Proficient Approach for Intrusion Detection using Behaviour Profiling Algorithm and Prevention Using Statistical Model in Cloud Networks

Objectives: The objective of the paper is to discuss the proposed dynamic software model to detect and prevent intrusion in the cloud network. Methods: The Behavior Profiling Algorithm (BPA) has been used to detect the intrusion in cloud network. For finding the intruder in the network the Event Log Entries and the network Unique Identification Address (UIA) has been fetched from the server and then the collected attribute values have been transferred to prevention module.  In the prevention module the dynamic statistical approach model has been used to prevent the network systems and data which are available in the Cloud Network. Findings: For testing the proposed model the 100 cloud network systems were taken and based on the loss of packets (in MB) ranges the samples were classified as 0-100, 101-200, 201-300, 301-400, 401-500, 501-600, 601-700 respectively. The range of data loss is assumed to be an interval of 100 Mbps. It is assumed that the higher the data loss ranges, the more data is lost. The mean, variance, and standard deviation were calculated to verify the data loss ranges. The mean (average) of the data loss in the ranges 0-100 is 060.77 and the mean in the ranges 101-200 is 144.714 data losses, which gradually increases in proportion to the data loss ranges, and in the ranges 601-700 it is 665.769 data losses. From the statistical approach model, the differences between mean and variance indicated that the intruder attacked the files during the data transformation in the network. Therefore, the administrator has to monitor the warning message from the proposed IPS model and get data packet losses in the transformation. If the frequency of data loss is low, the administrator can assume that the data flow is low due to network problems. On the other hand, if the frequency of data loss in the network system is high, he can block the transformation and protect the data file. This paper concludes that the behavioral profiling algorithm combined with a statistical model achieves an efficiency of over 96% in wired networks, over 97.6% in wireless networks, and over 98.7% in cloud networks. Novelty: In the previous paper discussed the approach which has been implemented with 40 nodes and the result of the proposed algorithm produced above 90%, 96% and 98% in the wired, wireless and cloud network respectively. Now, the model has been implemented with 100 nodes the result has been increased. This study concluded that, the efficient algorithm to detect the intrusion is behaviour profiling algorithm, while join with the statistical approach model, it produces efficient result

The Unbalanced Classification Problem: Detecting Breaches in Security

This research proposes several methods designed to improve solutions for security classification problems. The security classification problem involves unbalanced, high-dimensional, binary classification problems that are prevalent today. The imbalance within this data involves a significant majority of the negative class and a minority positive class. Any system that needs protection from malicious activity, intruders, theft, or other types of breaches in security must address this problem. These breaches in security are considered instances of the positive class. Given numerical data that represent observations or instances which require classification, state of the art machine learning algorithms can be applied. However, the unbalanced and high-dimensional structure of the data must be considered prior to applying these learning methods. High-dimensional data poses a “curse of dimensionality” which can be overcome through the analysis of subspaces. Exploration of intelligent subspace modeling and the fusion of subspace models is proposed. Detailed analysis of the one-class support vector machine, as well as its weaknesses and proposals to overcome these shortcomings are included. A fundamental method for evaluation of the binary classification model is the receiver operating characteristic (ROC) curve and the area under the curve (AUC). This work details the underlying statistics involved with ROC curves, contributing a comprehensive review of ROC curve construction and analysis techniques to include a novel graphic for illustrating the connection between ROC curves and classifier decision values. The major innovations of this work include synergistic classifier fusion through the analysis of ROC curves and rankings, insight into the statistical behavior of the Gaussian kernel, and novel methods for applying machine learning techniques to defend against computer intrusion detection. The primary empirical vehicle for this research is computer intrusion detection data, and both host-based intrusion detection systems (HIDS) and network-based intrusion detection systems (NIDS) are addressed. Empirical studies also include military tactical scenarios

AI models for recommendation

Ponencia presentada en EMAI2021, West Bengal, India, 4/4/2021[EN]Today, the industries of all European countries face common challenges: improving resource efficiency, becoming more environmentally friendly, mitigating climate change, improving the digitization in all segments of the value chain and improving transparency and safety, providing consumers with detailed information and ensuring the safety and quality of the final product. Growing concerns about environmental and social issues are pushing the demands of stakeholders (customers, workers, shareholders, consumers, etc.) and the public towards more sustainable processes and products. Sustainability is closely linked to climate change: the introduction of sustainable measures, both by consumers and producers, is inherently a measure against climate change

Data Mining

The availability of big data due to computerization and automation has generated an urgent need for new techniques to analyze and convert big data into useful information and knowledge. Data mining is a promising and leading-edge technology for mining large volumes of data, looking for hidden information, and aiding knowledge discovery. It can be used for characterization, classification, discrimination, anomaly detection, association, clustering, trend or evolution prediction, and much more in fields such as science, medicine, economics, engineering, computers, and even business analytics. This book presents basic concepts, ideas, and research in data mining

Sustainable Agriculture and Advances of Remote Sensing (Volume 2)

Agriculture, as the main source of alimentation and the most important economic activity globally, is being affected by the impacts of climate change. To maintain and increase our global food system production, to reduce biodiversity loss and preserve our natural ecosystem, new practices and technologies are required. This book focuses on the latest advances in remote sensing technology and agricultural engineering leading to the sustainable agriculture practices. Earth observation data, in situ and proxy-remote sensing data are the main source of information for monitoring and analyzing agriculture activities. Particular attention is given to earth observation satellites and the Internet of Things for data collection, to multispectral and hyperspectral data analysis using machine learning and deep learning, to WebGIS and the Internet of Things for sharing and publication of the results, among others