OpenStack Environment Research Articles

Intelligent Systems for Cloud SAAS Forensic

The rapid growth of Software-as-a-Service (SaaS) applications in cloud environments has introduced new challenges for digital forensics investigators. Traditional forensic methods are often insufficient to collect, preserve, and analyze digital evidence from cloud-based environments. To address these challenges, this scope will propose the development of an Intelligent System for Cloud SaaS Forensic (ISC-SF) for OpenStack environment. The ISC-SF will integrate machine learning algorithms with the SaaS model and will present the algorithms to design for attribute selection, machine learning model training, attack detection, evidence extraction, collection, and analysis. This paper provides an overview of OpenStack architecture, OpenStack services, and log locations. The ISC-SF aims to revolutionize the field of cloud SaaS forensic investigation by leveraging intelligent algorithms in a controlled cloud environment.

Secure Cyber Defense: An Analysis of Network Intrusion-Based Dataset CCD-IDSv1 with Machine Learning and Deep Learning Models

Anomaly detection and multi-attack classification are major concerns for cyber defense. Several publicly available datasets have been used extensively for the evaluation of Intrusion Detection Systems (IDSs). However, most of the publicly available datasets may not contain attack scenarios based on evolving threats. The development of a robust network intrusion dataset is vital for network threat analysis and mitigation. Proactive IDSs are required to tackle ever-growing threats in cyberspace. Machine learning (ML) and deep learning (DL) models have been deployed recently to detect the various types of cyber-attacks. However, current IDSs struggle to attain both a high detection rate and a low false alarm rate. To address these issues, we first develop a Center for Cyber Defense (CCD)-IDSv1 labeled flow-based dataset in an OpenStack environment. Five different attacks with normal usage imitating real-life usage are implemented. The number of network features is increased to overcome the shortcomings of the previous network flow-based datasets such as CIDDS and CIC-IDS2017. Secondly, this paper presents a comparative analysis on the effectiveness of different ML and DL models on our CCD-IDSv1 dataset. In this study, we consider both cyber anomaly detection and multi-attack classification. To improve the performance, we developed two DL-based ensemble models: Ensemble-CNN-10 and Ensemble-CNN-LSTM. Ensemble-CNN-10 combines 10 CNN models developed from 10-fold cross-validation, whereas Ensemble-CNN-LSTM combines base CNN and LSTM models. This paper also presents feature importance for both anomaly detection and multi-attack classification. Overall, the proposed ensemble models performed well in both the 10-fold cross-validation and independent testing on our dataset. Together, these results suggest the robustness and effectiveness of the proposed IDSs based on ML and DL models on the CCD-IDSv1 intrusion detection dataset.

Anomaly detection in cloud environment using artificial intelligence techniques

Now days the usage of cloud environment is rapidly increasing in all the fields to run applications in virtual machines instead of physical hardware based machine. This increases the service availability and also reduces the cost. The usage of openstack cloud environment is also increasing both in academics and industry as it provides open source cloud services to run the application both for research and for production environment. One of the challenges in cloud environment is that the detection and prediction of the anomalies before they occur. In the traditional approach, the anomalies are detected manually by keeping track of threshold level and heartbeat. The recent research is happening on using machine learning techniques in detecting the anomalies before they occur. In this paper, we propose a model for anomaly detection in openstack cloud environment. In the proposed model, we used Stacked and Bidirectional LSTM models to build the neural network. For the experiment the data is collected from openstack using collectd. The collected data sets 10 features and class label. Using LSTM neural network, we were able to detect the anomalies in openstack environment. The proposed model achieved the detection accuracy of 94.61% for training set and 93.98% for the test set using binary cross entropy function as a loss function.

Availability modeling in redundant OpenStack private clouds

AbstractIn cloud computing services, high availability is one of the quality of service requirements which is necessary to maintain customer confidence. High availability systems can be built by applying redundant nodes and multiple clusters in order to cope with software and hardware failures. Due to cloud computing complexity, dependability analysis of the cloud may require combining state‐based and nonstate‐based modeling techniques. This article proposes a hierarchical model combining reliability block diagrams and continuous time Markov chains to evaluate the availability of OpenStack private clouds, by considering different scenarios. The steady‐state availability, downtime, and cost are used as measures to compare different scenarios studied in the article. The heterogeneous workloads are considered in the proposed models by varying the number of CPUs requested by each customer. Both hardware and software failure rates of OpenStack components used in the model are collected via setting up a real OpenStack environment applying redundancy techniques. Results obtained from the proposed models emphasize the positive impact of redundancy on availability and downtime. Considering the tradeoff between availability and cost, system providers can choose an appropriate scenario for a specific purpose.

