A Novel IDS Technique to detect DDoS and Sniffers in Smart Grid

Abstract: Smart grid doesn't have a single standard definition to define it. Commonly, Smart Grid is an incorporation of advanced technologies over the normal electrical grid. Smart grid provides some novel features that mainly includes two way communication and automatic self-healing capability. Like the Internet, the Smart Grid consists of many new technologies and equipment that are bind together. These technologies works with the electrical grid to respond digitally accordingly to our quickly changing electric demand. Even though it is stuffed with pros, it suffers a lot due to its fragile data security. Smart grid usually have a centralized control system called SCADA to monitor and maintain all the data sources. Attackers would always tend to sneak through this centralized system through numerous types of attacks. Since SCADA system has no definite protocol, it can be fixed into any kind of protocol that is required by the utility. In this paper, the proposed method provides two techniques one to detect and remove sniffers from the network. Another one is to safeguard the SCADA system from the DDoS attack. Promiscuous mode detection and MD-5 algorithm is used to find the sniffers and by analysing the TTL values, DDoS attack is been identified and isolated. The proposed technique is also compared with a real time his electronic document is a "live" template. The various components of your paper [title, text, heads, etc.] are already defined on the style sheet, as illustrated by the portions given in this document. Do not use special characters, symbols, or math in your title or abstract. The authors must follow the instructions given in the document for the papers to be published. You can use this document as both an instruction set and as a template into which you can type your own text

Federated learning optimization: A computational blockchain process with offloading analysis to enhance security

The Internet of Things (IoT) technology in various applications used in data processing systems requires high security because more data must be saved in cloud monitoring systems. Even though numerous procedures are in place to increase the security and dependability of data in IoT applications, the majority of outside users can decode any transferred data at any time. Therefore, it is essential to include data blocks that, under any circumstance, other external users cannot understand. The major significance of proposed method is to incorporate an offloading technique for data processing that is carried out by using block chain technique where complete security is assured for each data. Since a problem methodology is designed with respect to clusters a load balancing technique is incorporated with data weights where parametric evaluations are made in real time to determine the consistency of each data that is monitored with IoT. The examined outcomes with five scenarios process that projected model on offloading analysis with block chain proves to be more secured thereby increasing the accuracy of data processing for each IoT applications to 89%

EC-YOLOX: A Deep Learning Algorithm for Floating Objects Detection in Ground Images of Complex Water Environments

Correct detection of floating objects in complex water environments is a challenge because of the problems of obscuration and dense floating objects. In view of the above issues, this paper proposed a network called EC-YOLOX by introducing the CA (Coordinate Attention) and ECA (Efficient Channel Attention) mechanism and improving the loss function to further the multi-feature extraction and detection accuracy of floating objects. In this paper, ablation experiments and comparison experiments were conducted on the river floating objects dataset. The ablation experiments showed that the ECA and CA mechanism played a great role in EC-YOLOX, which can reduce the miss detection rate by 5.86% and increase the mAP by 5.53% compared with YOLOX. The EC-YOLOX was also applicable to different types of floating objects; the mAP of the ball, plastic-garbage, plastic-bag, leaf, milk-box, grass, and branches were respectively improved by 4%, 4%, 4%, 6%, 4%, 18%, and 5%. The mAP of the comparison experiments was improved by 15.13%, 9.30%, and 8.03% compared to Faster R-CNN, YOLOv5, and YOLOv3, respectively. This method facilitates the precise extraction of floating objects from images, which holds paramount importance for monitoring and safeguarding water environments. It offers significant contributions to water environment monitoring and protection

Digital transformations in medical applications using audio and virtual reality procedures

Numerous members of society struggle with health care issues, and despite the use of sensing technology, diseases in the body are still unable to be detected. The main cause of this identification process failure is the absence of any recognized virtual technology on the market. The majority of health care solicitations seek to create a specific application that simply delivers data on sensing values and ignores the virtual representation of those values. So, in order to detect the existence of viruses inside the body, this article offers an integration platform that links sensing devices with Virtual/Audio Reality (VR/AR) approaches. Additionally, a specific form of swarm intelligence algorithm known as Fruit Fly (FF) is used in the recognition process with a modified fitness function. The FF technique offers a lot of low layer awareness, which improves the output for efficient operation. The proposed AR/VR technique is used with biological sensors to analyze the real-time situations, and five different case studies are divided. It is logical to conclude from the experimental results that all validated case studies offer excellent productivity and are adaptable to all environmental circumstances

Digital transformations in medical applications using audio and virtual reality procedures

