scholarly journals A Novel Framework to Classify Malware in MIPS Architecture-Based IoT Devices

2019 ◽  
Vol 2019 ◽  
pp. 1-13 ◽  
Author(s):  
Tran Nghi Phu ◽  
Kien Hoang Dang ◽  
Dung Ngo Quoc ◽  
Nguyen Tho Dai ◽  
Nguyen Ngoc Binh
Keyword(s):  
Industrial Revolution ◽  
Machine Learning Techniques ◽  
The Internet ◽  
The Real ◽  
Learning Techniques ◽  
Behavior System ◽  
Diverse Environment ◽  
New Type ◽  
Iot Devices ◽  
The Internet Of Things

Malware on devices connected to the Internet via the Internet of Things (IoT) is evolving and is a core component of the fourth industrial revolution. IoT devices use the MIPS architecture with a large proportion running on embedded Linux operating systems, but the automatic analysis of IoT malware has not been resolved. We proposed a framework to classify malware in IoT devices by using MIPS-based system behavior (system call—syscall) obtained from our F-Sandbox passive process and machine learning techniques. The F-Sandbox is a new type for IoT sandbox, automatically created from the real firmware of the specialized IoT devices, inheriting the specialized environment in the real firmware, therefore creating a diverse environment for sandboxing as an important characteristic of IoT sandbox. This framework classifies five families of IoT malware with F1-Weight = 97.44%.

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 Of The Issues And Challenges In IoT Security Using Machine Learning Techniques

2021 ◽  
Vol 9 (4) ◽  
pp. 499-502
Keyword(s):  
Machine Learning ◽  
Machine Learning Techniques ◽  
Security Threats ◽  
X Rays ◽  
Iot Security ◽  
Learning Techniques ◽  
Human Endeavor ◽  
Security Challenges ◽  
Iot Devices ◽  
The Internet Of Things

In a typical IoT network, a sensor connects to a controller using a wireless connection. Controllers collect data from sensors and sends the data for storage and analysis[1]. These controllers work with actuators that translate an electrical input to a physical action. The internet of things (IoT), have found application in different areas of human endeavor including healthcare, government, supply chain, cities, manufacturing, etc. and it is estimated that the number of connected devices will reach 50 billion by 2020[2] With the increasing number of devices comes an increase in the the varying number of security threats to the IoT network [3]. To contain these threats, a secure-by-design approach should be adopted as this will help the IoT devices to anticipate and neutralize the ever changing nature of the threats as against older systems where security was handled as it presents itself [2] This paper x-rays the security challenges in IoT networks and the application of machine learning (Supervised learning, Unsupervised learning and Reinforcement learning) in tackling the security challenges

Malware Threat in Internet of Things and Its Mitigation Analysis

2021 ◽  
pp. 371-387
Author(s):  
Shingo Yamaguchi ◽  
Brij Gupta
Keyword(s):  
Internet Of Things ◽  
Large Volume ◽  
Denial Of Service ◽  
The Internet ◽  
Distributed Denial Of Service ◽  
Ddos Attack ◽  
New Type ◽  
Iot Devices ◽  
Mitigation Methods ◽  
The Internet Of Things

This chapter introduces malware's threat in the internet of things (IoT) and then analyzes the mitigation methods against the threat. In September 2016, Brian Krebs' web site “Krebs on Security” came under a massive distributed denial of service (DDoS) attack. It reached twice the size of the largest attack in history. This attack was caused by a new type of malware called Mirai. Mirai primarily targets IoT devices such as security cameras and wireless routers. IoT devices have some properties which make them malware attack's targets such as large volume, pervasiveness, and high vulnerability. As a result, a DDoS attack launched by infected IoT devices tends to become massive and disruptive. Thus, the threat of Mirai is an extremely important issue. Mirai has been attracting a great deal of attention since its birth. This resulted in a lot of information related to IoT malware. Most of them came from not academia but industry represented by antivirus software makers. This chapter summarizes such information.

scholarly journals Multi Core DN​N based IDS for Botnet Attacks using KPCA Reduction Techniques

