The Experts below are selected from a list of 136404 Experts worldwide ranked by ideXlab platform
Mohsen Guizani - One of the best experts on this subject based on the ideXlab platform.
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CorrAUC: A Malicious Bot-IoT Traffic Detection Method in IoT Network Using Machine-Learning Techniques
IEEE Internet of Things Journal, 2021Co-Authors: Muhammad Shafiq, Zhihong Tian, Ali Kashif Bashir, Mohsen GuizaniAbstract:Identification of anomaly and malicious traffic in the Internet-of-Things (IoT) network is essential for the IoT security to keep eyes and block unwanted traffic flows in the IoT network. For this purpose, numerous machine-learning (ML) technique models are presented by many researchers to block malicious traffic flows in the IoT network. However, due to the inappropriate feature selection, several ML models prone misclassify mostly malicious traffic flows. Nevertheless, the significant problem still needs to be studied more in-depth that is how to select effective features for accurate malicious traffic detection in the IoT network. To address the problem, a new framework model is proposed. First, a novel feature selection metric approach named CorrAUC is proposed, and then based on CorrAUC, a new feature selection algorithm named CorrAUC is developed and designed, which is based on the wrapper technique to filter the features accurately and select effective features for the selected ML algorithm by using the area under the curve (AUC) metric. Then, we applied the integrated TOPSIS and Shannon entropy based on a bijective soft set to validate selected features for malicious traffic identification in the IoT network. We evaluate our proposed approach by using the Bot-IoT data set and four different ML algorithms. The experimental results analysis showed that our proposed method is efficient and can achieve >96% results on average.
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selection of effective machine learning algorithm and bot IoT attacks traffic identification for internet of things in smart city
Future Generation Computer Systems, 2020Co-Authors: Muhammad Shafiq, Zhihong Tian, Yanbin Sun, Mohsen GuizaniAbstract:Abstract Identifying cyber attacks traffic is very important for the Internet of things (IoT) security in smart city. Recently, the research community in the field of IoT Security endeavor hard to build anomaly, intrusion and cyber attacks traffic identification model using Machine Learning (ML) algorithms for IoT security analysis. However, the critical and significant problem still not studied in depth that is how to select an effective ML algorithm when there are numbers of ML algorithms for cyber attacks detection system for IoT security. In this paper, we proposed a new framework model and a hybrid algorithm to solve this problem. Firstly BoT-IoT identification dataset is applied and its 44 effective features are selected from a number of features for the machine learning algorithm. Then five effective machine learning algorithm is selected for the identification of malicious and anomaly traffic identification and also select the most widely ML algorithm performance evaluation metrics. To find out which ML algorithm is effective and should be used to select for IoT anomaly and intrusion traffic identification, a bijective soft set approach and its algorithm is applied. Then we applied the proposed algorithm based on bijective soft set approach. Our experimental results show that the proposed model with the algorithm is effective for the selection ML algorithm out of numbers of ML algorithms.
Athanasios V. Vasilakos - One of the best experts on this subject based on the ideXlab platform.
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lam cIoT lightweight authentication mechanism in cloud based IoT environment
Journal of Network and Computer Applications, 2020Co-Authors: Mohammad Wazid, Vivekananda Bhat K, Athanasios V. VasilakosAbstract:Abstract Internet of Things (IoT) becomes a new era of the Internet, which consists of several connected physical smart objects (i.e., sensing devices) through the Internet. IoT has different types of applications, such as smart home, wearable devices, smart connected vehicles, industries, and smart cities. Therefore, IoT based applications become the essential parts of our day-to-day life. In a cloud-based IoT environment, cloud platform is used to store the data accessed from the IoT sensors. Such an environment is greatly scalable and it supports real-time event processing which is very important in several scenarios (i.e., IoT sensors based surveillance and monitoring). Since some applications in cloud-based IoT are very critical, the information collected and sent by IoT sensors must not be leaked during the communication. To accord with this, we design a new lightweight authentication mechanism in cloud-based IoT environment, called LAM-CIoT. By using LAM-CIoT, an authenticated user can access the data of IoT sensors remotely. LAM-CIoT applies efficient “one-way cryptographic hash functions” along with “bitwise XOR operations”. In addition, fuzzy extractor mechanism is also employed at the user's end for local biometric verification. LAM-CIoT is methodically analyzed for its security part through the formal security using the broadly-accepted “Real-Or-Random (ROR)” model, formal security verification using the widely-used “Automated Validation of Internet Security Protocols and Applications (AVISPA)” tool as well as the informal security analysis. The performance analysis shows that LAM-CIoT offers better security, and low communication and computation overheads as compared to the closely related authentication schemes. Finally, LAM-CIoT is evaluated using the NS2 network simulator for the measurement of network performance parameters that envisions the impact of LAM-CIoT on the network performance of LAM-CIoT and other schemes.
Benjamin Turnbull - One of the best experts on this subject based on the ideXlab platform.
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towards the development of realistic botnet dataset in the internet of things for network forensic analytics bot IoT dataset
Future Generation Computer Systems, 2019Co-Authors: Nickolaos Koroniotis, Nour Moustafa, Elena Sitnikova, Benjamin TurnbullAbstract:Abstract The proliferation of IoT systems, has seen them targeted by malicious third parties. To address this challenge, realistic protection and investigation countermeasures, such as network intrusion detection and network forensic systems, need to be effectively developed. For this purpose, a well-structured and representative dataset is paramount for training and validating the credibility of the systems. Although there are several network datasets, in most cases, not much information is given about the Botnet scenarios that were used. This paper proposes a new dataset, so-called Bot-IoT, which incorporates legitimate and simulated IoT network traffic, along with various types of attacks. We also present a realistic testbed environment for addressing the existing dataset drawbacks of capturing complete network information, accurate labeling, as well as recent and complex attack diversity. Finally, we evaluate the reliability of the BoT-IoT dataset using different statistical and machine learning methods for forensics purposes compared with the benchmark datasets. This work provides the baseline for allowing botnet identification across IoT-specific networks. The Bot-IoT dataset can be accessed at Bot-IoT (2018) [1] .