Numerous members of society struggle with health care issues, and despite the use of sensing technology, diseases in the body are still unable to be detected. The main cause of this identification process failure is the absence of any recognized virtual technology on the market. The majority of health care solicitations seek to create a specific application that simply delivers data on sensing values and ignores the virtual representation of those values. So, in order to detect the existence of viruses inside the body, this article offers an integration platform that links sensing devices with Virtual/Audio Reality (VR/AR) approaches. Additionally, a specific form of swarm intelligence algorithm known as Fruit Fly (FF) is used in the recognition process with a modified fitness function. The FF technique offers a lot of low layer awareness, which improves the output for efficient operation. The proposed AR/VR technique is used with biological sensors to analyze the real-time situations, and five different case studies are divided. It is logical to conclude from the experimental results that all validated case studies offer excellent productivity and are adaptable to all environmental circumstances

Attention-based bidirectional-long short-term memory for abnormal human activity detection

Abstract Abnormal human behavior must be monitored and controlled in today’s technology-driven era, since it may cause damage to society in the form of assault or web-based violence, such as direct harm to a person or the propagation of hate crimes through the internet. Several authors have attempted to address this issue, but no one has yet come up with a solution that is both practical and workable. Recently, deep learning models have become popular as a means of handling massive amounts of data but their potential to categorize the aberrant human activity remains unexplored. Using a convolutional neural network (CNN), a bidirectional long short-term memory (Bi-LSTM), and an attention mechanism to pay attention to the unique spatiotemporal characteristics of raw video streams, a deep-learning approach has been implemented in the proposed framework to detect anomalous human activity. After analyzing the video, our suggested architecture can reliably assign an abnormal human behavior to its designated category. Analytic findings comparing the suggested architecture to state-of-the-art algorithms reveal an accuracy of 98.9%, 96.04%, and 61.04% using the UCF11, UCF50, and subUCF crime datasets, respectively

QoS enhancement in wireless ad hoc networks using resource commutable clustering and scheduling

Effective management and control of large-scale networks can be challenging in the absence of appropriate resource allocation. This paper presents a framework for highlighting the significance of resource allocation in mobile, wireless, and ad hoc networks. The model has been designed to incorporate a clustering protocol and a schedule-based resource allocation algorithm, resulting in the establishment of a multi-objective framework. The proposed framework places a significant emphasis on the allocation of energy and distance, with a focus on minimizing these objectives. Each node is separated into several clusters where individual energy is allocated and the cluster head in each cluster allows the nodes to communication with shortest distance. For the transmitted information the speed of transmission is maximized thus more amount of time period is saved where stability factor is maximized. To test the allocated resources in the network the proposed method compares and evaluates the parametric outcomes with existing method based on five scenarios. In the comparative analysis it is observed that proposed method can able to maximize the life time and quality of service for all networks with optimized range of 84%

MSI-A: An Energy Efficient Approximated Cache Coherence Protocol

Energy consumption has become an essential factor in designing modern computer system architecture. Because of physical limits, the termination of Moore’s law and Dennard’s scaling has forced the computer design community to investigate new approaches to meet the requirements for computing resources. Approximate computing has emerged as a promising method for reducing energy consumption while trading a controllable quality loss. This paper asserts that an approximated cache coherence protocol preserves overall energy for computation. We can approximate the cache coherence protocol by adding approximated cache lines to a certain level without hindering the output. This paper introduces an enhanced approximated version of the MSI (Modified Shared Invalid) cache coherence protocol MSI-A (Modified Shared Invalid-Approx). We have verified MSI-A and MSI by employing LTL specifications in the NuSMV model checker. To illustrate the benefits of MSI-A, we have added DTMC (Discrete-Time Markov Chain) with PCTL (Probabilistic Computational Tree Logic). Although the PCTL proves the theory of approximation, we have also simulated the MSI-A in the TEJAS hardware simulator on PARSEC 3.0 to investigate the energy gains and cycle gains of MSI-A in varied applications. The cache lines considered to be approx are between 10 and 30 percent. Each application benefited from approximation according to its nature, and VIPS has indicated a total energy gain of 30.18 percent

Pneumonia detection with QCSA network on chest X-ray

Abstract Worldwide, pneumonia is the leading cause of infant mortality. Experienced radiologists use chest X-rays to diagnose pneumonia and other respiratory diseases. The diagnostic procedure's complexity causes radiologists to disagree with the decision. Early diagnosis is the only feasible strategy for mitigating the disease's impact on the patent. Computer-aided diagnostics improve the accuracy of diagnosis. Recent studies established that Quaternion neural networks classify and predict better than real-valued neural networks, especially when dealing with multi-dimensional or multi-channel input. The attention mechanism has been derived from the human brain's visual and cognitive ability in which it focuses on some portion of the image and ignores the rest portion of the image. The attention mechanism maximizes the usage of the image's relevant aspects, hence boosting classification accuracy. In the current work, we propose a QCSA network (Quaternion Channel-Spatial Attention Network) by combining the spatial and channel attention mechanism with Quaternion residual network to classify chest X-Ray images for Pneumonia detection. We used a Kaggle X-ray dataset. The suggested architecture achieved 94.53% accuracy and 0.89 AUC. We have also shown that performance improves by integrating the attention mechanism in QCNN. Our results indicate that our approach to detecting pneumonia is promising