Innovative Defense: Deep Learning-Powered Intrusion Detection for IoT Networks

Security is considered as one of the primary characteristics in technical setup, as rise of IoT devices can be prone to various challenges, thus is extremely important to detect the attacks in order to prevent horrendous consequences, hence IDS (Intrusion Detection System) is utilized. Typically, different tactics are used for identification of attacks, however, manual approaches are considered to be time consuming and prone to error. Thus, AI based approaches are utilized for effective detection of attacks, however, prevailing works often lack in terms of accuracy and primarily focuses on binary classification of attacks. Hence, in order to overcome these pitfalls proposed framework utilizes proposed UN (Unify Net) and proposed NSSN (Neutro Sequential Sense Net) functions. Proposed UN model enhances the spatial features learning the property of LSTM model which amplifies the efficacy of the classifiers for intrusion detection. After employing proposed UN, proposed NSSN is primarily used for handling the issues associated to ambiguity information in intrusion data by extracting various deep features and helps in improving the performance and precision of model for multiclass classification and aids in preventing overfitting of the model. Dataset used in the model is IoT device network logs dataset, where five attacks and non-attack is specified. Eventually, the performance of the proposed mechanism is gauged by means of evaluation metrics. Besides, comparative analysis is carried out to compare the performance of the model and its ability to identify multiple attacks present in IoT network.