significance step for guarding system faults. Due to the uncertainty of abnormal log types, lack of real anomaly logs and accurately labeled log datasets. Existing technologies cannot be enough for detecting complex and various log point anomalies by using human-defined rules. We propose a log anomaly detection method based on Generative Adversarial Networks (GAN). This method uses the Encoder-Decoder framework based on Long Short-Term Memory (LSTM) network as the generator, takes the log keywords as the input of the encoder, and the decoder outputs the generated log template. The More >

Kangjik Kim¹, Hyunbin Kim¹, Junchul Chun¹, Mingoo Kang², Min Hong^3,*, Byungseok Min⁴

CMC-Computers, Materials & Continua, Vol.67, No.2, pp. 2547-2568, 2021, DOI:10.32604/cmc.2021.014642 - 05 February 2021

Abstract Physical contamination of food occurs when it comes into contact with foreign objects. Foreign objects can be introduced to food at any time during food delivery and packaging and can cause serious concerns such as broken teeth or choking. Therefore, a preventive method that can detect and remove foreign objects in advance is required. Several studies have attempted to detect defective products using deep learning networks. Because it is difficult to obtain foreign object-containing food data from industry, most studies on industrial anomaly detection have used unsupervised learning methods. This paper proposes a new method… More >

Shumaila Shahzadi¹, Fahad Ahmad^1,*, Asma Basharat¹, Madallah Alruwaili², Saad Alanazi², Mamoona Humayun², Muhammad Rizwan¹, Shahid Naseem³

CMC-Computers, Materials & Continua, Vol.66, No.3, pp. 2723-2749, 2021, DOI:10.32604/cmc.2021.014594 - 28 December 2020

Abstract With the rising demand for data access, network service providers face the challenge of growing their capital and operating costs while at the same time enhancing network capacity and meeting the increased demand for access. To increase efficacy of Software Defined Network (SDN) and Network Function Virtualization (NFV) framework, we need to eradicate network security configuration errors that may create vulnerabilities to affect overall efficiency, reduce network performance, and increase maintenance cost. The existing frameworks lack in security, and computer systems face few abnormalities, which prompts the need for different recognition and mitigation methods to… More >

Lewis Nkenyereye¹, Bayu Adhi Tama², Sunghoon Lim^3,*

CMC-Computers, Materials & Continua, Vol.66, No.2, pp. 2217-2227, 2021, DOI:10.32604/cmc.2020.012432 - 26 November 2020

Abstract An anomaly-based intrusion detection system (A-IDS) provides a critical aspect in a modern computing infrastructure since new types of attacks can be discovered. It prevalently utilizes several machine learning algorithms (ML) for detecting and classifying network traffic. To date, lots of algorithms have been proposed to improve the detection performance of A-IDS, either using individual or ensemble learners. In particular, ensemble learners have shown remarkable performance over individual learners in many applications, including in cybersecurity domain. However, most existing works still suffer from unsatisfactory results due to improper ensemble design. The aim of this study More >

Yang Yang^1,*, Qian Zhao¹, Linna Ruan², Zhipeng Gao¹, Yonghua Huo³, Xuesong Qiu¹

Intelligent Automation & Soft Computing, Vol.26, No.5, pp. 1139-1155, 2020, DOI:10.32604/iasc.2020.011705

Abstract In network anomaly detection, network traffic data are often imbalanced, that is, certain classes of network traffic data have a large sample data volume while other classes have few, resulting in reduced overall network traffic anomaly detection on a minority class of samples. For imbalanced data, researchers have proposed the use of oversampling techniques to balance data sets; in particular, an oversampling method called the SMOTE provides a simple and effective solution for balancing data sets. However, current oversampling methods suffer from the generation of noisy samples and poor information quality. Hence, this study proposes More >

Ji Qian¹, Fang Liu^2,*, Donghui Li³, Xin Jin⁴, Feng Li⁴

Journal of Cyber Security, Vol.2, No.4, pp. 157-166, 2020, DOI:10.32604/jcs.2020.011169 - 07 December 2020

Abstract Anomaly detection using KPI (Key Performance Indicator) is critical for Internet-based services to maintain high service availability. However, given the velocity, volume, and diversified nature of monitoring data, it is difficult to obtain enough labelled data to build an accurate anomaly detection model for using supervised machine leaning methods. In this paper, we propose an automatic and generic transfer learning strategy: Detecting anomalies on a new KPI by using pretrained model on existing selected labelled KPI. Our approach, called KADT (KPI Anomaly Detection based on Transfer Learning), integrates KPI clustering and model pretrained techniques. KPI More >