Seagull Optimization-based Feature Selection with Optimal Extreme Learning Machine for Intrusion Detection in Fog Assisted WSN

On the internet, various devices that are connected to the Internet of Things (IoT) and Wireless Sensor Networks (WSNs) share the resources that they have in accordance with their respective needs. The information gathered from these Internet of Things devices was preserved in the cloud. The problem of latency is made significantly worse by the proliferation of Internet of Things devices and the accessing of real-time data. In order to solve this issue, the fog layer, which was previously an adjunct layer between the cloud layer and the user, is now being utilised. As the data could be retrieved from the fog layer even if it was close to the edge of the network, it made the experience more convenient for the user. The lack of security in the fog layer is going to be an issue. The simple access to sources provided by the fog layer architecture makes it vulnerable to a great number of assaults. Consequently, the purpose of this work is to build a seagull optimization-based feature selection approach with optimum extreme learning machine (SGOFS-OELM) for the purpose of intrusion detection in a fog-enabled WSN. The identification of intrusions in the fog-enabled WSN is the primary focus of the SGOFS-OELM approach that has been presented here. The given SGOFS-OELM strategy is designed to accomplish this goal by designing the SGOFS approach to choose the best possible subset of attributes. In this work, the ELM classification model is applied for the purpose of intrusion detection. In conclusion, the political optimizer (PO) is utilised in order to accomplish automatic parameter adjustment of the ELM technique, which ultimately leads to enhanced classification performance. In order to demonstrate the usefulness of the SGOFS-OELM approach, a number of simulations were carried out. As compared to the other benchmark models that were employed for this research, the suggested SGOFS-OELM models give the best accuracy, which is 99.97 percent. The simulation research demonstrates that the SGOFS-OELM approach has the potential to deliver a good performance in the intrusion detection process

Barnacles Mating Optimizer with Hopfield Neural Network Based Intrusion Detection in Internet of Things Environment

Owing to the development and expansion of energy-aware sensing devices and autonomous and intelligent systems, the Internet of Things (IoT) has gained remarkable growth and found uses in several day-to-day applications. Currently, the Internet of Things (IoT) network is gradually developing ubiquitous connectivity amongst distinct new applications namely smart homes, smart grids, smart cities, and several others. The developing network of smart devices and objects allows people to make smart decisions with machine to machine (M2M) communications. One of the real-world security and IoT-related challenges was vulnerable to distinct attacks which poses several security and privacy challenges. Thus, an IoT provides effective and efficient solutions. An Intrusion Detection System (IDS) is a solution for addressing security and privacy challenges with identifying distinct IoT attacks. This study develops a new Barnacles Mating Optimizer with Hopfield Neural Network based Intrusion Detection (BMOHNN-ID) in IoT environment. The presented BMOHNN-ID technique majorly concentrates on the detection and classification of intrusions from IoT environments. In order to attain this, the BMOHNN-ID technique primarily pre-processes the input data for transforming it into a compatible format. Next, the HNN model was employed for the effectual recognition and classification of intrusions from IoT environments. Moreover, the BMO technique was exploited to optimally modify the parameters related to the HNN model. When a list of possible susceptibilities of every device is ordered, every device is profiled utilizing data related to every device. It comprises routing data, the reported hostname, network flow, and topology. This data was offered to the external modules for digesting the data via REST API model. The experimental values assured that the BMOHNN-ID model has gained effectual intrusion classification performance over the other models

Motor Imagery EEG Recognition using Deep Generative Adversarial Network with EMD for BCI Applications

The activities for motor imagery (MI) movements in Electroencephalography (EEG) are still interesting and challenging. BCI (Brain Computer Interface) allows the brain signals to control the external devices and also helps a disabled person suffering from neuromuscular disorders. In any BCI system, the two most essential steps are feature extraction and classification method. However, in this paper, the MI classification is improved by the performance of Deep Learning (DL) concept. In this proposed system two-moment imagination of right hand and right foot from the BCI competition three datasets IVA has been taken and classification methods utilizing Conventional neural network (CNN) and Generative Adversarial Network (GAN) are developed. The training time is reduced and non-stationary problem is managed by applying Empirical mode decomposition (EMD) and mixing their intrinsic mode functions (IMFs) in feature extraction technique. The experimental result indicates the proposed GAN classification technique achieves better classification accuracy in terms of 95.29% than the CNN of 89.38%. The proposed GAN method achieves an average positive rate of 62% and average false positive rate of 3.4% on BCI competition three datasets IVA whose EEG facts were resulting from the similar C3, C4, and Cz channels of the motor cortex

Fuzzy Logic Based DSR Trust Estimation Routing Protocol for MANET Using Evolutionary Algorithms

In MANET attaining consistent routing is a main problem due to several reasons such as lack of static infrastructure, exposed transmission medium, energetic network topology and restricted battery power. These features also create the scheme of direction-finding protocols in MANETs become even more interesting. In this work, a Trust centered routing protocol is suggested, since trust plays a vital role in computing path in mobile ad hoc networks (MANETs). Estimating and computing trust encourages cooperation in mobile ad hoc networks (MANETs). Various present grade systems suddenly estimate the trust by considering any one of the parameters such as energy of node, number of hops and mobility. Estimating trust is an Energetic multi objective optimization problem (EMOPs) typically including many contradictory goals such as lifetime of node, lifetime of link and buffer occupancy proportion which change over time. To solve this multi objective problem, a hybrid Harmony Search Combined with Genetic algorithm and Cuckoo search is used along with reactive method Dynamic Source routing protocol to provide the mobile hosts to find out and sustain routes between the origin node (SN) to the target node (TN). In this work, the performance of the direction-finding practice is assessed using throughput, end to end delay, and load on the network and route detection period

