Haiwen Chen¹, Guang Yu¹, Fang Liu², Zhiping Cai^{1, *}, Anfeng Liu³, Shuhui Chen¹, Hongbin Huang¹, Chak Fong Cheang⁴

CMC-Computers, Materials & Continua, Vol.62, No.2, pp. 917-927, 2020, DOI:10.32604/cmc.2020.05981

Abstract For many Internet companies, a huge amount of KPIs (e.g., server CPU usage, network usage, business monitoring data) will be generated every day. How to closely monitor various KPIs, and then quickly and accurately detect anomalies in such huge data for troubleshooting and recovering business is a great challenge, especially for unlabeled data. The generated KPIs can be detected by supervised learning with labeled data, but the current problem is that most KPIs are unlabeled. That is a time-consuming and laborious work to label anomaly for company engineers. Build an unsupervised model to detect unlabeled data is an urgent need… More >

Chaochao Luo¹, Shen Su^{2, *}, Yanbin Sun², Qingji Tan³, Meng Han⁴, Zhihong Tian^{2, *}

CMC-Computers, Materials & Continua, Vol.62, No.1, pp. 399-411, 2020, DOI:10.32604/cmc.2020.06507

Abstract Since the web service is essential in daily lives, cyber security becomes more and more important in this digital world. Malicious Uniform Resource Locator (URL) is a common and serious threat to cybersecurity. It hosts unsolicited content and lure unsuspecting users to become victim of scams, such as theft of private information, monetary loss, and malware installation. Thus, it is imperative to detect such threats. However, traditional approaches for malicious URLs detection that based on the blacklists are easy to be bypassed and lack the ability to detect newly generated malicious URLs. In this paper, we propose a novel malicious… More >

Sasan Saqaeeyan¹, Hamid Haj Seyyed Javadi^1,2,*, Hossein Amirkhani^1,3

CMES-Computer Modeling in Engineering & Sciences, Vol.121, No.3, pp. 815-834, 2019, DOI:10.32604/cmes.2019.07848

Abstract Anomaly detection in smart homes provides support to enhance the health and safety of people who live alone. Compared to the previous studies done on this topic, less attention has been given to hybrid methods. This paper presents a two-steps hybrid probabilistic anomaly detection model in the smart home. First, it employs various algorithms with different characteristics to detect anomalies from sensory data. Then, it aggregates their results using a Bayesian network. In this Bayesian network, abnormal events are detected through calculating the probability of abnormality given anomaly detection results of base methods. Experimental evaluation of a real dataset indicates… More >

Haiqi Zhu¹, Fanzhi Meng^2,*, Seungmin Rho³, Mohan Li^4,*, Jianyu Wang¹, Shaohui Liu¹, Feng Jiang¹

CMC-Computers, Materials & Continua, Vol.61, No.2, pp. 829-847, 2019, DOI:10.32604/cmc.2019.06115

Abstract In real-world many internet-based service companies need to closely monitor large amounts of data in order to ensure stable operation of their business. However, anomaly detection for these data with various patterns and data quality has been a great challenge, especially without labels. In this paper, we adopt an anomaly detection algorithm based on Long Short-Term Memory (LSTM) Network in terms of reconstructing KPIs and predicting KPIs. They use the reconstruction error and prediction error respectively as the criteria for judging anomalies, and we test our method with real data from a company in the insurance industry and achieved good… More >

Assia Maamar^1,*, Khelifa Benahmed²

CMC-Computers, Materials & Continua, Vol.60, No.1, pp. 15-39, 2019, DOI:10.32604/cmc.2019.06497

Abstract Recently, the radical digital transformation has deeply affected the traditional electricity grid and transformed it into an intelligent network (smart grid). This mutation is based on the progressive development of advanced technologies: advanced metering infrastructure (AMI) and smart meter which play a crucial role in the development of smart grid. AMI technologies have a promising potential in terms of improvement in energy efficiency, better demand management, and reduction in electricity costs. However the possibility of hacking smart meters and electricity theft is still among the most significant challenges facing electricity companies. In this regard, we propose a hybrid approach to… More >

