A Novel HTTP Anomaly Detection Framework Based on Edge Intelligence for the Internet of Things (IoT)

2020 ◽  
pp. 1-7
Author(s):  
Yufei An ◽  
Jianqiang Li ◽  
F. Richard Yu ◽  
Jianyong Chen ◽  
Victor C. M. Leung
Keyword(s):  
Internet Of Things ◽  
Anomaly Detection ◽  
The Internet ◽  
The Internet Of Things

scholarly journals A Novel Sensor Data Pre-Processing Methodology for the Internet of Things Using Anomaly Detection and Transfer-By-Subspace-Similarity Transformation

Sensors ◽  
2019 ◽  
Vol 19 (20) ◽  
pp. 4536 ◽  
Author(s):  
Yan Zhong ◽  
Simon Fong ◽  
Shimin Hu ◽  
Raymond Wong ◽  
Weiwei Lin
Keyword(s):  
Data Mining ◽  
Internet Of Things ◽  
Anomaly Detection ◽  
Sensor Data ◽  
Detection Methods ◽  
The Internet ◽  
Quality Of Data ◽  
Data Mining Algorithms ◽  
Sensing Applications ◽  
The Internet Of Things

The Internet of Things (IoT) and sensors are becoming increasingly popular, especially in monitoring large and ambient environments. Applications that embrace IoT and sensors often require mining the data feeds that are collected at frequent intervals for intelligence. Despite the fact that such sensor data are massive, most of the data contents are identical and repetitive; for example, human traffic in a park at night. Most of the traditional classification algorithms were originally formulated decades ago, and they were not designed to handle such sensor data effectively. Hence, the performance of the learned model is often poor because of the small granularity in classification and the sporadic patterns in the data. To improve the quality of data mining from the IoT data, a new pre-processing methodology based on subspace similarity detection is proposed. Our method can be well integrated with traditional data mining algorithms and anomaly detection methods. The pre-processing method is flexible for handling similar kinds of sensor data that are sporadic in nature that exist in many ambient sensing applications. The proposed methodology is evaluated by extensive experiment with a collection of classical data mining models. An improvement over the precision rate is shown by using the proposed method.

scholarly journals System Log Detection Model Based on Conformal Prediction

Electronics ◽  
2020 ◽  
Vol 9 (2) ◽  
pp. 232 ◽  
Author(s):  
Yitong Ren ◽  
Zhaojun Gu ◽  
Zhi Wang ◽  
Zhihong Tian ◽  
Chunbo Liu ◽  
...  
Keyword(s):  
Machine Learning ◽  
Internet Of Things ◽  
Anomaly Detection ◽  
Statistical Learning ◽  
The Internet ◽  
Learning Method ◽  
Log Data ◽  
Detection Model ◽  
Statistical Learning Method ◽  
The Internet Of Things

With the rapid development of the Internet of Things, the combination of the Internet of Things with machine learning, Hadoop and other fields are current development trends. Hadoop Distributed File System (HDFS) is one of the core components of Hadoop, which is used to process files that are divided into data blocks distributed in the cluster. Once the distributed log data are abnormal, it will cause serious losses. When using machine learning algorithms for system log anomaly detection, the output of threshold-based classification models are only normal or abnormal simple predictions. This paper used the statistical learning method of conformity measure to calculate the similarity between test data and past experience. Compared with detection methods based on static threshold, the statistical learning method of the conformity measure can dynamically adapt to the changing log data. By adjusting the maximum fault tolerance, a system administrator can better manage and monitor the system logs. In addition, the computational efficiency of the statistical learning method for conformity measurement was improved. This paper implemented an intranet anomaly detection model based on log analysis, and conducted trial detection on HDFS data sets quickly and efficiently.

scholarly journals Attack and Anomaly Detection in IoT Networks using Machine Learning Techniques: A Review

2021 ◽  
pp. 30-46
Author(s):  
Saad Hikmat Haji ◽  
Siddeeq Y. Ameen
Keyword(s):  
Machine Learning ◽  
Internet Of Things ◽  
Anomaly Detection ◽  
Attack Detection ◽  
Machine Learning Techniques ◽  
Smart Devices ◽  
The Internet ◽  
Business Services ◽  
Industrial Services ◽  
The Internet Of Things

The Internet of Things (IoT) is one of today's most rapidly growing technologies. It is a technology that allows billions of smart devices or objects known as "Things" to collect different types of data about themselves and their surroundings using various sensors. They may then share it with the authorized parties for various purposes, including controlling and monitoring industrial services or increasing business services or functions. However, the Internet of Things currently faces more security threats than ever before. Machine Learning (ML) has observed a critical technological breakthrough, which has opened several new research avenues to solve current and future IoT challenges. However, Machine Learning is a powerful technology to identify threats and suspected activities in intelligent devices and networks. In this paper, various ML algorithms have been compared in terms of attack detection and anomaly detection, following a thorough literature review on Machine Learning methods and the significance of IoT security in the context of various types of potential attacks. Furthermore, possible ML-based IoT protection technologies have been introduced.

