scholarly journals SMOTE-DRNN: A Deep Learning Algorithm for Botnet Detection in the Internet-of-Things Networks

Sensors ◽  
2021 ◽  
Vol 21 (9) ◽  
pp. 2985
Author(s):  
Segun I. Popoola ◽  
Bamidele Adebisi ◽  
Ruth Ande ◽  
Mohammad Hammoudeh ◽  
Kelvin Anoh ◽  
...  
Keyword(s):  
Deep Learning ◽  
Internet Of Things ◽  
Network Traffic ◽  
State Of The Art ◽  
Classification Performance ◽  
The Internet ◽  
Traffic Data ◽  
Distributed Resources ◽  
Deep Recurrent Neural Network ◽  
The Internet Of Things

Nowadays, hackers take illegal advantage of distributed resources in a network of computing devices (i.e., botnet) to launch cyberattacks against the Internet of Things (IoT). Recently, diverse Machine Learning (ML) and Deep Learning (DL) methods were proposed to detect botnet attacks in IoT networks. However, highly imbalanced network traffic data in the training set often degrade the classification performance of state-of-the-art ML and DL models, especially in classes with relatively few samples. In this paper, we propose an efficient DL-based botnet attack detection algorithm that can handle highly imbalanced network traffic data. Specifically, Synthetic Minority Oversampling Technique (SMOTE) generates additional minority samples to achieve class balance, while Deep Recurrent Neural Network (DRNN) learns hierarchical feature representations from the balanced network traffic data to perform discriminative classification. We develop DRNN and SMOTE-DRNN models with the Bot-IoT dataset, and the simulation results show that high-class imbalance in the training data adversely affects the precision, recall, F1 score, area under the receiver operating characteristic curve (AUC), geometric mean (GM) and Matthews correlation coefficient (MCC) of the DRNN model. On the other hand, the SMOTE-DRNN model achieved better classification performance with 99.50% precision, 99.75% recall, 99.62% F1 score, 99.87% AUC, 99.74% GM and 99.62% MCC. Additionally, the SMOTE-DRNN model outperformed state-of-the-art ML and DL models.

A Novel Insider Attack and Machine Learning Based Detection for the Internet of Things

2021 ◽  
Vol 2 (4) ◽  
pp. 1-23
Author(s):  
Morshed Chowdhury ◽  
Biplob Ray ◽  
Sujan Chowdhury ◽  
Sutharshan Rajasegarar
Keyword(s):  
Machine Learning ◽  
Internet Of Things ◽  
Network Traffic ◽  
The Internet ◽  
Traffic Data ◽  
Lossy Networks ◽  
Insider Attack ◽  
Iot Devices ◽  
The Impact ◽  
The Internet Of Things

Due to the widespread functional benefits, such as supporting internet connectivity, having high visibility and enabling easy connectivity between sensors, the Internet of Things (IoT) has become popular and used in many applications, such as for smart city, smart health, smart home, and smart vehicle realizations. These IoT-based systems contribute to both daily life and business, including sensitive and emergency situations. In general, the devices or sensors used in the IoT have very limited computational power, storage capacity, and communication capabilities, but they help to collect a large amount of data as well as maintain communication with the other devices in the network. Since most of the IoT devices have no physical security, and often are open to everyone via radio communication and via the internet, they are highly vulnerable to existing and emerging novel security attacks. Further, the IoT devices are usually integrated with the corporate networks; in this case, the impact of attacks will be much more significant than operating in isolation. Due to the constraints of the IoT devices, and the nature of their operation, existing security mechanisms are less effective for countering the attacks that are specific to the IoT-based systems. This article presents a new insider attack, named loophole attack , that exploits the vulnerabilities present in a widely used IPv6 routing protocol in IoT-based systems, called RPL (Routing over Low Power and Lossy Networks). To protect the IoT system from this insider attack, a machine learning based security mechanism is presented. The proposed attack has been implemented using a Contiki IoT operating system that runs on the Cooja simulator, and the impacts of the attack are analyzed. Evaluation on the collected network traffic data demonstrates that the machine learning based approaches, along with the proposed features, help to accurately detect the insider attack from the network traffic data.

scholarly journals Functioning of the Internet of Things Networks with Moving Nodes

Telecom IT ◽  
2019 ◽  
Vol 7 (3) ◽  
pp. 31-36
Author(s):  
A. Marochkina ◽  
А. Paramonov
Keyword(s):  
Internet Of Things ◽  
Network Traffic ◽  
The Internet ◽  
Traffic Stream ◽  
The Internet Of Things ◽  
Area Of Application ◽  
Self Organizing

The area of application for the Internet of Things networks is vast. One of the main uses for such a net-work is the organization of network traffic. A traffic stream can be considered as a self-organizing net-work with moving nodes. This article describes the various features of such networks. Models with vari-ous mobility, velocity and density parameters of nodes are considered for studying the routes in this networks.

Role of the Internet of Things and deep learning for the growth of healthcare technology

2021 ◽  
pp. 113-127
Author(s):  
Dinesh Bhatia ◽  
S. Bagyaraj ◽  
S. Arun Karthick ◽  
Animesh Mishra ◽  
Amit Malviya
Keyword(s):  
Deep Learning ◽  
Internet Of Things ◽  
The Internet ◽  
Healthcare Technology ◽  
The Internet Of Things

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.

Sign in / Sign up

Export Citation Format

Share Document