The Experts below are selected from a list of 49269 Experts worldwide ranked by ideXlab platform

Raj Jain - One of the best experts on this subject based on the ideXlab platform.

  • machine learning based network vulnerability analysis of Industrial Internet of things
    arXiv: Cryptography and Security, 2019
    Co-Authors: Maede Zolanvari, Lav Gupta, Marcio A. Teixeira, Khaled M. Khan, Raj Jain
    Abstract:

    It is critical to secure the Industrial Internet of Things (IIoT) devices because of potentially devastating consequences in case of an attack. Machine learning and big data analytics are the two powerful leverages for analyzing and securing the Internet of Things (IoT) technology. By extension, these techniques can help improve the security of the IIoT systems as well. In this paper, we first present common IIoT protocols and their associated vulnerabilities. Then, we run a cyber-vulnerability assessment and discuss the utilization of machine learning in countering these susceptibilities. Following that, a literature review of the available intrusion detection solutions using machine learning models is presented. Finally, we discuss our case study, which includes details of a real-world testbed that we have built to conduct cyber-attacks and to design an intrusion detection system (IDS). We deploy backdoor, command injection, and Structured Query Language (SQL) injection attacks against the system and demonstrate how a machine learning based anomaly detection system can perform well in detecting these attacks. We have evaluated the performance through representative metrics to have a fair point of view on the effectiveness of the methods.

  • Machine Learning-Based Network Vulnerability Analysis of Industrial Internet of Things
    IEEE Internet of Things Journal, 2019
    Co-Authors: Maede Zolanvari, Lav Gupta, Marcio A. Teixeira, Khaled M. Khan, Raj Jain
    Abstract:

    It is critical to secure the Industrial Internet of Things (IIoT) devices because of potentially devastating consequences in case of an attack. Machine learning (ML) and big data analytics are the two powerful leverages for analyzing and securing the Internet of Things (IoT) technology. By extension, these techniques can help improve the security of the IIoT systems as well. In this paper, we first present common IIoT protocols and their associated vulnerabilities. Then, we run a cyber-vulnerability assessment and discuss the utilization of ML in countering these susceptibilities. Following that, a literature review of the available intrusion detection solutions using ML models is presented. Finally, we discuss our case study, which includes details of a real-world testbed that we have built to conduct cyber-attacks and to design an intrusion detection system (IDS). We deploy backdoor, command injection, and Structured Query Language (SQL) injection attacks against the system and demonstrate how a ML-based anomaly detection system can perform well in detecting these attacks. We have evaluated the performance through representative metrics to have a fair point of view on the effectiveness of the methods.

Maede Zolanvari - One of the best experts on this subject based on the ideXlab platform.

  • machine learning based network vulnerability analysis of Industrial Internet of things
    arXiv: Cryptography and Security, 2019
    Co-Authors: Maede Zolanvari, Lav Gupta, Marcio A. Teixeira, Khaled M. Khan, Raj Jain
    Abstract:

    It is critical to secure the Industrial Internet of Things (IIoT) devices because of potentially devastating consequences in case of an attack. Machine learning and big data analytics are the two powerful leverages for analyzing and securing the Internet of Things (IoT) technology. By extension, these techniques can help improve the security of the IIoT systems as well. In this paper, we first present common IIoT protocols and their associated vulnerabilities. Then, we run a cyber-vulnerability assessment and discuss the utilization of machine learning in countering these susceptibilities. Following that, a literature review of the available intrusion detection solutions using machine learning models is presented. Finally, we discuss our case study, which includes details of a real-world testbed that we have built to conduct cyber-attacks and to design an intrusion detection system (IDS). We deploy backdoor, command injection, and Structured Query Language (SQL) injection attacks against the system and demonstrate how a machine learning based anomaly detection system can perform well in detecting these attacks. We have evaluated the performance through representative metrics to have a fair point of view on the effectiveness of the methods.

  • Machine Learning-Based Network Vulnerability Analysis of Industrial Internet of Things
    IEEE Internet of Things Journal, 2019
    Co-Authors: Maede Zolanvari, Lav Gupta, Marcio A. Teixeira, Khaled M. Khan, Raj Jain
    Abstract:

    It is critical to secure the Industrial Internet of Things (IIoT) devices because of potentially devastating consequences in case of an attack. Machine learning (ML) and big data analytics are the two powerful leverages for analyzing and securing the Internet of Things (IoT) technology. By extension, these techniques can help improve the security of the IIoT systems as well. In this paper, we first present common IIoT protocols and their associated vulnerabilities. Then, we run a cyber-vulnerability assessment and discuss the utilization of ML in countering these susceptibilities. Following that, a literature review of the available intrusion detection solutions using ML models is presented. Finally, we discuss our case study, which includes details of a real-world testbed that we have built to conduct cyber-attacks and to design an intrusion detection system (IDS). We deploy backdoor, command injection, and Structured Query Language (SQL) injection attacks against the system and demonstrate how a ML-based anomaly detection system can perform well in detecting these attacks. We have evaluated the performance through representative metrics to have a fair point of view on the effectiveness of the methods.

