This study focus on Machine Learning techniques for Internet of Things security threats detection... more This study focus on Machine Learning techniques for Internet of Things security threats detection. It seeks to investigate the feasible of using auto-encoders to detect IoT botnets. Botnets can develop DDoS attacks and present a major security concern in IoT networks, as there is no single method has demonstrated the potential to address this security threat. These methods often fail to meet IoT environments requirements, such as processing power and energy consumption. Auto-encoders offers one of the solutions to botnet detection. Future research needs to explore the opportunities that auto-encoders present in the detection of IoT botnets.
2019 International Conference on Computer and Information Sciences (ICCIS), 2019
Despite the benefits and convenience that are brought by technology, risks are also engulfed in t... more Despite the benefits and convenience that are brought by technology, risks are also engulfed in the use of technology. To foresee the probable risks, and come up with the appropriate countermeasures, a comprehensive examination of the mechanism of risk assessment we currently have is necessary. Therefore, in this paper, we present a comprehensive study of the current approaches for information security risk assessment. In addition, we discuss the three categories of the risk assessment approaches which are: qualitative, quantitative and hybrid. We also illustrate the advantages and limitations of each risk assessment category.
International Journal of Communication Networks and Information Security (IJCNIS)
While the IoT offers important benefits and opportunities for users, the technology raises variou... more While the IoT offers important benefits and opportunities for users, the technology raises various security issues and threats. These threats may include spreading IoT botnets through IoT devices which are the common and most malicious security threat in the world of internet. Protecting the IoT devices against these threats and attacks requires efficient detection. While we need to take into consideration IoT devices memory capacity limitation and low power processors. In this paper, we will focus in proposing low power consumption Machine Learning (ML) techniques for detecting IoT botnet attacks using Random forest as ML-based detection method and describing IoT common attacks with its countermeasures. The experimental result of our proposed solution shows higher accuracy. From the results, we conclude that IoT botnet detection is possible; achieving a higher accuracy rate as an experimental result indicates an accuracy rate of over 99.99% where the true positive rate is 1.000 and...
This study focus on Machine Learning techniques for Internet of Things security threats detection... more This study focus on Machine Learning techniques for Internet of Things security threats detection. It seeks to investigate the feasible of using auto-encoders to detect IoT botnets. Botnets can develop DDoS attacks and present a major security concern in IoT networks, as there is no single method has demonstrated the potential to address this security threat. These methods often fail to meet IoT environments requirements, such as processing power and energy consumption. Auto-encoders offers one of the solutions to botnet detection. Future research needs to explore the opportunities that auto-encoders present in the detection of IoT botnets.
2019 International Conference on Computer and Information Sciences (ICCIS), 2019
Despite the benefits and convenience that are brought by technology, risks are also engulfed in t... more Despite the benefits and convenience that are brought by technology, risks are also engulfed in the use of technology. To foresee the probable risks, and come up with the appropriate countermeasures, a comprehensive examination of the mechanism of risk assessment we currently have is necessary. Therefore, in this paper, we present a comprehensive study of the current approaches for information security risk assessment. In addition, we discuss the three categories of the risk assessment approaches which are: qualitative, quantitative and hybrid. We also illustrate the advantages and limitations of each risk assessment category.
International Journal of Communication Networks and Information Security (IJCNIS)
While the IoT offers important benefits and opportunities for users, the technology raises variou... more While the IoT offers important benefits and opportunities for users, the technology raises various security issues and threats. These threats may include spreading IoT botnets through IoT devices which are the common and most malicious security threat in the world of internet. Protecting the IoT devices against these threats and attacks requires efficient detection. While we need to take into consideration IoT devices memory capacity limitation and low power processors. In this paper, we will focus in proposing low power consumption Machine Learning (ML) techniques for detecting IoT botnet attacks using Random forest as ML-based detection method and describing IoT common attacks with its countermeasures. The experimental result of our proposed solution shows higher accuracy. From the results, we conclude that IoT botnet detection is possible; achieving a higher accuracy rate as an experimental result indicates an accuracy rate of over 99.99% where the true positive rate is 1.000 and...
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Papers by REEM ALHAJRI