MADS Based on DL Techniques on the Internet of Things (IoT): Survey

Talal, Hussah; Zagrouba, Rachid

doi:10.3390/electronics10212598

Cited by 3 publications

(3 citation statements)

References 93 publications

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“…In paper [1], researchers conducted a comprehensive study on security solutions in IoT applications, IDS, MDS, and the role of AI in improving security in IoT. After they analyzed more than one hundred state of the art research studies, they recommended continuous research in the field of MDS based on DL in IoT environments.…”

Section: Highly Scalable Duementioning

confidence: 99%

“…Many countries and governments around the world are currently seeking to switch from the traditional environments to IoT environments in various fields, whether in industry, healthcare, oil and gas, or smart cities, in order to take advantage of the services and facilities provided by the IoT. But until now there are still concerns about the security and privacy [1][2][3]. The essential components of internet of things (IoT) systems are IoT network and IoT devices.…”

Section: Introductionmentioning

confidence: 99%

“…Therefore, using an MDS has an effective role in detecting strange malware behavior before causing any damage to the system. To improve the effectiveness of the IoT system, there is need to think of saving energy and time [1]. In traditional networks, the connection is between a server and a client, so high-security levels can be provided by adding a firewall and protection programs to the server.…”

Section: Introductionmentioning

confidence: 99%

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A Robust Framework for MADS Based on DL Techniques on the IoT

Talal

Zagrouba

2021

Electronics

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Day after day, new types of malware are appearing, renewing, and continuously developing, which makes it difficult to identify and stop them. Some attackers exploit artificial intelligence (AI) to create renewable malware with different signatures that are difficult to detect. Therefore, the performance of the traditional malware detection systems (MDS) and protection mechanisms were weakened so the malware can easily penetrate them. This poses a great risk to security in the internet of things (IoT) environment, which is interconnected and has big and continuous data. Penetrating any of the things in the IoT environment leads to a penetration of the entire IoT network and control different devices on it. Also, the penetration of the IoT environment leads to a violation of users’ privacy, and this may result in many risks, such as obtaining and stealing the user’s credit card information or theft of identity. Therefore, it is necessary to propose a robust framework for a MDS based on DL that has a high ability to detect renewable malware and propose malware Anomaly detection systems (MADS) work as a human mind to solve the problem of security in IoT environments. RoMADS model achieves high results: 99.038% for Accuracy, 99.997% for Detection rate. The experiment results overcome eighteen models of the previous research works related to this field, which proved the effectiveness of RoMADS framework for detecting malware in IoT.

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Section: Highly Scalable Duementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A Robust Framework for MADS Based on DL Techniques on the IoT

Talal

Zagrouba

2021

Electronics

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IoT device security audit tools: a comprehensive analysis and a layered architecture approach for addressing expanded security requirements

Kumar,

Kavisankar,

Venkatesan

et al. 2024

Int. J. Inf. Secur.

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IoT Capabilities Analysis by Using Optimized Machine Learning with Uncertain Traffic Modeling

Kumaran¹,

Balakannan²

2023

J. Uncert. Sys.

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The Internet of Things (IoT) incorporates software-based physical devices with sensors which require interconnectivity to communicate through the internet and be able to exchange data. IoT traffic can be thought of as the aggregate of packets created by various devices in various contexts, such as smart homes or smart cities. These settings contain several sensors, each of which is dedicated to a certain duty, such as monitoring schemes or gathering cyber-physical information. Thus, unlike internet traffic, which has some human-centric features, IoT traffic and sensor devices consistently perform different tasks and make uncertain quantities of data on a regular basis. The proposed method is based on calculating the entropy values of various uncertain traffic aspects. We describe a novel scheme for identifying IoT devices based on uncertain traffic entropy in this paper. The testbed consists of five popular Smart Home IoT devices with a Raspberry Pi acting as a gateway. Machine learning (ML) algorithms such as support vector machine (SVM), [Formula: see text]-nearest neighbor (KNN) and relevance vector machines (RVM) classifier are used to classify the devices which produce IoT traffic based on uncertain entropy significance. The learning rate of the classifier model is improved by using the flower pollination optimization (FPO) technique to increase the classifier performance. The suggested approach minimizes the amount of time spent on optimization operations while maintaining the predictive performance of the induced uncertain models. We work out the entropy standards of traffic characteristics and categorize the uncertain traffic using ML techniques. Our technique successfully identifies devices in a variety of uncertain network situations, with consistent performance in all scenarios. Our technique is also resistant to unpredictability in network behavior, with abnormalities or uncertainties propagating throughout the network.

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MADS Based on DL Techniques on the Internet of Things (IoT): Survey

Cited by 3 publications

References 93 publications

A Robust Framework for MADS Based on DL Techniques on the IoT

A Robust Framework for MADS Based on DL Techniques on the IoT

IoT device security audit tools: a comprehensive analysis and a layered architecture approach for addressing expanded security requirements

IoT Capabilities Analysis by Using Optimized Machine Learning with Uncertain Traffic Modeling

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