The Experts below are selected from a list of 5139 Experts worldwide ranked by ideXlab platform
Jawad Ahmad - One of the best experts on this subject based on the ideXlab platform.
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A Novel Attack Detection Scheme for the Industrial Internet of Things Using a Lightweight Random Neural Network
2020Co-Authors: Shahid Latif, Zeba Idrees, Jawad AhmadAbstract:The Industrial Internet of Things (IIoT) brings together many sensors, machines, industrial applications, databases, services, and people at work. The IIoT is improving our lives in several ways including smarter cities, agriculture, and e-healthcare, etc. Although the IIoT shares several characteristics with the consumer IoT, different cybersecurity mechanisms are adopted for both networks. Unlike consumer IoT solutions that are used by an individual user for a single purpose, IIoT solutions tend to be integrated into larger Operational systems. As a result, IIoT security solutions require additional planning and awareness to ensure the security and privacy of the system. In this paper, different cybersecurity attacks such as denial of service (DoS), Malicious Operation, Malicious control, data type probing, spying, scan, and wrong setup are predicted by applying machine learning techniques. To predict the aforementioned attacks, a novel lightweight random neural network (RaNN)-based prediction model has been proposed in this article. To investigate the performance of the RaNN-based prediction model, several evaluation parameters such as accuracy, precision, recall, and F1 score were calculated and compared with the traditional artificial neural network (ANN), support vector machine (SVM) and decision tree (DT). The evaluation results show that the proposed RaNN model achieves an accuracy of 99.20% for a learning rate of 0.01, with a prediction time of 34.51 milliseconds. Other performance parameters such as the precision, recall, and F1 score were 99.11%, 99.13%, and 99.20%, respectively. The proposed scheme improves the attack detection accuracy by an average of 5.65% compared to that of state-of-the-art machine learning schemes for IoT security.
Shahid Latif - One of the best experts on this subject based on the ideXlab platform.
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A Novel Attack Detection Scheme for the Industrial Internet of Things Using a Lightweight Random Neural Network
2020Co-Authors: Shahid Latif, Zeba Idrees, Jawad AhmadAbstract:The Industrial Internet of Things (IIoT) brings together many sensors, machines, industrial applications, databases, services, and people at work. The IIoT is improving our lives in several ways including smarter cities, agriculture, and e-healthcare, etc. Although the IIoT shares several characteristics with the consumer IoT, different cybersecurity mechanisms are adopted for both networks. Unlike consumer IoT solutions that are used by an individual user for a single purpose, IIoT solutions tend to be integrated into larger Operational systems. As a result, IIoT security solutions require additional planning and awareness to ensure the security and privacy of the system. In this paper, different cybersecurity attacks such as denial of service (DoS), Malicious Operation, Malicious control, data type probing, spying, scan, and wrong setup are predicted by applying machine learning techniques. To predict the aforementioned attacks, a novel lightweight random neural network (RaNN)-based prediction model has been proposed in this article. To investigate the performance of the RaNN-based prediction model, several evaluation parameters such as accuracy, precision, recall, and F1 score were calculated and compared with the traditional artificial neural network (ANN), support vector machine (SVM) and decision tree (DT). The evaluation results show that the proposed RaNN model achieves an accuracy of 99.20% for a learning rate of 0.01, with a prediction time of 34.51 milliseconds. Other performance parameters such as the precision, recall, and F1 score were 99.11%, 99.13%, and 99.20%, respectively. The proposed scheme improves the attack detection accuracy by an average of 5.65% compared to that of state-of-the-art machine learning schemes for IoT security.
Zeba Idrees - One of the best experts on this subject based on the ideXlab platform.