Yi Qia - One of the best experts on this subject based on the ideXlab platform.

  • Resource Trading in Blockchain-Based Industrial Internet of Things
    IEEE Transactions on Industrial Informatics, 2019
    Co-Authors: Haipeng Yao, Tianle Mai, Jingjing Wang, Yi Qia
    Abstract:

    Past few years have witnessed the compelling applications of the blockchain technique in our daily life ranging from the financial market to health care. Considering the integration of the blockchain technique and the Industrial Internet of Things (IoT), blockchain may act as a distributed ledger for beneficially establishing a decentralized autonomous trading platform for Industrial IoT (IIoT) networks. However, the power and computation constraints prevent IoT devices from directly participating in this proof-of-work process. As a remedy, in this treatise, the cloud computing service is introduced into the blockchain platform for the sake of assisting to offload computational task from the IIoT network itself. In addition, we study the resource management and pricing problem between the cloud provider and miners. More explicitly, we model the interaction between the cloud provider and miners as a Stackelberg game, where the leader, i.e., cloud provider, makes the price first, and then miners act as the followers. Moreover, in order to find the Nash equilibrium of the proposed Stackelberg game, a multiagent reinforcement learning algorithm is conceived for searching the near-optimal policy. Finally, extensive simulations are conducted to evaluate our proposed algorithm in comparison to some state-of-the-art schemes.

Xin Peng - One of the best experts on this subject based on the ideXlab platform.

  • a secure fabric blockchain based data transmission technique for Industrial Internet of things
    IEEE Transactions on Industrial Informatics, 2019
    Co-Authors: Wei Liang, Mingdong Tang, Jing Long, Xin Peng
    Abstract:

    The previous blockchain data transmission techniques in Industrial Internet of Things (IoT) have low security, high management cost of the trading center, and big difficulty in supervision. To address these issues, this paper proposes a secure FaBric blockchain-based data transmission technique for Industrial IoT. This technique uses the blockchain-based dynamic secret sharing mechanism. A reliable trading center is realized using the power blockchain sharing model, which can also share power trading books. The power data consensus mechanism and dynamic linked storage are designed to realize the secure matching of the power data transmission. Experiments show that the optimized FaBric power data storage and transmission has high security and reliability. The proposed technique can improve the transmission rate and packet receiving rate by 12% and 13%, respectively. Moreover, the proposed technique has good superiority in sharing management and decentralization.

Lav Gupta - One of the best experts on this subject based on the ideXlab platform.

  • machine learning based network vulnerability analysis of Industrial Internet of things
    arXiv: Cryptography and Security, 2019
    Co-Authors: Maede Zolanvari, Lav Gupta, Marcio A. Teixeira, Khaled M. Khan, Raj Jain
    Abstract:

    It is critical to secure the Industrial Internet of Things (IIoT) devices because of potentially devastating consequences in case of an attack. Machine learning and big data analytics are the two powerful leverages for analyzing and securing the Internet of Things (IoT) technology. By extension, these techniques can help improve the security of the IIoT systems as well. In this paper, we first present common IIoT protocols and their associated vulnerabilities. Then, we run a cyber-vulnerability assessment and discuss the utilization of machine learning in countering these susceptibilities. Following that, a literature review of the available intrusion detection solutions using machine learning models is presented. Finally, we discuss our case study, which includes details of a real-world testbed that we have built to conduct cyber-attacks and to design an intrusion detection system (IDS). We deploy backdoor, command injection, and Structured Query Language (SQL) injection attacks against the system and demonstrate how a machine learning based anomaly detection system can perform well in detecting these attacks. We have evaluated the performance through representative metrics to have a fair point of view on the effectiveness of the methods.

  • Machine Learning-Based Network Vulnerability Analysis of Industrial Internet of Things
    IEEE Internet of Things Journal, 2019
    Co-Authors: Maede Zolanvari, Lav Gupta, Marcio A. Teixeira, Khaled M. Khan, Raj Jain
    Abstract:

    It is critical to secure the Industrial Internet of Things (IIoT) devices because of potentially devastating consequences in case of an attack. Machine learning (ML) and big data analytics are the two powerful leverages for analyzing and securing the Internet of Things (IoT) technology. By extension, these techniques can help improve the security of the IIoT systems as well. In this paper, we first present common IIoT protocols and their associated vulnerabilities. Then, we run a cyber-vulnerability assessment and discuss the utilization of ML in countering these susceptibilities. Following that, a literature review of the available intrusion detection solutions using ML models is presented. Finally, we discuss our case study, which includes details of a real-world testbed that we have built to conduct cyber-attacks and to design an intrusion detection system (IDS). We deploy backdoor, command injection, and Structured Query Language (SQL) injection attacks against the system and demonstrate how a ML-based anomaly detection system can perform well in detecting these attacks. We have evaluated the performance through representative metrics to have a fair point of view on the effectiveness of the methods.