Yu Wang^1,2,*, Yan Cao², Liancheng Zhang², Hongtao Zhang³, Roxana Ohriniuc⁴, Guodong Wang⁵, Ruosi Cheng⁶

CMC-Computers, Materials & Continua, Vol.60, No.3, pp. 1171-1187, 2019, DOI:10.32604/cmc.2019.05575

Abstract Network traffic anomaly detection has gained considerable attention over the years in many areas of great importance. Traditional methods used for detecting anomalies produce quantitative results derived from multi-source information. This makes it difficult for administrators to comprehend and deal with the underlying situations. This study proposes another method to yet determine traffic anomaly (YATA), based on the cloud model. YATA adopts forward and backward cloud transformation algorithms to fuse the quantitative value of acquisitions into the qualitative concept of anomaly degree. This method achieves rapid and direct perspective of network traffic. Experimental results with standard dataset indicate that using… More >

He Shi^1,2,3,4, Wenli Shang^1,2,3,4,*, Chunyu Chen^1,2,3,4, Jianming Zhao^1,2,3,4, Long Yin^{1, 2, 3, 4}

CMC-Computers, Materials & Continua, Vol.60, No.2, pp. 645-658, 2019, DOI:10.32604/cmc.2019.05648

Abstract In order to solve the problem of locating and protecting key processes and detecting outliers efficiently in complex industrial processes. An anomaly detection system which is based on the two-layer model fusion frame is designed in this paper. The key process is located by using the random forest model firstly, then the process data feature selection, dimension reduction and noise reduction are processed. Finally, the validity of the model is verified by simulation experiments. It is shown that this method can effectively reduce the prediction accuracy variance and improve the generalization ability of the traditional anomaly detection model from the… More >

Menghua Luo^1,2, Ke Wang¹, Zhiping Cai^1,*, Anfeng Liu³, Yangyang Li⁴, Chak Fong Cheang⁵

CMC-Computers, Materials & Continua, Vol.58, No.1, pp. 15-26, 2019, DOI:10.32604/cmc.2019.03708

Abstract The extreme imbalanced data problem is the core issue in anomaly detection. The amount of abnormal data is so small that we cannot get adequate information to analyze it. The mainstream methods focus on taking fully advantages of the normal data, of which the discrimination method is that the data not belonging to normal data distribution is the anomaly. From the view of data science, we concentrate on the abnormal data and generate artificial abnormal samples by machine learning method. In this kind of technologies, Synthetic Minority Over-sampling Technique and its improved algorithms are representative milestones, which generate synthetic examples… More >

Ming Wan¹, Jiangyuan Yao^2,*, Yuan Jing¹, Xi Jin^3,4

CMC-Computers, Materials & Continua, Vol.55, No.3, pp. 447-463, 2018, DOI: 10.3970/cmc.2018.02195

Abstract As the main communication mediums in industrial control networks, industrial communication protocols are always vulnerable to extreme exploitations, and it is very difficult to take protective measures due to their serious privacy. Based on the SDN (Software Defined Network) technology, this paper proposes a novel event-based anomaly detection approach to identify misbehaviors using non-public industrial communication protocols, and this approach can be installed in SDN switches as a security software appliance in SDN-based control systems. Furthermore, aiming at the unknown protocol specification and message format, this approach first restructures the industrial communication sessions and merges the payloads from industrial communication… More >

Nadipuram R. Prasad¹, Salvador Almanza-Garcia¹, Thomas T. Lu²

CMC-Computers, Materials & Continua, Vol.14, No.1, pp. 1-22, 2009, DOI:10.3970/cmc.2009.014.001

Abstract The paper presents a revolutionary framework for the modeling, detection, characterization, identification, and machine-learning of anomalous behavior in observed phenomena arising from a large class of unknown and uncertain dynamical systems. An evolved behavior would in general be very difficult to correct unless the specific anomalous event that caused such behavior can be detected early, and any consequence attributed to the specific anomaly following its detection. Substantial investigative time and effort is required to back-track the cause for abnormal behavior and to recreate the event sequence leading to such abnormal behavior. The need to automatically detect anomalous behavior is therefore… More >