scholarly journals Adaptive and Incremental-Clustering Anomaly Detection Algorithm for VMs Under Cloud Platform Runtime Environment

IEEE Access ◽  
2018 ◽  
Vol 6 ◽  
pp. 76984-76992
Author(s):  
Hancui Zhang ◽  
Jun Liu ◽  
Tianshu Wu
Keyword(s):  
Anomaly Detection ◽  
Detection Algorithm ◽  
Incremental Clustering ◽  
Cloud Platform ◽  
Runtime Environment

scholarly journals An Anomaly Detection Algorithm of Cloud Platform Based on Self-Organizing Maps

2016 ◽  
Vol 2016 ◽  
pp. 1-9 ◽  
Author(s):  
Jun Liu ◽  
Shuyu Chen ◽  
Zhen Zhou ◽  
Tianshu Wu
Keyword(s):  
Anomaly Detection ◽  
Virtual Machine ◽  
Large Scale ◽  
Virtual Machines ◽  
Detection Algorithm ◽  
Detection Accuracy ◽  
Cloud Platform ◽  
Self Organizing Maps ◽  
Detection Speed ◽  
Self Organizing

Virtual machines (VM) on a Cloud platform can be influenced by a variety of factors which can lead to decreased performance and downtime, affecting the reliability of the Cloud platform. Traditional anomaly detection algorithms and strategies for Cloud platforms have some flaws in their accuracy of detection, detection speed, and adaptability. In this paper, a dynamic and adaptive anomaly detection algorithm based on Self-Organizing Maps (SOM) for virtual machines is proposed. A unified modeling method based on SOM to detect the machine performance within the detection region is presented, which avoids the cost of modeling a single virtual machine and enhances the detection speed and reliability of large-scale virtual machines in Cloud platform. The important parameters that affect the modeling speed are optimized in the SOM process to significantly improve the accuracy of the SOM modeling and therefore the anomaly detection accuracy of the virtual machine.

scholarly journals eTPM: A Trusted Cloud Platform Enclave TPM Scheme Based on Intel SGX Technology

Sensors ◽  
2018 ◽  
Vol 18 (11) ◽  
pp. 3807 ◽  
Author(s):  
Haonan Sun ◽  
Rongyu He ◽  
Yong Zhang ◽  
Ruiyun Wang ◽  
Wai Hung Ip ◽  
...  
Keyword(s):  
Virtual Machines ◽  
Trusted Computing ◽  
Information Leakage ◽  
Research Topic ◽  
Cloud Environment ◽  
Cloud Platform ◽  
Computing Technology ◽  
Security Issues ◽  
Runtime Environment ◽  
Cloud Users

Today cloud computing is widely used in various industries. While benefiting from the services provided by the cloud, users are also faced with some security issues, such as information leakage and data tampering. Utilizing trusted computing technology to enhance the security mechanism, defined as trusted cloud, has become a hot research topic in cloud security. Currently, virtual TPM (vTPM) is commonly used in a trusted cloud to protect the integrity of the cloud environment. However, the existing vTPM scheme lacks protections of vTPM itself at a runtime environment. This paper proposed a novel scheme, which designed a new trusted cloud platform security component, ‘enclave TPM (eTPM)’ to protect cloud and employed Intel SGX to enhance the security of eTPM. The eTPM is a software component that emulates TPM functions which build trust and security in cloud and runs in ‘enclave’, an isolation memory zone introduced by SGX. eTPM can ensure its security at runtime, and protect the integrity of Virtual Machines (VM) according to user-specific policies. Finally, a prototype for the eTPM scheme was implemented, and experiment manifested its effectiveness, security, and availability.

scholarly journals A Detection Method for Abnormal Transactions in E-Commerce Based on Extended Data Flow Conformance Checking

2022 ◽  
Vol 2022 ◽  
pp. 1-14
Author(s):  
Yadi Wang ◽  
Wangyang Yu ◽  
Peng Teng ◽  
Guanjun Liu ◽  
Dongming Xiang
Keyword(s):  
Anomaly Detection ◽  
Business Processes ◽  
Data Flow ◽  
Detection Algorithm ◽  
Control Flow ◽  
Smart Devices ◽  
Integration Model ◽  
Whole Process ◽  
Mobile Transaction ◽  
And Control

With the development of smart devices and mobile communication technologies, e-commerce has spread over all aspects of life. Abnormal transaction detection is important in e-commerce since abnormal transactions can result in large losses. Additionally, integrating data flow and control flow is important in the research of process modeling and data analysis since it plays an important role in the correctness and security of business processes. This paper proposes a novel method of detecting abnormal transactions via an integration model of data and control flows. Our model, called Extended Data Petri net (DPNE), integrates the data interaction and behavior of the whole process from the user logging into the e-commerce platform to the end of the payment, which also covers the mobile transaction process. We analyse the structure of the model, design the anomaly detection algorithm of relevant data, and illustrate the rationality and effectiveness of the whole system model. Through a case study, it is proved that each part of the system can respond well, and the system can judge each activity of every mobile transaction. Finally, the anomaly detection results are obtained by some comprehensive analysis.

scholarly journals BCEAD: A Blockchain-Empowered Ensemble Anomaly Detection for Wireless Sensor Network via Isolation Forest

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Xiong Yang ◽  
Yuling Chen ◽  
Xiaobin Qian ◽  
Tao Li ◽  
Xiao Lv
Keyword(s):  
Anomaly Detection ◽  
Block Structure ◽  
Detection Algorithm ◽  
Wireless Sensor ◽  
Detection Systems ◽  
Blockchain Technology ◽  
The Internet Of Things ◽  
Isolation Forest ◽  
Distributed Deployment ◽  
Internal Attacks

The distributed deployment of wireless sensor networks (WSNs) makes the network more convenient, but it also causes more hidden security hazards that are difficult to be solved. For example, the unprotected deployment of sensors makes distributed anomaly detection systems for WSNs more vulnerable to internal attacks, and the limited computing resources of WSNs hinder the construction of a trusted environment. In recent years, the widely observed blockchain technology has shown the potential to strengthen the security of the Internet of Things. Therefore, we propose a blockchain-based ensemble anomaly detection (BCEAD), which stores the model of a typical anomaly detection algorithm (isolated forest) in the blockchain for distributed anomaly detection in WSNs. By constructing a suitable block structure and consensus mechanism, the global model for detection can iteratively update to enhance detection performance. Moreover, the blockchain guarantees the trust environment of the network, making the detection algorithm resistant to internal attacks. Finally, compared with similar schemes, in terms of performance, cost, etc., the results prove that BCEAD performs better.

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