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.

scholarly journals Mitigating Malicious Insider Attacks in the Internet of Things using Supervised Machine Learning Techniques

2021 ◽  
Vol 22 (1) ◽  
pp. 13-28
Author(s):  
Mir Shahnawaz Ahmad ◽  
Shahid Mehraj Shah
Keyword(s):  
Machine Learning ◽  
Internet Of Things ◽  
Supervised Machine Learning ◽  
Security Requirements ◽  
Machine Learning Techniques ◽  
Smart Devices ◽  
The Internet ◽  
Prime Field ◽  
Learning Techniques ◽  
The Internet Of Things

The interconnection of large number of smart devices and sensors for critical information gathering and analysis over the internet has given rise to the Internet of Things (IoT) network. In recent times, IoT has emerged as a prime field for solving diverse real-life problems by providing a smart and affordable solutions. The IoT network has various constraints like: limited computational capacity of sensors, heterogeneity of devices, limited energy resource and bandwidth etc. These constraints restrict the use of high-end security mechanisms, thus making these type of networks more vulnerable to various security attacks including malicious insider attacks. Also, it is very difficult to detect such malicious insiders in the network due to their unpredictable behaviour and the ubiquitous nature of IoT network makes the task more difficult. To solve such problems machine learning techniques can be used as they have the ability to learn the behaviour of the system and predict the particular anomaly in the system. So, in this paper we have discussed various security requirements and challenges in the IoT network. We have also applied various supervised machine learning techniques on available IoT dataset to deduce which among them is best suited to detect the malicious insider attacks in the IoT network.

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 Crop Management with the IoT: An Interdisciplinary Survey

Agronomy ◽  
2021 ◽  
Vol 11 (1) ◽  
pp. 181 ◽  
Author(s):  
Giuliano Vitali ◽  
Matteo Francia ◽  
Matteo Golfarelli ◽  
Maurizio Canavari
Keyword(s):  
Machine Learning ◽  
Internet Of Things ◽  
Crop Management ◽  
Cropping System ◽  
Heterogeneous Data ◽  
Machine Learning Techniques ◽  
The Internet ◽  
Crop Fields ◽  
Learning Techniques ◽  
The Internet Of Things

In this study, we analyze how crop management will benefit from the Internet of Things (IoT) by providing an overview of its architecture and components from agronomic and technological perspectives. The present analysis highlights that IoT is a mature enabling technology with articulated hardware and software components. Cheap networked devices can sense crop fields at a finer grain to give timeliness warnings on the presence of stress conditions and diseases to a wider range of farmers. Cloud computing allows reliable storage, access to heterogeneous data, and machine-learning techniques for developing and deploying farm services. From this study, it emerges that the Internet of Things will draw attention to sensor quality and placement protocols, while machine learning should be oriented to produce understandable knowledge, which is also useful to enhance cropping system simulation systems.

scholarly journals A Review on Identifying Suitable Machine Learning Approach for Internet of Things Applications

2021 ◽  
Vol 3 (3) ◽  
pp. 128-145
Author(s):  
R. Valanarasu
Keyword(s):  
Machine Learning ◽  
Internet Of Things ◽  
Research Work ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
The Internet ◽  
Smart Transportation ◽  
Iot Applications ◽  
Enhance Efficiency ◽  
The Internet Of Things

Recently, IoT is referred as a descriptive term for the idea that everything in the world should be connected to the internet. Healthcare and social goods, industrial automation, and energy are just a few of the areas where the Internet of Things applications are widely used. Applications are becoming smarter and linked devices are enabling their exploitation in every element of the Internet of Things [IoT]. Machine Learning (ML) methods are used to improve an application's intelligence and capabilities by analysing the large amounts of data. ML and IoT have been used for smart transportation, which has gained the increasing research interest. This research covers a range of Internet of Things (IoT) applications that use suitable machine learning techniques to enhance efficiency and reliability in the intelligent automation sector. Furthermore, this research article examines and identifies various applications such as energy, high-quality sensors associated, and G-map associated appropriate applications for IoT. In addition to that, the proposed research work includes comparisons and tabulations of several different machine learning algorithms for IoT applications.

scholarly journals Usages of Spark Framework with Different Machine Learning Algorithms

2021 ◽  
Vol 2021 ◽  
pp. 1-7
Author(s):  
Mohamed Ali Mohamed ◽  
Ibrahim Mahmoud El-henawy ◽  
Ahmad Salah
Keyword(s):  
Machine Learning ◽  
Big Data ◽  
Internet Of Things ◽  
Large Data ◽  
Machine Learning Algorithms ◽  
Smart Devices ◽  
The Internet ◽  
Learning Methods ◽  
Machine Learning Methods ◽  
The Internet Of Things

