Minimized False Alarm predictive Threshold for Cloud Service Providers

2019 ◽  
Vol 12 ◽  
Author(s):  
Amandeep Singh Arora ◽  
Linesh Raja ◽  
Barkha Bahl
Keyword(s):  
Machine Learning ◽  
Cross Validation ◽  
Service Providers ◽  
Denial Of Service ◽  
Cloud Service ◽  
Machine Learning Algorithms ◽  
Cloud Services ◽  
Estimation Accuracy ◽  
Denial Of Service Attacks

Cloud Security is a strong hindrance which discourage organizations to move toward cloud despite huge benefits. Denial of Service attacks [1] operated via distributed systems compromise availability of cloud services. Techniques to identify distributed denial of service attacks with minimized false positives is highly required to ensure availability of cloud services to genuine users. Classification of incoming requests and outgoing responses using machine learning algorithms is a quite effective way of detection and prevention. In this paper, Ten algorithms of machine learning have been evaluated for performance and detection accuracies. An estimation accuracy method known as F-Hold cross validation [2] is used for time efficient analysis.

scholarly journals Dolus : cyber defense using pretense against DDoS attacks in cloud platforms

2017 ◽  
Author(s):  
◽  
Roshan Lal Neupane
Keyword(s):  
Virtual Machines ◽  
Service Providers ◽  
Denial Of Service ◽  
Cloud Service ◽  
Cloud Services ◽  
Ddos Attacks ◽  
Child Play ◽  
Original Target ◽  
Multiple Network ◽  
The Impact

Cloud-hosted services are being increasingly used in online businesses in e.g., retail, healthcare, manufacturing, entertainment due to benefits such as scalability and reliability. These benefits are fueled by innovations in orchestration of cloud platforms that make them totally programmable as Software Defined everything Infrastructures (SDxI). At the same time, sophisticated targeted attacks such as Distributed Denial-of-Service (DDoS) are growing on an unprecedented scale threatening the availability of online businesses. In this thesis, we present a novel defense system called Dolus to mitigate the impact of DDoS attacks launched against high-value services hosted in SDxI-based cloud platforms. Our Dolus system is able to initiate a pretense in a scalable and collaborative manner to deter the attacker based on threat intelligence obtained from attack feature analysis in a two-stage ensemble learning scheme. Using foundations from pretense theory in child play, Dolus takes advantage of elastic capacity provisioning via quarantine virtual machines and SDxI policy co-ordination across multiple network domains. To maintain the pretense of false sense of success after attack identification, Dolus uses two strategies: (i) dummy traffic pressure in a quarantine to mimic target response time profiles that were present before legitimate users were migrated away, and (ii) Scapy-based packet manipulation to generate responses with spoofed IP addresses of the original target before the attack traffic started being quarantined. From the time gained through pretense initiation, Dolus enables cloud service providers to decide on a variety of policies to mitigate the attack impact, without disrupting the cloud services experience for legitimate users. We evaluate the efficacy of Dolus using a GENI Cloud testbed and demonstrate its real-time capabilities to: (a) detect DDoS attacks and redirect attack traffic to quarantine resources to engage the attacker under pretense, and (b) coordinate SDxI policies to possibly block DDoS attacks closer to the attack source(s).

scholarly journals Machine Learning-based Orchestration of Containers: A Taxonomy and Future Directions

2022 ◽  
Author(s):  
Zhiheng Zhong ◽  
Minxian Xu ◽  
Maria Alejandra Rodriguez ◽  
Chengzhong Xu ◽  
Rajkumar Buyya
Keyword(s):  
Machine Learning ◽  
Performance Metrics ◽  
Service Providers ◽  
Cloud Service ◽  
Research Problem ◽  
Resource Provisioning ◽  
Machine Learning Algorithms ◽  
Complex Environments ◽  
Future Directions ◽  
Container Orchestration

Containerization is a lightweight application virtualization technology, providing high environmental consistency, operating system distribution portability, and resource isolation. Existing mainstream cloud service providers have prevalently adopted container technologies in their distributed system infrastructures for automated application management. To handle the automation of deployment, maintenance, autoscaling, and networking of containerized applications, container orchestration is proposed as an essential research problem. However, the highly dynamic and diverse feature of cloud workloads and environments considerably raises the complexity of orchestration mechanisms. Machine learning algorithms are accordingly employed by container orchestration systems for behavior modelling and prediction of multi-dimensional performance metrics. Such insights could further improve the quality of resource provisioning decisions in response to the changing workloads under complex environments. In this paper, we present a comprehensive literature review of existing machine learning-based container orchestration approaches. Detailed taxonomies are proposed to classify the current researches by their common features. Moreover, the evolution of machine learning-based container orchestration technologies from the year 2016 to 2021 has been designed based on objectives and metrics. A comparative analysis of the reviewed techniques is conducted according to the proposed taxonomies, with emphasis on their key characteristics. Finally, various open research challenges and potential future directions are highlighted.

scholarly journals exprso: an R-package for the rapid implementation of machine learning algorithms

F1000Research ◽  
2017 ◽  
Vol 5 ◽  
pp. 2588 ◽  
Author(s):  
Thomas Quinn ◽  
Daniel Tylee ◽  
Stephen Glatt
Keyword(s):  
Machine Learning ◽  
Cross Validation ◽  
Object Oriented ◽  
R Package ◽  
Machine Learning Algorithms ◽  
Ensemble Classification ◽  
Scientific Investigations ◽  
Binary Classifiers ◽  
Multi Class Classification

