Ambient intelligence approach: Internet of Things based decision performance analysis for intrusion detection

In recent infrastructures, Internet of Things (IoT) have become an important technology for connecting various actuators and sensors over wireless networks. Due to increase in mission-critical infrastructures, we make use of these new technologies for reliable communication but their security is always not promising in terms of availability, confidentiality, integrity, and privacy of network services. Users can be compromised and vulnerable by a motivated malicious opponent unless they are not adequately protected by a robust defense. Due to this reason, an ambient intelligence approach for Intrusion Detection System (IDS) is required. In this research, ww proposed Ambient Approach based on Reinforcement Learning Integrated Deep Q-Neural Network (RL-DQN) model for WSNs and IoT in which it leverages the Markov decision process (MDP) formalism to enhance the decision performance in IDS. We deploy RL-DQN-IDS over Edge-cloud intrusion detection infrastructure in which binary attack classification of the network traffic is performed at the edge network while multi-attack classification is performed at the cloud network. To identify intrusions, we use a two-phase process that includes an initial learning phase that relies on RL, followed by a detection and classification phase that relies on DQN. We used four datasets namely UNSW-NB-15, BoTNeTIoT-L01, CICIDS2017 and IoTID20 with a smart house simulation environment configured with WSN and IoT technologies to evaluate performance. Accuracy, precision, and recall were all considered while assessing the dataset under consideration. When compared to five other machine learning models, the RL-DQN model method has demonstrated superior performance. This model outperforms the other five that were tested

Directional Statistics and Machine Learning for crater detection in Space

Craters are distinctive features on the surfaces of most terrestrial planets such as Mars and Venus. The distribution of craters reveals the relative ages of surface units and provides information on surface geology. Extracting craters is one of the fundamental tasks in planetary research. Although many automated crater detection algorithms have been developed to extract craters from image or topographic data, most of them are applicable only in particular regions, and only a few can be widely used, especially in complex surface settings. On the other side, once we have a reasonable craters data, statistics play an important role in better understanding their features, in particular their distribution. In this workshop, we will demonstrate to participants how basic methodologies with directional statistics and machine learning/deep learning models help in the detection and analysis of craters in our Universe

Consumer Product Recommendation System using Adapted PSO with Federated Learning Method

peer reviewedIn this paper, we proposed adapted particle swarm optimization integrated federated learning-based sentiment analysis integrated deep learning (aPSO-FLSADL) model for personalized recommendations of consumer electronics that leverage SentiWordNet and BERT for word embedding, CNN-BiLSTM based Federated learning model to train a global sentiment analysis model and mutation operator based modified particle swarm optimization for learning parameter optimization in federated learning environment. SentiWordNet is a sentiment lexicon that provides sentiment scores for words, while BERT is a powerful pre-trained deep learning model for natural language processing. Our approach involves pre-processing the text data, calculating sentiment scores using SentiWordNet, converting text data into word embedding using BERT, and assigning weights to words based on a defined weighting scheme. We evaluate the performance of our approach on a separate evaluation dataset including Amazon review dataset and CNET dataset. Based on the various evaluation metrics including accuracy, loss, hit ratio, we demonstrated the effectiveness of proposed aPSO-FLSADL in generating accurate and personalized recommendations. The depicted result shows that proposed aPSO-FLSADL achieved highest training and testing accuracy for both datasets and outperform over baseline models with maximum hit ratio for consumer electronics product recommendation