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A Novel Attack Detection Scheme for the Industrial Internet of Things Using a Lightweight Random Neural Network
2020Co-Authors: Shahid Latif, Zeba Idrees, Jawad AhmadAbstract:The Industrial Internet of Things (IIoT) brings together many sensors, machines, industrial applications, databases, services, and people at work. The IIoT is improving our lives in several ways including smarter cities, agriculture, and e-healthcare, etc. Although the IIoT shares several characteristics with the consumer IoT, different cybersecurity mechanisms are adopted for both networks. Unlike consumer IoT solutions that are used by an individual user for a single purpose, IIoT solutions tend to be integrated into larger Operational systems. As a result, IIoT security solutions require additional planning and awareness to ensure the security and privacy of the system. In this paper, different cybersecurity attacks such as denial of service (DoS), Malicious Operation, Malicious control, data type probing, spying, scan, and wrong setup are predicted by applying machine learning techniques. To predict the aforementioned attacks, a novel lightweight random neural network (RaNN)-based prediction model has been proposed in this article. To investigate the performance of the RaNN-based prediction model, several evaluation parameters such as accuracy, precision, recall, and F1 score were calculated and compared with the traditional artificial neural network (ANN), support vector machine (SVM) and decision tree (DT). The evaluation results show that the proposed RaNN model achieves an accuracy of 99.20% for a learning rate of 0.01, with a prediction time of 34.51 milliseconds. Other performance parameters such as the precision, recall, and F1 score were 99.11%, 99.13%, and 99.20%, respectively. The proposed scheme improves the attack detection accuracy by an average of 5.65% compared to that of state-of-the-art machine learning schemes for IoT security.
M M A Hashem - One of the best experts on this subject based on the ideXlab platform.
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attack and anomaly detection in iot sensors in iot sites using machine learning approaches
2019Co-Authors: Mahmudul Hasan, Md Milon Islam, Ishrak Islam Zarif, M M A HashemAbstract:Abstract Attack and anomaly detection in the Internet of Things (IoT) infrastructure is a rising concern in the domain of IoT. With the increased use of IoT infrastructure in every domain, threats and attacks in these infrastructures are also growing commensurately. Denial of Service, Data Type Probing, Malicious Control, Malicious Operation, Scan, Spying and Wrong Setup are such attacks and anomalies which can cause an IoT system failure. In this paper, performances of several machine learning models have been compared to predict attacks and anomalies on the IoT systems accurately. The machine learning (ML) algorithms that have been used here are Logistic Regression (LR), Support Vector Machine (SVM), Decision Tree (DT), Random Forest (RF), and Artificial Neural Network (ANN). The evaluation metrics used in the comparison of performance are accuracy, precision, recall, f1 score, and area under the Receiver Operating Characteristic Curve. The system obtained 99.4% test accuracy for Decision Tree, Random Forest, and ANN. Though these techniques have the same accuracy, other metrics prove that Random Forest performs comparatively better.
Janardan Tayade - One of the best experts on this subject based on the ideXlab platform.
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6 Review on Passive DNS Analysis for Finding Malicious Domain
2016Co-Authors: Ku. Vijayalaxmi, Janardan TayadeAbstract:The domain name service (DNS) plays an important role in the Operation of the Internet, providing a two-way mapping between domain names and their numerical identifiers. Given its fundamental role, it is not surprising that a wide variety of Malicious activities involve the domain name service in one way or another. For example, bots resolve DNS names to locate their command and control servers, and spam mails contain URLs that link to domains that resolve to scam servers. Thus, it seems beneficial to monitor the use of the DNS system for signs that indicate that a certain name is used as part of a Malicious Operation. In this paper, we introduce EXPOSURE, a system that employs large-scale, passive DNS analysis techniques to detect domains that are involved in Malicious activity. We use 15 features that we extract from the DNS traffic that allow us to characterize different properties of DNS names and the ways that they are queried. Our experiments with a large, real-world data set consisting of 100 billion DNS requests, and a real-life deployment for two weeks in an ISP show that our approach is scalable and that we are able to automatically identify unknown Malicious domains that are misused in a variety of Malicious activity (such as for botnet command and control, spamming, and phishing)