Search

In this paper, we show a deep learning model for bot detection, named as DeBot, for industrial network traffic. DeBot uses a novel Cascade Forward Back Propagation Neural Network (CFBPNN) model with a subset of features using the Correlation-based Feature Selection (CFS) technique. A time series-based Nonlinear Auto-regressive Network with eXogenous inputs (NARX) technique analyzes the factors having a higher impact on the target variable and predict the behavioral pattern. To the best of our knowledge, we pioneer the use of optimal feature selection and integration with the cascading model of deep learning in bot detection of IIoTs. We conduct a thorough set of experiments on five popular bot datasets: NF-UNSW-NB15, NF-ToN-IoT, NF-BoT-IoT, NF-CSE-CIC-IDS2018, and ToN-IoT-Windows. We compare CFBPNN with other existing neural network models. We observe that CFBPNN in DeBot shows 100% accuracy in all the datasets with subset evaluation and obtains optimum F1-score and zero precision. [Display omitted] • We address the multi-layered network model with IIoT features to enable bot attack detection. • Our DeBot model evaluates the subset with optimal features with high impact on attack class and predict the risk factor. • We pioneer the implementation of time series-based Nonlinear Auto-Regressive eXogenous (NARX) model to predict the risk in the specified time gap. • We introduce a new cascading model with recurrent feature to preserve the relationship between input and output variables. [ABSTRACT FROM AUTHOR]

In this paper, we develop a feature extraction model using the amalgamation of machine and deep learning techniques for Industrial Internet of Thing (IIoT). We train the model with the most effective feature set evaluated using machine learning algorithms and deep learning feature extraction methods. We test these features with deep learning based network models for validation. We consider error metrics and accuracy as the major factors for combining the machine learning and deep learning techniques. The error rates are analysed using mean square error which show low error rate for the subset than the model tested for full dataset. Further, mean square error, accuracy rate and false values are analysed to test the performance of the proposed model. The comparative analysis with IIoT dataset and existing methods show that our approach is 25% better than the others. [Display omitted] • We address the multi-dimensional problem of the IIoT features to make an adequate feature extraction method. • We use machine learning classifier and wrapper subset evaluation technique with Naive-Bayes and meta bagging methods for feature selection. Deep learning auto encoders are implemented as a part of dimensionality reduction techniques and provides a amalgamation for the appropriateness of the features. We verify them with Principal Component Analysis (PCA) and ranking. Thus, only relevant features are extracted for analysis. • We create network models for analysing detection using deep learning Feed forward neural network and Elman Back propagation techniques for classification and detection with various training methods. [ABSTRACT FROM AUTHOR]

Internet-of-Things (IoTs) are one of the most promising technologies in the present world. It connects people, processes and things. The increasing number of devices in IoT invoking various security and privacy issues. Authentication is one of them which should be measured at the very first level when a device comes into the network. Many authentication schemes are in use which follow the traditional cryptographic techniques of first-sign-then-encryption. The low performance and high complexity of these techniques also have urged the development of signcryption and lightweight signcryption techniques which are more beneficial for low-powered devices in IoTs. The literature study also provides a scope of improvement in the existing signcryption techniques. With this objectives, in this paper, the work shows a lightweight signcryption scheme for perception layer devices in IoT. It uses PHOTON-256 as a lightweight hash function which makes the proposed scheme less complex and faster. The performance of the scheme has been measured in terms of complexity, cost analysis and energy consumption. AVISPA validation is performed to evaluate security. The analysis of attacks, comparative experimentation and energy consumption confirm the efficiency of the scheme. [ABSTRACT FROM AUTHOR]