Machine learning plays a major role in many scientific investigations. However, non-expert programmers may struggle to implement the elaborate pipelines necessary to build highly accurate and generalizable models. We introduce exprso, a new R package that is an intuitive machine learning suite designed specifically for non-expert programmers. Built initially for the classification of high-dimensional data, exprso uses an object-oriented framework to encapsulate a number of common analytical methods into a series of interchangeable modules. This includes modules for feature selection, classification, high-throughput parameter grid-searching, elaborate cross-validation schemes (e.g., Monte Carlo and nested cross-validation), ensemble classification, and prediction. In addition, exprso also supports multi-class classification (through the 1-vs-all generalization of binary classifiers) and the prediction of continuous outcomes.

scholarly journals exprso: an R-package for the rapid implementation of machine learning algorithms

F1000Research ◽  
2016 ◽  
Vol 5 ◽  
pp. 2588 ◽  
Author(s):  
Thomas Quinn ◽  
Daniel Tylee ◽  
Stephen Glatt
Keyword(s):  
Machine Learning ◽  
Cross Validation ◽  
Object Oriented ◽  
R Package ◽  
Machine Learning Algorithms ◽  
Ensemble Classification ◽  
Scientific Investigations ◽  
Binary Classifiers ◽  
Multi Class Classification

Machine learning plays a major role in many scientific investigations. However, non-expert programmers may struggle to implement the elaborate pipelines necessary to build highly accurate and generalizable models. We introduce here a new R package, exprso, as an intuitive machine learning suite designed specifically for non-expert programmers. Built primarily for the classification of high-dimensional data, exprso uses an object-oriented framework to encapsulate a number of common analytical methods into a series of interchangeable modules. This includes modules for feature selection, classification, high-throughput parameter grid-searching, elaborate cross-validation schemes (e.g., Monte Carlo and nested cross-validation), ensemble classification, and prediction. In addition, exprso provides native support for multi-class classification through the 1-vs-all generalization of binary classifiers. In contrast to other machine learning suites, we have prioritized simplicity of use over expansiveness when designing exprso.

scholarly journals Machine Learning Classification of Port Scanning and DDoS Attacks: A Comparative Analysis

2021 ◽  
Vol 40 (1) ◽  
pp. 215-229
Author(s):  
Muhammad Aamir ◽  
Syed Sajjad Hussain Rizvi ◽  
Manzoor Ahmed Hashmani ◽  
Muhammad Zubair ◽  
Jawwad Ahmed . Usman
Keyword(s):  
Machine Learning ◽  
Comparative Analysis ◽  
Denial Of Service ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Subjective Rating ◽  
Support Vector ◽  
Ddos Attacks ◽  
Testing Accuracy

Cyber security is one of the major concerns of today’s connected world. For all the platforms of today’s communication technology such as wired, wireless, local and remote access, the hackers are present to corrupt the system functionalities, circumvent the security measures and steal sensitive information. Amongst many techniques of hackers, port scanning and Distributed Denial of Service (DDoS) attacks are very common. In this paper, the benefits of machine learning are taken into consideration for classification of port scanning and DDoS attacks in a mix of normal and attack traffic. Different machine learning algorithms are trained and tested on a recently published benchmark dataset (CICIDS2017) to identify the best performing algorithms on the data which contains more recent vectors of port scanning and DDoS attacks. The classification results show that all the variants of discriminant analysis and Support Vector Machine (SVM) provide good testing accuracy i.e. more than 90%. According to a subjective rating criterion mentioned in this paper, 9 algorithms from a set of machine learning experiments receive the highest rating (good) as they provide more than 85% classification (testing) accuracy out of 22 total algorithms. This comparative analysis is further extended to observe training performance of machine learning models through k-fold cross validation, Area Under Curve (AUC) analysis of the Receiver Operating Characteristic (ROC) curves, and dimensionality reduction using the Principal Component Analysis (PCA). To the best of our knowledge, a comprehensive comparison of various machine learning algorithms on CICIDS2017 dataset is found to be deficient for port scanning and DDoS attacks while considering such recent features of attack.

scholarly journals Detection of DDoS Attacks in OpenStack-based Private Cloud Using Apache Spark

2020 ◽  
Vol 4 ◽  
pp. 62-71
Author(s):  
Shweta Gumaste ◽  
Narayan D. G. ◽  
Sumedha Shinde ◽  
Amit K
Keyword(s):  
Machine Learning ◽  
Real Time ◽  
Service Providers ◽  
Distributed Processing ◽  
Denial Of Service ◽  
Machine Learning Algorithms ◽  
Apache Spark ◽  
Ddos Attacks ◽  
Ddos Detection ◽  
Real Time Detection

Security is a critical concern for cloud service providers. Distributed denial of service (DDoS) attacks are the most frequent of all cloud security threats, and the consequences of damage caused by DDoS are very serious. Thus, the design of an efficient DDoS detection system plays an important role in monitoring suspicious activity in the cloud. Real-time detection mechanisms operating in cloud environments and relying on machine learning algorithms and distributed processing are an important research issue. In this work, we propose a real-time detection of DDoS attacks using machine learning classifiers on a distributed processing platform. We evaluate the DDoS detection mechanism in an OpenStack-based cloud testbed using the Apache Spark framework. We compare the classification performance using benchmark and real-time cloud datasets. Results of the experiments reveal that the random forest method offers better classifier accuracy. Furthermore, we demonstrate the effectiveness of the proposed distributed approach in terms of training and detection time.

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