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• W3183353669 abstract "One of the most basic characteristic features of every smart device in a network based on the Internet of Things (IoT) is to gather a larger set of data that has been created and then transfer the gathered data to the destination/receiver server through the internet. Thus, IoT-based networks are most vulnerable to simple or complex attacks that need to be identified in the early stage of data transmission for saving the network from these malicious attacks. The chief goal of the proposed work is to design and form the intelligent intrusion detection system (I-IDS) using the machine learning models such that the attacks can be identified in the IoT network. The model is built considering the normal and malicious attacks on the data that are generated in IoT smart environment. To simulate such a model, a testbed is built where a wireless router, a DHT11 sensor, and a node MCU are being used during the design phase. An attacker or adversarial system is built to perform poisoning and sniffing attacks using a laptop system. The node captures the sensor values and transmits the data to the ThinkSpeak platform, during the normal phase via the wireless gateway, and in the attack phase, the malicious attacker interprets the data, modifies it while transmitting from node to the ThinkSpeak server. Thus, the attack called Man-In-The-Middle (MITM) is performed and classified as abnormal data. Various machine learning algorithms are performed on the data, and finally, the results obtained using a probabilistic model called as Markov model have a high performance evaluated based on the I-IDS IoT network. The results obtained during the experimental analysis show that the Markov model has obtained a 100% detection rate and 19% of false alarm rate (FAR) with high precision and low error rate. The algorithms such as naïve Bayes classifier, support vector machine (SVM), decision tree, and Adaboost are considered in comparison with the Markov model. The optimal solution is obtained concerning other evaluation metrics like sensitivity, F1, and true-positive rate (TPR). Therefore, the integrated network of IoT-WSN with its performance metrics is tabulated to show the potentials of securing a network system. Additionally, the proposed work gives a high level of security for IoT smart environment as compared with the other machine learning algorithms using the novel technique of intelligent IDS." @default.
• W3183353669 created "2021-08-02" @default.
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• W3183353669 date "2021-07-19" @default.
• W3183353669 modified "2023-10-10" @default.
• W3183353669 title "IoT-based smart environment using intelligent intrusion detection system" @default.
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• W3183353669 doi "https://doi.org/10.1007/s00500-021-06028-1" @default.
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