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towards the development of realistic botnet dataset in the internet of things for network forensic analytics bot IoT dataset
arXiv: Cryptography and Security, 2018Co-Authors: Nickolaos Koroniotis, Nour Moustafa, Elena Sitnikova, Benjamin TurnbullAbstract:The proliferation of IoT systems, has seen them targeted by malicious third parties. To address this, realistic protection and investigation countermeasures need to be developed. Such countermeasures include network intrusion detection and network forensic systems. For that purpose, a well-structured and representative dataset is paramount for training and validating the credibility of the systems. Although there are several network, in most cases, not much information is given about the Botnet scenarios that were used. This paper, proposes a new dataset, Bot-IoT, which incorporates legitimate and simulated IoT network traffic, along with various types of attacks. We also present a realistic testbed environment for addressing the existing dataset drawbacks of capturing complete network information, accurate labeling, as well as recent and complex attack diversity. Finally, we evaluate the reliability of the BoT-IoT dataset using different statistical and machine learning methods for forensics purposes compared with the existing datasets. This work provides the baseline for allowing botnet identificaiton across IoT-specifc networks. The Bot-IoT dataset can be accessed at [1].
Muhammad Shafiq - One of the best experts on this subject based on the ideXlab platform.
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CorrAUC: A Malicious Bot-IoT Traffic Detection Method in IoT Network Using Machine-Learning Techniques
IEEE Internet of Things Journal, 2021Co-Authors: Muhammad Shafiq, Zhihong Tian, Ali Kashif Bashir, Mohsen GuizaniAbstract:Identification of anomaly and malicious traffic in the Internet-of-Things (IoT) network is essential for the IoT security to keep eyes and block unwanted traffic flows in the IoT network. For this purpose, numerous machine-learning (ML) technique models are presented by many researchers to block malicious traffic flows in the IoT network. However, due to the inappropriate feature selection, several ML models prone misclassify mostly malicious traffic flows. Nevertheless, the significant problem still needs to be studied more in-depth that is how to select effective features for accurate malicious traffic detection in the IoT network. To address the problem, a new framework model is proposed. First, a novel feature selection metric approach named CorrAUC is proposed, and then based on CorrAUC, a new feature selection algorithm named CorrAUC is developed and designed, which is based on the wrapper technique to filter the features accurately and select effective features for the selected ML algorithm by using the area under the curve (AUC) metric. Then, we applied the integrated TOPSIS and Shannon entropy based on a bijective soft set to validate selected features for malicious traffic identification in the IoT network. We evaluate our proposed approach by using the Bot-IoT data set and four different ML algorithms. The experimental results analysis showed that our proposed method is efficient and can achieve >96% results on average.
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selection of effective machine learning algorithm and bot IoT attacks traffic identification for internet of things in smart city
Future Generation Computer Systems, 2020Co-Authors: Muhammad Shafiq, Zhihong Tian, Yanbin Sun, Mohsen GuizaniAbstract:Abstract Identifying cyber attacks traffic is very important for the Internet of things (IoT) security in smart city. Recently, the research community in the field of IoT Security endeavor hard to build anomaly, intrusion and cyber attacks traffic identification model using Machine Learning (ML) algorithms for IoT security analysis. However, the critical and significant problem still not studied in depth that is how to select an effective ML algorithm when there are numbers of ML algorithms for cyber attacks detection system for IoT security. In this paper, we proposed a new framework model and a hybrid algorithm to solve this problem. Firstly BoT-IoT identification dataset is applied and its 44 effective features are selected from a number of features for the machine learning algorithm. Then five effective machine learning algorithm is selected for the identification of malicious and anomaly traffic identification and also select the most widely ML algorithm performance evaluation metrics. To find out which ML algorithm is effective and should be used to select for IoT anomaly and intrusion traffic identification, a bijective soft set approach and its algorithm is applied. Then we applied the proposed algorithm based on bijective soft set approach. Our experimental results show that the proposed model with the algorithm is effective for the selection ML algorithm out of numbers of ML algorithms.
Qiuting Huang - One of the best experts on this subject based on the ideXlab platform.
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ec gsm IoT network synchronization with support for large frequency offsets
arXiv: Signal Processing, 2018Co-Authors: Stefan Lippuner, Benjamin Weber, Matthias Korb, Mauro Salomon, Qiuting HuangAbstract:EDGE-based EC-GSM-IoT is a promising candidate for the billion-device cellular IoT (cIoT), providing similar coverage and battery life as NB-IoT. The goal of 20 dB coverage extension compared to EDGE poses significant challenges for the initial network synchronization, which has to be performed well below the thermal noise floor, down to an SNR of -8.5 dB. We present a low-complexity synchronization algorithm supporting up to 50 kHz initial frequency offset, thus enabling the use of a low-cost +/-25 ppm oscillator. The proposed algorithm does not only fulfill the 3GPP requirements, but surpasses them by 3 dB, enabling communication with an SNR of -11.5 dB or a maximum coupling loss of up to 170.5 dB.