2021 ◽  
Author(s):  
Sharmila B S ◽  
Rohini Nagapadma
Keyword(s):  
Detection System ◽  
Research Work ◽  
Principal Component ◽  
Kernel Principal Component Analysis ◽  
The Internet ◽  
Reduction Techniques ◽  
Private Data ◽  
Learning Techniques ◽  
Iot Devices ◽  
The Internet Of Things

Abstract Research on network security has recently acquired attention in the field of the Internet of Things. In the context of security, most of the IoT devices with the internet are connected directly which results in the exploitation of private data. Nowadays, the fraudster will release novel attacks very frequently especially for IoT devices. As a result, the traditional sophisticated Intrusion Detection System (IDS) model is not suitable for the identification of vulnerabilities in IoT devices. In our research work, we propose MCDNN for IDS. MCDNN is Multi Core DNN with having parallel optimizer. Rather than a traditional dataset, this paper experiment is conducted on the BoTIoT dataset. Since IoT devices generate a huge volume of data, this work focuses on reducing huge datasets using Kernel Principal Component Analysis(KPCA) reduction technique with optimizer parallelly. To decrease false alarm rate and maintaining less computational power multi-core is introduced in our research work. This helps identification of vulnerabilities in IoT devices using deep learning techniques faster. Experimental results indicate that designing MCDNN based IDS with different optimizers parallelly achieved higher performance than those of other techniques.

scholarly journals Multiclass Classification Procedure for Detecting Attacks on MQTT-IoT Protocol

Complexity ◽  
2019 ◽  
Vol 2019 ◽  
pp. 1-11 ◽  
Author(s):  
Hector Alaiz-Moreton ◽  
Jose Aveleira-Mata ◽  
Jorge Ondicol-Garcia ◽  
Angel Luis Muñoz-Castañeda ◽  
Isaías García ◽  
...  
Keyword(s):  
Intrusion Detection ◽  
Ensemble Methods ◽  
Intrusion Detection Systems ◽  
Machine Learning Techniques ◽  
The Internet ◽  
Classification Models ◽  
Sensors And Actuators ◽  
Detection Systems ◽  
Learning Techniques ◽  
The Internet Of Things

The large number of sensors and actuators that make up the Internet of Things obliges these systems to use diverse technologies and protocols. This means that IoT networks are more heterogeneous than traditional networks. This gives rise to new challenges in cybersecurity to protect these systems and devices which are characterized by being connected continuously to the Internet. Intrusion detection systems (IDS) are used to protect IoT systems from the various anomalies and attacks at the network level. Intrusion Detection Systems (IDS) can be improved through machine learning techniques. Our work focuses on creating classification models that can feed an IDS using a dataset containing frames under attacks of an IoT system that uses the MQTT protocol. We have addressed two types of method for classifying the attacks, ensemble methods and deep learning models, more specifically recurrent networks with very satisfactory results.

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>

Malware Threat in Internet of Things and Its Mitigation Analysis

2020 ◽  
pp. 363-379
Author(s):  
Shingo Yamaguchi ◽  
Brij Gupta
Keyword(s):  
Internet Of Things ◽  
Large Volume ◽  
Denial Of Service ◽  
The Internet ◽  
Distributed Denial Of Service ◽  
Ddos Attack ◽  
New Type ◽  
Iot Devices ◽  
Mitigation Methods ◽  
The Internet Of Things

This chapter introduces malware's threat in the internet of things (IoT) and then analyzes the mitigation methods against the threat. In September 2016, Brian Krebs' web site “Krebs on Security” came under a massive distributed denial of service (DDoS) attack. It reached twice the size of the largest attack in history. This attack was caused by a new type of malware called Mirai. Mirai primarily targets IoT devices such as security cameras and wireless routers. IoT devices have some properties which make them malware attack's targets such as large volume, pervasiveness, and high vulnerability. As a result, a DDoS attack launched by infected IoT devices tends to become massive and disruptive. Thus, the threat of Mirai is an extremely important issue. Mirai has been attracting a great deal of attention since its birth. This resulted in a lot of information related to IoT malware. Most of them came from not academia but industry represented by antivirus software makers. This chapter summarizes such information.

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