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In this pandemic, providing a quality environment is considered one of the essential objectives of smart wellbeing observation. Therefore, the prediction of irregular events has become a fundamental requirement of assistive or clinical consideration. By concentrating on this need, a dew computation-inspired irregular physical event determination solution is presented to determine the symptoms of COVID-19 by analyzing the physical activities of the individuals at their initial stage. The benefit of the proposed solution is enhanced by forwarding the predicted outcomes and their occurrence within a speculated time on a private cloud database to decide the health seriousness. Furthermore, a dynamic decisive-module is introduced to notify medical specialists about the current wellbeing status of the individuals under monitoring. The real-time prediction efficiency of the proposed solution is determined by implementing and calculating the outcomes on both the dew and cloud platforms. The calculated outcomes exhibit the improved viability of the dew platform over the cloud platform by increasing the prediction speed of 46.27% for 40 and 45.54% for 30 frames per second. Moreover, the event prediction performance is justified over the state-of-the-art monitoring arrangements by achieving accuracy (92.88%), specificity (90.87%), sensitivity (88.26%) and F1-measure (89.53%) with the least decision-making delay. [ABSTRACT FROM AUTHOR]

Deep learning (DL) is gaining significant prevalence in every field of study due to its domination in training large data sets. However, several applications are utilizing machine learning (ML) methods from the past several years and reported good performance. However, their limitations in terms of data complexity give rise to DL methods. Intrusion detection is one of the prominent areas in which researchers are extending DL methods. Even though several excellent surveys cover the growing body of research on this subject, the literature lacks a detailed comparison of ML methods such as ANN, SVM, fuzzy approach, swarm intelligence and evolutionary computation methods in intrusion detection, particularly on recent research. In this context, the present paper deals with the systematic review of ML methods and DL methods in intrusion detection. In addition to reviewing ML and DL methods, this paper also focuses on benchmark datasets, performance evaluation measures and various applications of DL methods for intrusion detection. The present paper summarizes the recent work, compares their experimental results for detecting network intrusions. Furthermore, current research challenges are identified for helping fellow researchers in the era of DL-based intrusion detection. [ABSTRACT FROM AUTHOR]

Modern vehicles are increasingly getting connected within the vehicles, with other systems, leading to more concerns about security. Controller area network (CAN) has become a de-facto standard for connecting internal vehicles' components. However, it lacks security features. Conventional security mechanisms fail to protect in-vehicle networks from attacks, requiring the development of an effective intrusion detection system (IDS). This work develops an IDS for in-vehicle networks called IDS-IVN based on a compact representation of location invariant and time-variant traffic features using deep learning. The IDS-IVN uses convolutional neural and long–short-term memory networks as encoder/decoder functions of autoencoder networks to extract features from raw data and classify them using latent space representation into intrusive and non-intrusive classes. A benchmark real-time ROAD dataset is used to demonstrate the IDS-IVN's performance compared to the existing methods. IDS-IVN reports 99% accuracy with a 0.32% low false-positive rate for detecting intrusions. [ABSTRACT FROM AUTHOR]

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]

This paper proposes an intelligent system, called IDGADS, for detecting algorithmically generated domains in the early stages based on easy and automatic computable features of real domain name system (DNS) traffic quickly without investing time in reverse engineering and/or log monitoring or dependency on external information like WHOIS/DNS response. IDGADS is a supervised deep learning model, trained over 17M domains from the reputed sources. It is implemented in Python and served as a service over the cloud for free testing of the public in the form of a web app. IDGADS is capable of detecting malicious domains up to 99% accuracy. Till 17-April-2020, 1160963 domains have been tested, and it has detected 817069 DGA-generated domains by the users of different countries. Since IDGADS is developed to check DNS queries only, thus it can be installed as the first line of defence in security stack for validating DNS queries before sending to DNS server. [Display omitted] • An intelligent deep learning system for detecting algorithmically generated domains. • Extracted intrinsic features from domain names without any human intervention. • Proposed system as first line of defence system for analyzing real domain names as a web service. [ABSTRACT FROM AUTHOR]