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.

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.

A Smart Home by Using Internet of Things System: A Succinct Study

2020 ◽  
Vol 17 (9) ◽  
pp. 4207-4212
Author(s):  
Padala Neeraja ◽  
Durgesh Nandan
Keyword(s):  
Internet Of Things ◽  
Mobile Device ◽  
Smart Grids ◽  
Smart Home ◽  
Emerging Technology ◽  
The Internet ◽  
System A ◽  
Machine To Machine Communication ◽  
Machine Communication ◽  
The Internet Of Things

The internet of things is nothing but the interconnection of a number of systems or objects in which the internal circuit consists of a number of sensors and connectors. The main aim of the internet of things is to transfer information and to make an interaction between the systems. Through IoT, all the systems can be sensed and all the home appliances will be controlled remotely through a mobile device. It creates an integration of more and more networks in the future. The IoT is a very important emerging technology nowadays in which the main applications of IoT are smart grids, smart homes, etc. As the number of devices was increasing nowadays IoT plays a very significant role in present society. So, the challenges were increasing and there will be a machine to machine communication and also with the user. It reduces human efforts as it is machine-dependent. It acts according to the instructions given by the user.

scholarly journals Managing Contingencies in Smart Grids via the Internet of Things

2016 ◽  
Vol 7 (4) ◽  
pp. 2134-2141 ◽  
Author(s):  
Stefano Ciavarella ◽  
Jhi-Young Joo ◽  
Simone Silvestri
Keyword(s):  
Internet Of Things ◽  
Smart Grids ◽  
The Internet ◽  
The Internet Of Things

scholarly journals COIIoT - An Interface between CoAP and OSGP Protocols for the Integration of Internet of Things Devices with Smart Grids

2020 ◽  
Author(s):  
Felipe Viel ◽  
Luis Augusto Silva ◽  
Valderi Leithardt ◽  
Gabriel Villarubia González ◽  
Raimundo Celeste Ghizoni Teive ◽  
...  
Keyword(s):  
Internet Of Things ◽  
Smart Grid ◽  
Smart Grids ◽  
Communication Protocols ◽  
High Volume ◽  
The Internet ◽  
Human Intervention ◽  
Exchange Information ◽  
Systems Architectures ◽  
The Internet Of Things

The evolution and miniaturization of the technologies for processing, storage, and communication have enabled computer systems to process a high volume of information and make decisions without human intervention. Within this context, several systems architectures and models have gained prominences, such as the Internet of Things (IoT) and Smart Grids (SGs). SGs use communication protocols to exchange information, among which the Open Smart Grid Protocol (OSGP) stands out. In contrast, this protocol does not have integration support with IoT systems that use some already consolidated communication protocols, such as the Constrained Application Protocol (CoAP). Thus, this work develops the integration of the protocols OSGP and CoAP to allow the communication between conventional IoT systems and systems dedicated to SGs. Results demonstrate the effectiveness of this integration, with the minimum impact on the flow of commands and data, making possible the use of the developed CoAP-OSGP Interface for Internet of Things (COIIoT).

Botnet and Internet of Things (IoTs)

2020 ◽  
pp. 304-316
Author(s):  
Kamal Alieyan ◽  
Ammar Almomani ◽  
Rosni Abdullah ◽  
Badr Almutairi ◽  
Mohammad Alauthman
Keyword(s):  
Internet Of Things ◽  
Cyber Attacks ◽  
The Internet ◽  
Ddos Attacks ◽  
Botnet Detection ◽  
The Public ◽  
Detection Techniques ◽  
Channel Information ◽  
Iot Devices ◽  
The Internet Of Things

In today's internet world the internet of things (IoT) is becoming the most significant and developing technology. The primary goal behind the IoT is enabling more secure existence along with the improvement of risks at various life levels. With the arrival of IoT botnets, the perspective towards IoT products has transformed from enhanced living enabler into the internet of vulnerabilities for cybercriminals. Of all the several types of malware, botnet is considered as really a serious risk that often happens in cybercrimes and cyber-attacks. Botnet performs some predefined jobs and that too in some automated fashion. These attacks mostly occur in situations like phishing against any critical targets. Files sharing channel information are moved to DDoS attacks. IoT botnets have subjected two distinct problems, firstly, on the public internet. Most of the IoT devices are easily accessible. Secondly, in the architecture of most of the IoT units, security is usually a reconsideration. This particular chapter discusses IoT, botnet in IoT, and various botnet detection techniques available in IoT.

