Towards Security Monitoring for Cloud Analytic Applications

Elsayed, Marwa; Zulkernine, Mohammad

doi:10.1109/bds/hpsc/ids18.2018.00028

Cited by 11 publications

(11 citation statements)

References 14 publications

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“…Further to log-based anomaly detection approaches, there are other research efforts share the same goal for improving the security of big data systems. These approaches span different research directions, ranging from differential privacy [36], integrity verification [37]- [40], policy enforcement [41]- [44], data provenance [45]- [48], honeypot-based [49], to encryptionbased [50] mechanisms, Security Monitoring [51], among others.…”

Section: Graph Neural Networkmentioning

confidence: 99%

“…Our recent approach (SMaaS) [51] proposes an advanced security monitoring service that leverages streaming big data pipeline and Cloud technologies as key enablers to elude the limitations in the aforementioned solutions. However, SMaaS focuses on detecting common patterns of security anomalies for analytic applications.…”

Section: Graph Neural Networkmentioning

confidence: 99%

“…The proposed framework integrates with Security Monitoring as a Service (SMaaS) framework, our previously proposed monitoring systems [51]. SMaaS leverages streaming data analytics to automate the collection, management, processing, analysis, and visualization of log data from multiple sources, making it valuable, comprehensive, and cohesive for security inspection [51]. It is mainly responsible for detecting common patterns of security anomalies at real-time.…”

Section: Introductionmentioning

confidence: 99%

“…On one hand, SMaaS consolidates the unstructured heterogenous log data (i.e., text), emitted from various sources (e.g., log4j and Syslog ) of a monitored cluster (e.g., Hadoop), into a semi-structured format (i.e., JSON ''JavaScript Object Notation'') representing an information flow profile. This profile is useful to model the normal behavior of a monitored system and detect known patterns of security anomalies, tackling the first three challenges (a-c) [51]. In what follows, we will refer to the log data and the extracted profile as the gleaned data.…”

Section: Introductionmentioning

confidence: 99%

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PredictDeep: Security Analytics as a Service for Anomaly Detection and Prediction

Elsayed

Zulkernine

2020

IEEE Access

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As businesses embrace digitization, the Internet of Everything (IoE) begins to take shape and the Cloud continues to empower new innovations for big data-at the heart, Cloud analytic applications gain increasing momentum. Such applications have remarkable benefits for big data processing, making it easy, fast, scalable, and cost-effective; albeit, they pose many security risks. Security breaches causing anomalous activities due to malicious, vulnerable, or misconfigured analytic applications are considered the top security risks to big ''sensitive'' data. The risk is further expanded from the coupling of data analytics with the Cloud. Towards maintaining secure and trustworthy applications, effective anomaly detection and prediction become crucial tasks to be offered by Cloud providers. This paper presents, PredictDeep, a novel security analytics framework for anomaly detection and prediction. The proposed framework leverages log data collected from monitoring systems with graph analytics and deep learning techniques to add intelligence for detecting and predicting known and unknown patterns of security anomalies. It represents the collected data and transforms them into a graph model. The graph model captures the analytical activities as well as their interrelation. In this sense, such a model provides informed insight of the monitored application, understanding its behavior, and revealing anomalous patterns. Different from existing traditional rule-based machine learning and statistics-based approaches, our solution takes the benefits of incorporating not only available node attributes but also graph structure and context information to extract rich features that boost the anomaly classification and prediction. We leverage graph embeddings to represent the nodes and relationships in the graph model as feature vectors to learn and predict anomalies in an inductive way utilizing recent advanced deep graph neural network techniques. This design augments our solution with robustness and computational efficiency. Extensive experiments are conducted over an open-source Hadoop log dataset. The evaluation results demonstrate that PredictDeep is a viable solution and very effective.

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Section: Graph Neural Networkmentioning

confidence: 99%

Section: Graph Neural Networkmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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PredictDeep: Security Analytics as a Service for Anomaly Detection and Prediction

Elsayed

Zulkernine

2020

IEEE Access

Self Cite

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“…Although they provide outstanding benefits on the perceived trust, they lack of generality and cannot be easily adapted to current scenarios, where different services deployed on hybrid public and private infrastructures are composed at run time. Many of these solutions are in fact ad hoc (Cheah et al, 2018;Elsayed and Zulkernine, 2018), meaning they cannot handle a modern IT system as a whole. Moreover, existing assurance techniques, and corresponding frameworks, require some effort for being integrated with the target system, interfering with its normal operation (e.g., performance), and introducing not-negligible (monetary and business) costs.…”

Section: Introductionmentioning

confidence: 99%

Stay Thrifty, Stay Secure: A VPN-based Assurance Framework for Hybrid Systems

Anisetti

Ardagna

Bena

et al. 2020

Proceedings of the 17th International Joint Conference on E-Business and Telecommunications

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Security assurance provides a wealth of techniques to demonstrate that a target system holds some nonfunctional properties and behaves as expected. These techniques have been recently applied to the cloud ecosystem, while encountering some critical issues that reduced their benefit when hybrid systems, mixing public and private infrastructures, are considered. In this paper, we present a new assurance framework that evaluates the trustworthiness of hybrid systems, from traditional private networks to public clouds. It implements an assurance process that relies on a Virtual Private Network (VPN)-based solution to smoothly integrate with the target systems. The assurance process provides a transparent and non-invasive solution that does not interfere with the working of the target system. The performance of the framework have been experimentally evaluated in a simulated scenario.

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A Taxonomy of Security as a Service

Elsayed

Zulkernine

2018

Lecture Notes in Computer Science

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Towards Security Monitoring for Cloud Analytic Applications

Cited by 11 publications

References 14 publications

PredictDeep: Security Analytics as a Service for Anomaly Detection and Prediction

PredictDeep: Security Analytics as a Service for Anomaly Detection and Prediction

Stay Thrifty, Stay Secure: A VPN-based Assurance Framework for Hybrid Systems

A Taxonomy of Security as a Service

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