scholarly journals DEDA: An algorithm for early detection of topology attacks in the internet of things

2021 ◽  
Vol 11 (2) ◽  
pp. 1761
Author(s):  
Jalindar Karande ◽  
Sarang Joshi
Keyword(s):  
Internet Of Things ◽  
Early Detection ◽  
Power Plants ◽  
Attack Detection ◽  
Detection Methods ◽  
The Internet ◽  
Critical Systems ◽  
Control Packet ◽  
Entry Points ◽  
The Internet Of Things

The internet of things (IoT) is used in domestic, industrial as well as mission-critical systems including homes, transports, power plants, industrial manufacturing and health-care applications. Security of data generated by such systems and IoT systems itself is very critical in such applications. Early detection of any attack targeting IoT system is necessary to minimize the damage. This paper reviews security attack detection methods for IoT Infrastructure presented in the state-of-the-art. One of the major entry points for attacks in IoT system is topology exploitation. This paper proposes a distributed algorithm for early detection of such attacks with the help of predictive descriptor tables. This paper also presents feature selection from topology control packet fields. The performance of the proposed algorithm is evaluated using an extensive simulation carried out in OMNeT++. Performance parameter includes accuracy and time required for detection. Simulation results presented in this paper show that the proposed algorithm is effective in detecting attacks ahead in time.

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 Towards Deep-Learning-Driven Intrusion Detection for the Internet of Things

Sensors ◽  
2019 ◽  
Vol 19 (9) ◽  
pp. 1977 ◽  
Author(s):  
Geethapriya Thamilarasu ◽  
Shiven Chawla
Keyword(s):  
Deep Learning ◽  
Internet Of Things ◽  
Intrusion Detection ◽  
Intrusion Detection System ◽  
Learning Algorithm ◽  
Detection System ◽  
Cyber Attacks ◽  
The Internet ◽  
Critical Systems ◽  
The Internet Of Things

Cyber-attacks on the Internet of Things (IoT) are growing at an alarming rate as devices, applications, and communication networks are becoming increasingly connected and integrated. When attacks on IoT networks go undetected for longer periods, it affects availability of critical systems for end users, increases the number of data breaches and identity theft, drives up the costs and impacts the revenue. It is imperative to detect attacks on IoT systems in near real time to provide effective security and defense. In this paper, we develop an intelligent intrusion-detection system tailored to the IoT environment. Specifically, we use a deep-learning algorithm to detect malicious traffic in IoT networks. The detection solution provides security as a service and facilitates interoperability between various network communication protocols used in IoT. We evaluate our proposed detection framework using both real-network traces for providing a proof of concept, and using simulation for providing evidence of its scalability. Our experimental results confirm that the proposed intrusion-detection system can detect real-world intrusions effectively.

Pengembangan Smart Home Dengan Microcontrollers ESP32 Dan MC-38 Door Magnetic Switch Sensor Berbasis Internet of Things (IoT) Untuk Meningkatkan Deteksi Dini Keamanan Perumahan

2019 ◽  
Vol 3 (3) ◽  
pp. 451-457
Author(s):  
Andi Setiawan ◽  
Ade Irma Purnamasari
Keyword(s):  
Internet Of Things ◽  
Early Detection ◽  
Early Warning ◽  
Smart Home ◽  
Detection System ◽  
The Internet ◽  
Magnetic Switch ◽  
Web Framework ◽  
Closed Angle ◽  
The Internet Of Things

The objective developed from this research is to utilize Smart Home with an integrated ESP32 microcontroller with a camera and MC-38 door magnetic switch sensor based on the Internet of Things (IoT) as a research base to detect the security of arumsari earth housing in Cirebon District when left by its inhabitants. ESP32 microcontroller which can be programmed via arduino IDE, then functioned to respond to the integrated camera so that it can transmit images when the MC-38 sensor door magnetic switch sensor is active. Technically the combination of the ESP32 microcontroller and MC-38 door magnetic switch sensor, which was developed as a prototype in this study is called the arumsari housing early detection system. The mechanism of the arumsari housing early detection system is when a house door or window is successfully forcibly broken without going through the system mechanism, then automatically an image or can also be developed into a video from a camera mounted on an ESP32 microcontroller will send the image through a web framework or smartphone as a form early warning of security to housing owners. The results obtained from this study are at the angle of normally open MC-38 door magnetic switch sensor of 60 - 1800, will work sending an image signal which means there is an indication of a burglar or unknown person entering the house. Whereas at the normally closed angle MC-38 door magnetic switch sensor is 00-50, it will not work sending an image signal which means the house is safe.

Role of the Internet of Things for Early Detection of Covid-19 Infections: a Comprehensive Survey

2021 ◽  
Author(s):  
Sanjoy Mondal ◽  
Indrakshi Ghosh ◽  
Sayak Ghosh ◽  
Ayushi Gupta ◽  
Dipankar Basu
Keyword(s):  
Internet Of Things ◽  
Early Detection ◽  
The Internet ◽  
Comprehensive Survey ◽  
The Internet Of Things

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.

IMPLEMENTING DISTRIBUTED GENERATION BY CREATING VIRTUAL POWER PLANTS

2020 ◽  
pp. 51-59
Author(s):  
V.V. Okuneva ◽  
◽  
A.A. Agamirzoev ◽  
K.B. Korneev ◽  
◽  
...  
Keyword(s):  
Internet Of Things ◽  
Power System ◽  
Distributed Generation ◽  
Decentralized Control ◽  
Power Plants ◽  
The Internet ◽  
Virtual Power ◽  
Effective Mechanism ◽  
Virtual Power Plants ◽  
The Internet Of Things

We consider one of the most promising technologies for solving the problem of balancing a system with distributed generation is a virtual substation. It is noted that using the technologies of distributed computing and the «Internet of things», it is possible to implement an effective mechanism for decentralized control of power system elements.

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