CCoDaMiC: A framework for Coherent Coordination of Data Migration and Computation platforms

The amount of data generated by millions of connected IoT sensors and devices is growing exponentially. The need to extract relevant information from this data in modern and future generation computing system, necessitates efficient data handling and processing platforms that can migrate such big data from one location to other locations seamlessly and securely, and can provide a way to preprocess and analyze that data before migrating to the final destination. Various data pipeline architectures have been proposed allowing the data administrator/user to handle the data migration operation efficiently. However, the modern data pipeline architectures do not offer built-in functionalities for ensuring data veracity, which includes data accuracy, trustworthiness and security. Furthermore, allowing the intermediate data to be processed, especially in the serverless computing environment, is becoming a cumbersome task. In order to fill this research gap, this paper introduces an efficient and novel data pipeline architecture, named as CCoDaMiC (Coherent Coordination of Data Migration and Computation), which brings both the data migration operation and its computation together into one place. This also ensures that the data delivered to the next destination/pipeline block is accurate and secure. The proposed framework is implemented in private OpenStack environment and Apache Nifi.

Information technology for the deployment of the OpenStack cloud environment

This article discusses the information technology of implementing and adapting cloud services for a research laboratory using the OpenStack open environment as an example. The structure of the cloud computing environment is developed. An example of the implementation of the installation and configuration of client and server applications using three computers is given. The proposed technology will allow the user to deploy and configure the OpenStack environment with basic services for installing virtual machines with an internal network and a set of external addresses.

QoS improvisation of delay sensitive communication using SDN based multipath routing for medical applications

The present Internet engineering architecture provides just ”best services”. Consequently, it cannot ensure Quality of Services (QoS) for sophisticated and sensitive medical applications. Today’s technology improvement helps any medical practitioner to provide medical assistant for the needy based on the knowledge available across the community. This paper aims to provide the possible way for fastest communication without any delay by prioritizing the medical application data from normal data in the network using SDN. Software-defined networking (SDN) is used to manage the networking equipment by separating the control plane and data plane. SDN is an efficient and effective solution to centralize the state and intelligence of a network. The core functionality of an SDN-enabled network is to abstract the infrastructure of the network from the applications to increase the agility, speed and scalability of traditional networking infrastructure. It also helps to deploy the applications repeatedly. It is a new way to design, deploy and manage the network and its services. This paper focuses on to create an SDN-enabled environment using the OpenStack cloud operating system. This paper also focuses on OpenContrail, which is used as a controller to separate the control plane and manage the control plane as, OpenContrail is more scalable and responsive as it can be used to manage, customize and automate the network of massive size with ease. Finally, to improve the Quality of Service(QoS), this paper proposes a multipath routing that gives QoS ensures utilizing SDN to be implemented in the SDN-enabled OpenStack environment using the OpenContrail controller. Multipath routing makes utilization of several paths amongst source and destination to ensure QoS for diverse kinds of traffic. Proposed model demonstrates that our Multipath routing algorithm can reduce the delay time within the network to make the network more robust and effective.

BUILDING A BILLION SPATIO-TEMPORAL OBJECT SEARCH AND VISUALIZATION PLATFORM

Abstract. With funding from the Sloan Foundation and Harvard Dataverse, the Harvard Center for Geographic Analysis (CGA) has developed a prototype spatio-temporal visualization platform called the Billion Object Platform or BOP. The goal of the project is to lower barriers for scholars who wish to access large, streaming, spatio-temporal datasets. The BOP is now loaded with the latest billion geo-tweets, and is fed a real-time stream of about 1 million tweets per day. The geo-tweets are enriched with sentiment and census/admin boundary codes when they enter the system. The system is open source and is currently hosted on Massachusetts Open Cloud (MOC), an OpenStack environment with all components deployed in Docker orchestrated by Kontena. This paper will provide an overview of the BOP architecture, which is built on an open source stack consisting of Apache Lucene, Solr, Kafka, Zookeeper, Swagger, scikit-learn, OpenLayers, and AngularJS. The paper will further discuss the approach used for harvesting, enriching, streaming, storing, indexing, visualizing and querying a billion streaming geo-tweets.

Approaches for Software Performance Modelling, Cloud Computing and Openstack

Performance modelling is simulation of the user inputs and then altering the systems' configuration in order to monitor performance of software. Performance modelling rarely incorporated but gradually we have realized its significance. There are various approaches for modelling and each has its own benefits and hindrances. Performance modelling when applied on the cloud is again a very challenging task. For a good model for specific software we should have a good knowledge about the software working. This paper includes all the important performance modelling approaches. Along with this, we also have an overview of the cloud and Openstack environment.

Реализация сервиса для выполнения Apache Spark задач и создания Apache Spark кластеров на основе Openstack Sahara

In this paper the problem of creating virtual clusters in clouds for big data analysis with Apache Hadoop and Apache Spark is discussed. Existing methods for Apache Spark clusters creation are described in this work. Also the implemented solution for building Apache Spark clusters and Apache Spark jobs execution in Openstack environment is described. The implemented solution is a modification for OpenStack Sahara project and it was featured in Openstack Liberty release.