Multi-objective Sand Piper Optimization Based Clustering with Multihop Routing Technique for IoT Assisted WSN

Abstract Internet of Things (IoT) can be considered as one of the emergent research topics, which linked several sensor enabled devices. Wireless sensor networks (WSNs) remains a key enabling technology for IoT environment due to their possibility in placing different types of essential smart city applications, like healthcare, smart cities, environment monitoring, etc. At the same time, effectual utilization of energy is required for the design of energy-efficient data transmission strategy in the IoT environment. In this view, this study develops a novel multi-objective sand piper optimization based clustering with multi-hop routing (MOSPO-CMR) technique to IoT assisted WSN. The proposed MOSPO-CMR technique intends to effectively choose cluster heads (CHs) and derive optimal routes to BS. The MOSPO-CMR technique initially performs cluster construction process by the election of CHs using three variables namely Residual energy (RES), distance to BS (DIST), and Node Degree (NDEG). Besides, the MOSPO-CMR technique derives an objective function involving two variables such as RES and DIST to determine optimal routes to destination. In order to demonstrate the enhanced outcomes of the MOSPO-CMR approach, a series of simulations were carried out and the outcomes highlighted the enhanced outcomes of the MOSPO-CMR technique over the other recent approaches

Performance Improvement of SIMD Processor for High-Speed end Devices in IoT Operation Based on Reversible Logic with Hybrid Adder Configuration

The reversible logic function is gaining significant consideration as a style for the logic design by implementing modern Nano and quantum computing with minimal impact on physical entropy. Recent advances in reversible logic allow for computer design applications using advanced quantum computer algorithms. In the literature, significant contributions have been made towards reversible logic gate structures and arithmetic units. However, there are many attempts to dictate the design of Single Instruction-Multiple Data (SIMD) processors. In this research work, a novel programmable reversible logic gate design is verified and a reversible processor design suggests its implementation of SIMD processor. Then, implementing the ripple-carry, carry-select and Kogge-Stone carry look-ahead adders using reversible logic and the performance is compared. The proposed reversible logic-based architecture has a minimum fan out with binary tree structure and minimum logic depth. The simulation result of the proposed design is obtained from Xilinx 14.5 software. From the simulated result, the computational path net delay for 16 × 16 reversible logic with Kogge Stone Adder is 17.247 ns. Compared with 16-bit Kogge Stone Adder, the reversible logic-based 16-bit Kogge Stone Adder gives low power and low time delay. By looking at the speed, energy and area parameters, including fast applications in which two smaller delay and low power adders are required, the effectiveness, including the proper area use of the hybrid adder recommended by it is evaluated

Seagull Optimization-based Feature Selection with Optimal Extreme Learning Machine for Intrusion Detection in Fog Assisted WSN

On the internet, various devices that are connected to the Internet of Things (IoT) and Wireless Sensor Networks (WSNs) share the resources that they have in accordance with their respective needs. The information gathered from these Internet of Things devices was preserved in the cloud. The problem of latency is made significantly worse by the proliferation of Internet of Things devices and the accessing of real-time data. In order to solve this issue, the fog layer, which was previously an adjunct layer between the cloud layer and the user, is now being utilised. As the data could be retrieved from the fog layer even if it was close to the edge of the network, it made the experience more convenient for the user. The lack of security in the fog layer is going to be an issue. The simple access to sources provided by the fog layer architecture makes it vulnerable to a great number of assaults. Consequently, the purpose of this work is to build a seagull optimization-based feature selection approach with optimum extreme learning machine (SGOFS-OELM) for the purpose of intrusion detection in a fog-enabled WSN. The identification of intrusions in the fog-enabled WSN is the primary focus of the SGOFS-OELM approach that has been presented here. The given SGOFS-OELM strategy is designed to accomplish this goal by designing the SGOFS approach to choose the best possible subset of attributes. In this work, the ELM classification model is applied for the purpose of intrusion detection. In conclusion, the political optimizer (PO) is utilised in order to accomplish automatic parameter adjustment of the ELM technique, which ultimately leads to enhanced classification performance. In order to demonstrate the usefulness of the SGOFS-OELM approach, a number of simulations were carried out. As compared to the other benchmark models that were employed for this research, the suggested SGOFS-OELM models give the best accuracy, which is 99.97 percent. The simulation research demonstrates that the SGOFS-OELM approach has the potential to deliver a good performance in the intrusion detection process

Efficient Privacy-Preserving Red Deer Optimization Algorithm with Blockchain Technology for Clustered VANET

Vehicular Adhoc Network (VANET) is a version of Mobile Adhoc Network (MANET). Owing to an increase in road accidents, VANET offers safety to road vehicles through appropriate coordination with vehicles and road side units. Along with the security guidelines of the vehicles in the network, privacy and security become vital parameters that need to be accomplished for secure data transmission in VANET. This study develops an efficient privacy-preserving data transmission architecture using red deer optimization algorithm based clustering with blockchain technology (RDOAC-BT) in cluster-based VANET. The proposed RDOAC-BT technique involves the design of RDOA based clustering technique to elect cluster heads (CHs) and construct clusters. In addition, blockchain technology is employed for secured transmission in VANET. Moreover, the blockchain is utilized to perform intra-cluster and inter-cluster communication processes. A wide range of simulations take place and the results are examined under varying aspects. The resultant outcome portrayed the betterment of the RDOAC-BT technique over the recent techniques