Internet of Things (IoT) devices exchange information directly between devices. They are more prone to vulnerability because of the design of the network layer in its architecture and also connected to the internet 24X7. IoT-based smart healthcare devices like patient monitoring cameras in hospital create life-saving data that must be shielded from intruders. Effective intrusion detection is required to safeguard sensitive private data before assault takes place due to the humongous data created by the IoT. This work proposes a 5-layered framework to find intrusion in large datasets. This work uses constructing new custom features to increase the learning rate and to reduce imperceptions during learning by the machine model. The proposed ACAAS algorithm obtains significant features and Recurrent Neural Networks with Long Short-Term Memory in both directions (RNNBiLSTM) is used to identify the assault to optimize the prediction performance accuracy by using the IoTID20 dataset to protect IoT networks. The experiment results provided Accuracy Rate of 99.16%, Error Rate of 0.008371%, Sensitivity Ratio of 99.89% and Specificity Ratio of 98.203% for IoTID20 with custom features using RNNBiLSTM. The obtained high accuracy rate shows the effectiveness of the system in protecting the network from intruders.
An efficient Intrusion Detection System has to be given high priority while connecting systems with a network to prevent the system before an attack happens. It is a big challenge to the network security group to prevent the system from a variable types of new attacks as technology is growing in parallel. In this paper, an efficient model to detect Intrusion is proposed to predict attacks with high accuracy and less false-negative rate by deriving custom features UNSW-CF by using the benchmark intrusion dataset UNSW-NB15. To reduce the learning complexity, Custom Features are derived and then Significant Features are constructed by applying meta-heuristic FPA (Flower Pollination algorithm) and MRMR (Minimal Redundancy and Maximum Redundancy) which reduces learning time and also increases prediction accuracy. ENC (ElasicNet Classifier), KRRC (Kernel Ridge Regression Classifier), IGBC (Improved Gradient Boosting Classifier) is employed to classify the attacks in the datasets UNSW-CF, UNSW and recorded that UNSW-CF with derived custom features using IGBC integrated with FPA provided high accuracy of 97.38% and a low error rate of 2.16%. Also, the sensitivity and specificity rate for IGB attains a high rate of 97.32% and 97.50% respectively.
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