Botnet and Internet of Things (IoTs)

2021 ◽  
pp. 138-150
Author(s):  
Kamal Alieyan ◽  
Ammar Almomani ◽  
Rosni Abdullah ◽  
Badr Almutairi ◽  
Mohammad Alauthman
Keyword(s):  
Internet Of Things ◽  
Cyber Attacks ◽  
The Internet ◽  
Ddos Attacks ◽  
Botnet Detection ◽  
The Public ◽  
Detection Techniques ◽  
Channel Information ◽  
Iot Devices ◽  
The Internet Of Things

In today's internet world the internet of things (IoT) is becoming the most significant and developing technology. The primary goal behind the IoT is enabling more secure existence along with the improvement of risks at various life levels. With the arrival of IoT botnets, the perspective towards IoT products has transformed from enhanced living enabler into the internet of vulnerabilities for cybercriminals. Of all the several types of malware, botnet is considered as really a serious risk that often happens in cybercrimes and cyber-attacks. Botnet performs some predefined jobs and that too in some automated fashion. These attacks mostly occur in situations like phishing against any critical targets. Files sharing channel information are moved to DDoS attacks. IoT botnets have subjected two distinct problems, firstly, on the public internet. Most of the IoT devices are easily accessible. Secondly, in the architecture of most of the IoT units, security is usually a reconsideration. This particular chapter discusses IoT, botnet in IoT, and various botnet detection techniques available in IoT.

The Internet of Things From a Legal Perspective

2022 ◽  
pp. 27-49
Author(s):  
Sidi Mohamed Sidi Ahmed
Keyword(s):  
Internet Of Things ◽  
Data Protection ◽  
Smart Grids ◽  
Personal Data ◽  
The Internet ◽  
Legal Perspective ◽  
Data Protection Law ◽  
Fitness Tracker ◽  
Wearable Medical ◽  
The Internet Of Things

The internet of things (IoT) is one of successive technological waves that could have great impact on different aspects of modern life. It is being used in transport, smart grids, healthcare, environmental monitoring, logistics, as well as for processing pure personal data through a fitness tracker, wearable medical device, smartwatch, smart clothing, wearable camera, and so forth. From a legal viewpoint, processing personal data has to be done in accordance with rules of data protection law. This law aims to protect data from collection to retention. It usually applies to the processing of personal data that identifies or can identify a specific natural person. Strict adherence to this law is necessary for protecting personal data from being misused and also for promoting the IoT industry. This chapter discusses the applicability of the data protection law to IoT and the consequences of non-compliance with this law. It also provides recommendations on how to effectively comply with the data protection law in the IoT environment.

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 Optimizing Hybrid Metaheuristic Algorithm with Cluster Head to Improve Performance Metrics on the IoT

2021 ◽  
Author(s):  
Malik bader alazzam ◽  
Fawaz Alassery
Keyword(s):  
Energy Consumption ◽  
Internet Of Things ◽  
Smart Grids ◽  
Weather Prediction ◽  
Cluster Head ◽  
Optimization Techniques ◽  
The Internet ◽  
Wide Range ◽  
Whale Optimization ◽  
The Internet Of Things

Abstract The Internet of Things (IoT) has subsequently been applied to a variety of sectors, including smart grids, farming, weather prediction, power generation, wastewater treatment, and so on. So if the Internet of Things has enormous promise in a wide range of applications, there still are certain areas where it may be improved. Designers had focused our present research on reducing the energy consumption of devices in IoT networks, which will result in a longer network lifetime. The far more suitable Cluster Head (CH) throughout the IoT system is determined in this study to optimize energy consumption. Whale Optimization Algorithm (WOA) with Evolutionary Algorithm (EA) is indeed a mixed meta-heuristic algorithm used during the suggested study. Various quantifiable metrics, including the variety of adult nodes, workload, temperatures, remaining energy, and a target value, were utilized IoT network groups. The suggested method then is contrasted to several cutting-edge optimization techniques, including the Artificial Bee Colony method, Neural Network, Adapted Gravity Simulated annealing. The findings show that the suggested hybrid method outperforms conventional methods.

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