A cloud-assisted malware detection and suppression framework for wireless multimedia system in IoT based on dynamic differential game

Zhou, Weiwei; Yu, Bin

doi:10.1109/cc.2018.8300282

Cited by 26 publications

(10 citation statements)

References 11 publications

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“…Support Vector Machines (SVM) SVM is a supervised ML algorithm with low computational complexity, used for classification and regression. It has the ability to work with binary as well as with multi-class environments [78], [79]. It classifies input data into n dimensional space and draws n − 1 hyperplane to divide the entire data points into groups.…”

Section: K-means Algorithmmentioning

confidence: 99%

Machine Learning in IoT Security: Current Solutions and Future Challenges

Hussain

Hassan

et al. 2020

IEEE Commun. Surv. Tutorials

593

270

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The future Internet of Things (IoT) will have a deep economical, commercial and social impact on our lives. The participating nodes in IoT networks are usually resourceconstrained, which makes them luring targets for cyber attacks. In this regard, extensive efforts have been made to address the security and privacy issues in IoT networks primarily through traditional cryptographic approaches. However, the unique characteristics of IoT nodes render the existing solutions insufficient to encompass the entire security spectrum of the IoT networks. This is, at least in part, because of the resource constraints, heterogeneity, massive real-time data generated by the IoT devices, and the extensively dynamic behavior of the networks. Therefore, Machine Learning (ML) and Deep Learning (DL) techniques, which are able to provide embedded intelligence in the IoT devices and networks, are leveraged to cope with different security problems. In this paper, we systematically review the security requirements, attack vectors, and the current security solutions for the IoT networks. We then shed light on the gaps in these security solutions that call for ML and DL approaches. We also discuss in detail the existing ML and DL solutions for addressing different security problems in IoT networks. At last, based on the detailed investigation of the existing solutions in the literature, we discuss the future research directions for ML-and DL-based IoT security.

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Section: K-means Algorithmmentioning

confidence: 99%

Machine Learning in IoT Security: Current Solutions and Future Challenges

Hussain

Hassan

et al. 2020

IEEE Commun. Surv. Tutorials

593

270

View full text Add to dashboard Cite

show abstract

“…For the detection of WMS malware, obtaining its network behaviors is critical. The malware detection scheme of Zhou et al [79] facilitated the collection of network behaviors in WMS using the data sniffer (DroidSniffer), combined with SVM, BP neural networks to detect malware and suppress malicious codes. In the experiment, the highest infection rate was only 22.17%, which proved that malware can be detected at a lower infection rate.…”

Section: ) Network Behaviorsmentioning

confidence: 99%

Research on Artificial Intelligence Enhancing Internet of Things Security: A Survey

et al. 2020

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Through three development routes of authentication, communication, and computing, the Internet of Things (IoT) has become a variety of innovative integrated solutions for specific applications. However, due to the openness, extensiveness and resource constraints of IoT, each layer of the three-tier IoT architecture suffers from a variety of security threats. In this work, we systematically review the particularity and complexity of IoT security protection, and then find that Artificial Intelligence (AI) methods such as Machine Learning (ML) and Deep Learning (DL) can provide new powerful capabilities to meet the security requirements of IoT. We analyze the technical feasibility of AI in solving IoT security problems and summarize a general process of AI solutions for IoT security. For four serious IoT security threats: device authentication, Denial of Service (DoS) and Distributed Denial of Service (DDoS) attacks defense, intrusion detection and malware detection, we summarize representative AI solutions and compare the different algorithms and technologies used by various solutions. It should be noted that although AI provides many new capabilities for the security protection of IoT, it also brings new potential challenges and possible negative effects to IoT in terms of data, algorithm and architecture. In the future, how to solve these challenges can serve as potential research directions.INDEX TERMS Artificial intelligence, deep learning, Internet of Things, machine learning, security.

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“…[26] and [27] have also published some recent works on Deep Reinforcement Learning (DRL). There are varieties of machine learning algorithms used in IoT which includes: NaiveBayes [28], K-Nearest neighbor [29], k-means algorithm [30], Random Forest and Decision Tree [31], Support Vector Machine (SVM) [32], [33], Recurrent Neural Networks (RNN) [34]- [36], Principal Component Analysis [37], Q-learning [30] and Deep Learning [38]. These algorithms can be used in addressing some research problems like authentication, attack detection and mitigation, distributed DoS attack, Anomaly/Intrusion Detection and malware analysis.…”

Section: Literature Reviewmentioning

confidence: 99%

Implementation of Cutting-Edge Internet Technology to the Improvement of Revenue Generation Using Cyber-Security

Joseph¹,

Udeze²,

Bukie³

et al. 2019

Preprint

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The need for the most accurate and flexible system of revenue collection from internal sources has become a matter of extreme urgency and importance in e-governance. This need underscores the eagerness on the part of the Government to look for a new principle and policy of revenue collection or to become aggressive and innovative in the mode of collecting revenue from existing sources using the present system. The Boards of some Governments in Africa, even up to the moment are facing a lot of setbacks in performing their tasks due to the manual system of revenue collection from the public. This can be improved through an effective collection of revenue using the most accurate and flexible system. Tax is usually collected in the form of specific sales tax, general sales tax, corporate income tax, individual income tax, property tax and inheritance tax. Problems such as high cost of collection, fraud, underpayment, leakage in revenue, poor access to information, poor tracking of defaulters is at the increase. As a result of this, there is need to computerize the revenue collection system. Computerized systems have proven to introduce massive efficiencies and quick collection of revenue from the public. This research work demonstrates how to design and implement an automated system of revenue collection and how to maintain a secured database for collected tax information. This research delves into the study of how machine learning algorithms and Software-defined Networks improve the security of such automated systems.

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A cloud-assisted malware detection and suppression framework for wireless multimedia system in IoT based on dynamic differential game

Cited by 26 publications

References 11 publications

Machine Learning in IoT Security: Current Solutions and Future Challenges

Machine Learning in IoT Security: Current Solutions and Future Challenges

Research on Artificial Intelligence Enhancing Internet of Things Security: A Survey

Implementation of Cutting-Edge Internet Technology to the Improvement of Revenue Generation Using Cyber-Security

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