New Intrusion Detection System Based on Neural Networks and Clustering

Efficiency of Intrusion detection systems-IDS are evaluated using parameters like completeness, performance and accuracy. The first important parameter is the completeness, which occurs when the detection of attack fails. This is the most difficult parameter to evaluate compared to the other two parameters. The second one is performance, which indicates the audit events process. When the IDS doesn’t work properly or works poorly, the real time detection becomes impossible. Legitimate actions are flagged as anomalous which is termed as inaccuracy. This part needs attention to address the inaccuracies. Optimal solutions must take the inaccuracies into consideration for accuracy, thereby efficiency of IDS. There are different trends in IDS. Some of them are discussed below. Behavior and knowledge-based IDS: Misuse detection, appearance-based detection, behavior detection and anomaly detection etc. There are numerous stability and security issues as a result of the Internet’s and computer networks’ rapid proliferation. The present study reports the case study of image processing in a fruit grading plant with data safety over cloud with Original Equipment Manufacturer (OEM). How Artificial Neural Networks (ANN) architecture can help is discussed and recommendations are made for impending improvement

An Ensemble Learning Approach For Task Failure Prediction In Cloud Data Centers

Due to cloud computing’s extensive use and diverse nature, they experience failures in terms of software, service, and platform, which lead to the failure of task execution, resource waste and performance deterioration. Most studies focused on failure prediction resulted in lower prediction accuracies due to limited attributes and a single prediction model. Hence, in this paper, an efficient ensemble model for task failure prediction is put forth. Initially, the input dataset is collected and pre-processed. In pre-processing, the dataset is cleaned up of all null values. Then, the dimensionality of the pre-processed dataset is reduced by using the PCA algorithm. Thus, the reconstructed dataset is split into training and testing sets to train failure prediction models. The proposed model employs an ensemble learning approach based on different ML and DL algorithms. Then, a comparative study is performed, and the results show that task failure in the cloud system can be effectively predicted using the proposed ensemble method

Gas Leakage Detection System Using IoT And cloud Technology: A Review

In industries and other locations gas leakage causes number of negative health effects .so an early detection of gas leakage and alertness will reduce the damage and save human life’s. Gas leakage techniques, trends and sensors are constantly evolving, and it is important for developers and researchers to stay up-to-date on the latest advancements. This paper conducts a systematic literature review on current state of gas leakage detection using Internet of Things (IOT) and Cloud technology. It explores the various sensor-based and non-sensor based IOT systems available for gas leakage detection, and their relative advantages and disadvantages. Additionally, this review summarizes current trends and challenges in the field of gas leakage detection, and discusses future research directions for improving the reliability and accuracy of these systems. This literature review highlights the need for more efficient, cost effective, and scalable IOT-based solutions for gas leakage detection

A Comprehensive Survey on Face Quality Detection in a Video Frame

The correctness of the generated face data, which is impacted by a number of variables, significantly affects how well face analysis and recognition systems perform. By automatically analysing the face data quality in terms of its biometric value, it might be able to identify low-quality data and take the necessary action. With a focus on visible wavelength face image input, this study summarises the body of research on the evaluation of face picture quality. The use of DL-based methods is unquestionably expanding, and there are major conceptual differences between them and current approaches, such as the inclusion of quality assessment in face recognition models. In addition to image selection, which is the topic of this article, face picture quality assessment can be used in a wide range of application scenarios. The requirement for comparative algorithm assessments and the difficulty of creating Deep Learning (DL) techniques that are intelligible in addition to providing accurate utility estimates are just a few of the issues and topics that remain unanswered. For each frame, the suggested method is compared to traditional facial feature extraction, and for a collection of video frames, it is compared to well-known clustering algorithms

Weapon Detection in Surveillance Videos Using YOLOV8 and PELSF-DCNN

Weapon detection (WD) provides early detection of potentially violent situations. Despite deep learning (DL) algorithms and sophisticated closed-circuit television (CCTVs), detecting weapons is still a difficult task. So, this paper proposes a WD model using PELSF-DCNN. Initially, the input video is converted into frames and pre-processed. The objects in the pre-processed frames are detected using the YOLOv8. In meantime, motion estimation is done using the DS algorithm in the pre-processed images to cover all the information. Then, the detected weapons undergo a sliding window process by considering the motion estimated frames. The silhouette score is calculated for detected humans and other objects. Now, the features are extracted and the important features are selected using the CSBO algorithm. The selected features and the output of YOLOv8 are given to the PELSF-DCNN classifier. Finally, the confidence score is calculated for the frame to define the number of weapons. In an experimental evaluation, the proposed method is found to be more efficient than the existing methods

IoT Based Gas Leakage detection System Using GPS

Gas leaks are a significant problem since they may have disastrous effects on infrastructure, human health, and greenhouse gas emissions, among other things. A method for early detection and alerting of gas leaks is required to reduce these dangers. In this project, we suggest a low-cost and efficient cloud-based Internet of Things (IoT) gas leak detection system for usage in residential, commercial, and industrial contexts. An Arduino Uno microcontroller, a Wi-Fi module, and a MQ 2 gas sensor make up the system. The sensor notifies the microcontroller when gas is detected, and the microcontroller analyses the information before sending it to the cloud through the IoT module. The cloud platform offers a user-friendly interface for managing and visualising data on gas leaks, and it also notifies customers through email and SMS. The system comes with a GPS module and a smoke detector for real-time position tracking and fire detection. The smoke detector detects smoke and sounds an alert, while the GPS module monitors the system’s location. These qualities enable the system to effectively reduce the dangers of gas leaks and fires while enhancing environmental safety

An enhanced consortium blockchain diversity mining technique for IoT metadata aggregation

Over the last two decades, Internet of Things (IoT) networks have grown exponentially. Although the devices have relatively low memory, resource, and processing capability, the trend is that nodes generate a large volume of data. That is where cloud technology comes into play to provide storage space. Because of its centralized nature and robustness, a large network operating with cloud assistance may be vulnerable. Due to rigid access control policies, the devices may be vulnerable to malicious activity. On the other hand, cloud technology provides a platform for such a security system to operate. A centralized secure architecture fails to consider mobile and edge devices within the context of these criteria. This raises numerous concerns about trusting third-party cloud intermediaries, which cause security and privacy leaks. The goal of this research is to look into the problem of blockchain consensus algorithms and their applicability in IoT with cloud-native infrastructure in the Ethereum and MultiChain variants. The significant challenge is scaling the core layer without sacrificing decentralization, security, or public verifiability. This type of testbed is used to investigate the impact of architectural design and consensus models in a lightweight IoT environment. Consensus in each IoT transaction remains the most important aspect of blockchain-enabled IoT networks. When the ledger is updated without privacy protection, transaction-oriented breaches can occur. Current practices for integrating finite IoT network resources into infrastructure-oriented blockchain implementations are flawed due to they are willing to sacrifice data security and integrity in order to save time and energy. This encourages researchers to investigate an improved lightweight block verification approach to the blockchain functional framework, that decreases processing needs, network latency, and network overhead substantially. As a result, the layer-3 consensus promotes blockchain to include the block with a 35% improvement in base layer block time efficiency and a 56% increase in throughput