UIoTN-PMSE: Ubiquitous IoT network-based predictive modeling in smart environment

peer reviewedWe proposed a three-stage intrusion detection system that utilizes a predictive machine learning model to identify and mitigate attacks on ubiquitous network. In the first stage, we applied Apriori-enabled Association Rule Mining (AARM) feature selection with support vector machine (SVM) for classification of flow of network. Second, we proposed ensemble learning-based AARM model (PAEL) for behavior analysis. Finally, for classification of multi-task labels, we proposed swarm bat optimization-based PAEL model. The trained model is applied to edge and fog computing devices to obtain lower resource utilization and improve the efficiency of the system. The intrusion detection process is performed in three stages: (i) at the edge devices, where abnormal data from network traffic from IoT devices were identified, (ii) the abnormal data sample is sent to fog computing deivce to confirm the attacks and abnormalities, (iii) final identified data sample is sent to cloud server. At cloud, proposed predictive machine learning (ML)-based generalized weight sum-enabled ensemble learning (PML-GWEL) model is trained on sample data, including new detected samples, to continually improve its accuracy. Once the model is trained, it is published to all nodes in the network to update their primary detector models and clear out any outdated pre-detector models. This process helps to reduce the hardware resources used by the pre-detector models and improve the overall efficiency of the system. The proposed model is compared with other existing techniques

Intelligent AI-based Healthcare Cyber Security System using Multi-Source Transfer Learning Method

peer reviewedCyber-security intelligence have made a great impact over healthcare industry where several researchers are developing new techniques to improve security for healthcare systems. Besides, Artificial Intelligence (AI) become the tremendous technology in recent decades to improve the existing methods to be more intelligent. In this paper, we proposed cyber attack detection system for healthcare sector with centralized and federated transfer learning mode. Edge of Things (EoT) framework is developed in connection with cloud and healthcare sectors to transmit the data efficiently and the proposed Centralized with Multi-Source Transfer Learning (CMTL) algorithm which is used for detection and classification of various threats such as information gathering, DoS/DDoS attacks, Malware attacks, Injection attacks, and Man in the Middle attacks. Performance of the proposed framework is evaluated using various datasets such as EMNIST, X-IIoTID, and Federated TON_IoT. Our framework outperforms with the analysis of execution time and obtains high level accuracy when compared with different algorithms

Intelligent Task Scheduling Approach for IoT Integrated Healthcare Cyber Physical Systems

peer reviewedCyber-physical systems (CPS) based on cloud computing provides resources over the Internet and allow a variety of applications to be deployed to provide services for various industries. We proposed IoT-based healthcare cyber-physical system that provides effective resource utilization at fog and cloud levels with minimum execution cost. In addition, we also consider data from social media networking and drug review for the analysis. Furthermore, two different feature extraction approaches were applied based on data collection. Homogeneity score-based K-means clustering is used as a feature extraction and selection method for sensor data features, while text mining and sentiment analysis approach is used for social media networking and drug review data feature extraction. We proposed efficient resource utilization and cost-effective task scheduling at the Fog level and multi-objective heuristic approach Ant colony optimization task scheduling (MOHACO-TS) at cloud level. Both task scheduling algorithms focus on executing maximum task tasks in minimum time with effective resource utilization. We consider five different datasets and existing task scheduling and classification approaches for performance evaluation of the proposed IoT-HCPS framework. From the results, it is evident that the proposed work IoT-HCPS outperformed the exisitng techniques and algorithms

Enhancing network lifespan in wireless sensor networks using deep learning based Graph Neural Network

peer reviewedInadequate energy of sensors is one of the most significant challenges in the development of a reliable wireless sensor network (WSN) that can withstand the demands of growing WSN applications. Implementing a sleep-wake scheduling scheme while assigning data collection and sensing chores to a dominant group of awake sensors while all other nodes are in a sleep state seems to be a potential way for preserving the energy of these sensor nodes. When the starting energy of the nodes changes from one node to another, this issue becomes more difficult to solve. The notion of a dominant set-in graph has been used in a variety of situations. The search for the smallest dominant set in a big graph might be time-consuming. Specifically, we address two issues: first, identifying the smallest possible dominant set, and second, extending the network lifespan by saving the energy of the sensors. To overcome the first problem, we design and develop a deep learning-based Graph Neural Network (DL-GNN). The GNN training method and back-propagation approach were used to train a GNN consisting of three networks such as transition network, bias network, and output network, to determine the minimal dominant set in the created graph. As a second step, we proposed a hybrid fixed-variant search (HFVS) method that considers minimal dominant sets as input and improves overall network lifespan by swapping nodes of minimal dominating sets. We prepared simulated networks with various network configurations and modeled different WSNs as undirected graphs. To get better convergence, the different values of state vector dimensions of the input vectors are investigated. When the state vector dimension is 3 or 4, minimum dominant set is recognized with high accuracy. The paper also presents comparative analyses between the proposed HFVS algorithm and other existing algorithms for extending network lifespan and discusses the trade-offs that exist between them. Lifespan of wireless sensor network, which is based on the dominant set method, is greatly increased by the techniques we have proposed