Sensors, satellites, mobile devices, social media, e-commerce, and the Internet, among others, saturate us with data. The Internet of Things, in particular, enables massive amounts of data to be generated more quickly. The Internet of Things is a term that describes the process of connecting computers, smart devices, and other data-generating equipment to a network and transmitting data. As a result, data is produced and updated on a regular basis to reflect changes in all areas and activities. As a consequence of this exponential growth of data, a new term and idea known as big data have been coined. Big data is required to illuminate the relationships between things, forecast future trends, and provide more information to decision-makers. The major problem at present, however, is how to effectively collect and evaluate massive amounts of diverse and complicated data. In some sectors or applications, machine learning models are the most frequently utilized methods for interpreting and analyzing data and obtaining important information. On their own, traditional machine learning methods are unable to successfully handle large data problems. This article gives an introduction to Spark architecture as a platform that machine learning methods may utilize to address issues regarding the design and execution of large data systems. This article focuses on three machine learning types, including regression, classification, and clustering, and how they can be applied on top of the Spark platform.

scholarly journals Mining knowledge graphs to map heterogeneous relations between the internet of things patterns

2021 ◽  
Vol 11 (6) ◽  
pp. 5066
Author(s):  
Vusi Sithole ◽  
Linda Marshall
Keyword(s):  
Machine Learning ◽  
Internet Of Things ◽  
Machine Learning Techniques ◽  
Small Scale ◽  
The Internet ◽  
Use Case ◽  
Object Oriented Design ◽  
Learning Techniques ◽  
Knowledge Graphs ◽  
The Internet Of Things

<span lang="EN-US">Patterns for the internet of things (IoT) which represent proven solutions used to solve design problems in the IoT are numerous. Similar to object-oriented design patterns, these IoT patterns contain multiple mutual heterogeneous relationships. However, these pattern relationships are hidden and virtually unidentified in most documents. In this paper, we use machine learning techniques to automatically mine knowledge graphs to map these relationships between several IoT patterns. The end result is a semantic knowledge graph database which outlines patterns as vertices and their relations as edges. We have identified four main relationships between the IoT patterns-a pattern is similar to another pattern if it addresses the same use case problem, a large-scale pattern uses a small- scale pattern in a lower level layer, a large pattern is composed of multiple smaller scale patterns underneath it, and patterns complement and combine with each other to resolve a given use case problem. Our results show some promising prospects towards the use of machine learning techniques to generate an automated repository to organise the IoT patterns, which are usually extracted at various levels of abstraction and granularity.</span>

scholarly journals A Novel, Efficient, and Secure Anomaly Detection Technique Using DWU-ODBN for IoT-Enabled Multimedia Communication Systems

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
M. Sathya ◽  
M. Jeyaselvi ◽  
Lalitha Krishnasamy ◽  
Mohammad Mazyad Hazzazi ◽  
Prashant Kumar Shukla ◽  
...  
Keyword(s):  
Internet Of Things ◽  
Communication Systems ◽  
Missing Values ◽  
Attack Detection ◽  
Test Method ◽  
Smart Devices ◽  
The Internet ◽  
Existing Problems ◽  
The Internet Of Things ◽  
Open Nature

The Internet of Things (IoT) is enhancing our lives in a variety of structures, which consists of smarter cities, agribusiness, and e-healthcare, among others. Even though the Internet of Things has many features with the consumer Internet of Things, the open nature of smart devices and their worldwide connection make IoT networks vulnerable to a variety of assaults. Several approaches focused on attack detection in Internet of Things devices, which has the longest calculation times and the lowest accuracy issues. It is proposed in this paper that an attack detection framework for Internet of Things devices, based on the DWU-ODBN method, be developed to alleviate the existing problems. At the end of the process, the proposed method is used to identify the source of the assault. It comprises steps such as preprocessing, feature extraction, feature selection, and classification to identify the source of the attack. A random oversampler is used to preprocess the input data by dealing with NaN values, categorical features, missing values, and unbalanced datasets before being used to deal with the imbalanced dataset. When the data has been preprocessed, it is then sent to the MAD Median-KS test method, which is used to extract features from the dataset. To categorize the data into attack and nonattack categories, the features are classified using the dual weight updation-based optimal deep belief network (DWU-ODBN) classification technique, which is explained in more detail below. According to the results of the experimental assessment, the proposed approach outperforms existing methods in terms of detecting intrusions and assaults. The proposed work achieves 77 seconds to achieve the attack detection with an accuracy rate of 98.1%.

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