Cluster-based Ensemble Classification Approach for Anomaly Detection in the Internet of Things

2020 ◽  
Vol 10 (4) ◽  
pp. 594-604
Author(s):  
Mostafa Hosseini ◽  
Hamidreza Shayegh Brojeni
Keyword(s):  
Internet Of Things ◽  
Anomaly Detection ◽  
Smart Grids ◽  
False Positive Rate ◽  
Ensemble Classification ◽  
Misclassification Rate ◽  
The Internet ◽  
Classification Approach ◽  
Detection Techniques ◽  
The Internet Of Things

Background & Objective: The next generation of the internet where physical things or objects are going to interact with each other without human interventions is called the Internet of Things (IoT). Its presence can improve the quality of human lives in different domains and environments such as agriculture, smart homes, intelligent transportation systems, and smart grids. : In the lowest layer of the IoT architecture (i.e., the perception layer), there are a variety of sensors which are responsible for gathering data from their environment to provide service for customers. However, these collected data are not always accurate and may be infected with anomalies for some reasons such as limited sensor’s resources and environmental influences. : Accordingly, anomaly detection can be used as a preprocessing phase to prevent sending inappropriate data for the processing. Methods: Since distributed characteristic and its heterogeneous elements complicate the application of anomaly detection techniques, in this paper, a cluster-based ensemble classification approach has been presented. Results & Conclusion: Will possessing low complexity, the proposed method has high accuracy in detecting anomalies. This method has been tested on the data collected from sensors in the Intel Berkley research laboratory which is one of the free and available datasets in the domain of IoT. The results indicated that the proposed technique could achieve an accuracy of 99.9186%, a positive detection rate of 99.7459%, while reducing false positive rate and misclassification rate to 0.0025% and 0.0813% respectively.

scholarly journals Smart Audio Sensors in the Internet of Things Edge for Anomaly Detection

IEEE Access ◽  
2018 ◽  
Vol 6 ◽  
pp. 67594-67610 ◽  
Author(s):  
Mattia Antonini ◽  
Massimo Vecchio ◽  
Fabio Antonelli ◽  
Pietro Ducange ◽  
Charith Perera
Keyword(s):  
Internet Of Things ◽  
Anomaly Detection ◽  
The Internet ◽  
The Internet Of Things

scholarly journals READ-IoT: Reliable Event and Anomaly Detection Framework for the Internet of Things

IEEE Access ◽  
2021 ◽  
Vol 9 ◽  
pp. 24168-24186 ◽  
Author(s):  
Aymen Yahyaoui ◽  
Takoua Abdellatif ◽  
Sami Yangui ◽  
Rabah Attia
Keyword(s):  
Internet Of Things ◽  
Anomaly Detection ◽  
The Internet ◽  
The Internet Of Things

Edge Intelligence (EI)-Enabled HTTP Anomaly Detection Framework for the Internet of Things (IoT)

2020 ◽  
pp. 1-1
Author(s):  
Yufei An ◽  
F. Richard Yu ◽  
Jianqiang Li ◽  
Jianyong Chen ◽  
Victor C.M. Leung
Keyword(s):  
Internet Of Things ◽  
Anomaly Detection ◽  
The Internet ◽  
The Internet Of Things

scholarly journals Anomaly Detection of Highway Vehicle Trajectory under the Internet of Things Converged with 5G Technology

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Ketao Deng
Keyword(s):  
Internet Of Things ◽  
Anomaly Detection ◽  
Communication Technology ◽  
Traffic Monitoring ◽  
The Internet ◽  
Moving Target ◽  
Continuous Increase ◽  
5G Communication ◽  
Vehicle Trajectory ◽  
The Internet Of Things

The gradual increase in the density of highway vehicles and traffic flow makes the abnormal driving state of vehicles an indispensable tool for assisting traffic dispatch. Intelligent transportation systems can detect and track vehicles in real time, acquire characteristics such as vehicle traffic, vehicle speed, vehicle flow density, and vehicle trajectory, and further perform advanced tasks such as vehicle trajectory. The detection of abnormal vehicle trajectory is an important content of vehicle trajectory understanding. And the development of the Internet of Things (IoT) and 5G technology has led to a continuous increase in the rate of data information circulation. The “Internet of Vehicles” generated based on the practice of 5G communication technology constitutes a vehicle abnormal trajectory detection system, which has very high feasibility and safety and stability. Therefore, this research is aimed at the needs of preventing major accidents and forensic analysis during highway vehicles. Based on the integration of the Internet of Things 5G communication technology, a trajectorial anomaly detection of highway vehicle trajectory based on the integration of the Internet of Things 5G is proposed. By accurately sensing unsafe events at the perception layer, network layer, and application layer, the vehicle driving trajectory state is divided into several simple semantic representations. The semantic representation is analyzed, and then the moving target detection and moving target tracking algorithms needed to extract the vehicle trajectory are introduced. Through video detection and tracking of moving vehicle targets, the driving trajectory of the vehicle is obtained, and the movement characteristics of the vehicle in each frame of image are extracted. According to the relationship between the trajectory of the vehicle and the lane line, the vehicle trajectory analysis is realized, and then it is judged whether the vehicle has abnormal trajectory. Compared with the traditional method of manually detecting the driving condition of the vehicle, the abnormal trajectory detection of the vehicle based on the integration of the Internet of Things and 5G can quickly detect the abnormal trajectory of the vehicle in the traffic monitoring video.

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