DDNSAS: Deep reinforcement learning based deep Q-learning network for smart agriculture system

peer reviewedAs the global population continues to grow and environmental conditions become increasingly unpredictable, meeting the demands for food becomes increasingly difficult. To overcome these challenges, smart agriculture has emerged as the key technology. Deep Learning model with Internet of Things (IoT), unmanned aerial vehicle (UAV), and edge–fog–cloud architecture enabled smart agriculture as a key component for next agriculture revolution. In this work, we present two stage end-to-end DRL based smart agricultural system. In stage one, we proposed ACO enabled DQN (MACO-DQN) model to offload task including fire detection, pest detection, crop growth monitoring, irrigation scheduling, soil monitoring, climate monitoring, field monitoring etc. MACO-DQN model offload the task to either edge, fog or cloud networking devices based on latency, energy consumption and computing power. Once the task offloaded to computing devices (edge, fog or cloud), task of prediction and monitoring various agriculture activities is performed at stage two. In stage two, we proposed DRL based DQN (RL-DQN) model for prediction and monitoring agricultural task activities. Finally, we demonstrate experimental findings of our proposed model that represent a marked enhancement in terms of convergence speed, planning success rate, and path accuracy. To evaluate its performance, the method presented in this paper was compared to traditional deep Q-networks-based intensive learning method under consistent experimental conditions. Overall, 98.5% precision, 99.1% recall, 98.1% F-measure, and 98.5% accuracy is obtained when using our proposed methodology and based on the performance results the model outperforms other existing methodologies

Autologous Bone Marrow Stem Cell Infusion (AMBI) therapy for Chronic Liver Diseases

Liver Cirrhosis is the end stage of chronic liver disease which may happen due to alcoholism, viral infections due to Hepatitis B, Hepatitis C viruses and is difficult to treat. Liver transplantation is the only available definitive treatment which is marred by lack of donors, post operative complications such as rejection and high cost. Autologous bone marrow stem cells have shown a lot of promise in earlier reported animal studies and clinical trials. We have in this study administered in 22 patients with chronic liver disease, autologous bone marrow stem cell whose results are presented herewith

IADF-CPS: Intelligent Anomaly Detection Framework towards Cyber Physical Systems

Cyber–Physical Systems (CPSs) becoming one of the most complex, intelligent, and sophisticated system. Ensuring security is an important aspect towards CPSs. However, increase in sophisticated and complexity attacks in CPSs, the conventional anomaly detection methods are facing problems and also growth in volume of data becomes challenging which requires domain specific knowledge that could be applied directly to analyze these challenges. In order to overcome this problem, various deep learning based anomaly detection system is developed. In this research, we propose an anomaly detection approach by integration of intelligent deep learning technique named Convolutional Neural Network (CNN) with Kalman Filter (KF) based Gaussian-Mixture Model (GMM). The proposed model is used for identifying and detecting anomalous behavior in CPSs. This proposed framework consists of two important process. First is to pre-process the data by transforming and filtering original data into new format and achieved privacy preservation of the data. Secondly, we proposed GMM-KF integrated deep CNN model for anomaly detection and accurately estimated the posterior probabilities of anomalous and legitimate events in CPSs