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

Elsayed, Marwa; Zulkernine, Mohammad

doi:10.1109/access.2020.2977325

Cited by 26 publications

(6 citation statements)

References 40 publications

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“…In [ 27 ], the authors proposed PredictDeep, a security analytical framework for known and unknown anomaly detection and prediction in Big Data systems. PredictDeep is proposed as a service to be offered to cloud users.…”

Section: Related Workmentioning

confidence: 99%

Deep learning approach to security enforcement in cloud workflow orchestration

Kassabi¹,

Serhani²,

Masud³

et al. 2023

J Cloud Comp

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Supporting security and data privacy in cloud workflows has attracted significant research attention. For example, private patients’ data managed by a workflow deployed on the cloud need to be protected, and communication of such data across multiple stakeholders should also be secured. In general, security threats in cloud environments have been studied extensively. Such threats include data breaches, data loss, denial of service, service rejection, and malicious insiders generated from issues such as multi-tenancy, loss of control over data and trust. Supporting the security of a cloud workflow deployed and executed over a dynamic environment, across different platforms, involving different stakeholders, and dynamic data is a difficult task and is the sole responsibility of cloud providers. Therefore, in this paper, we propose an architecture and a formal model for security enforcement in cloud workflow orchestration. The proposed architecture emphasizes monitoring cloud resources, workflow tasks, and the data to detect and predict anomalies in cloud workflow orchestration using a multi-modal approach that combines deep learning, one class classification, and clustering. It also features an adaptation scheme to cope with anomalies and mitigate their effect on the workflow cloud performance. Our prediction model captures unsupervised static and dynamic features as well as reduces the data dimensionality, which leads to better characterization of various cloud workflow tasks, and thus provides better prediction of potential attacks. We conduct a set of experiments to evaluate the proposed anomaly detection, prediction, and adaptation schemes using a real COVID-19 dataset of patient health records. The results of the training and prediction experiments show high anomaly prediction accuracy in terms of precision, recall, and F1 scores. Other experimental results maintained a high execution performance of the cloud workflow after applying adaptation strategy to respond to some detected anomalies. The experiments demonstrate how the proposed architecture prevents unnecessary wastage of resources due to anomaly detection and prediction.

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Section: Related Workmentioning

confidence: 99%

Deep learning approach to security enforcement in cloud workflow orchestration

Kassabi¹,

Serhani²,

Masud³

et al. 2023

J Cloud Comp

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“…This solution produced better outcomes in regards of the fast discovery and forecasting of incidents of security and was able to cope with the multifaceted nature of clouds. The problem with this technique, though, is that it doesn't recognize and classify irregularities in a range of classifications in accordance with the changes in system function they cause [21]. Nguyen et al (2021) examined the difficulties associated with compute offloading and cybersecurity in a multiple-userfriendly mobile edge-cloud computing framework utilizing blockchain.…”

Section: Abirami Et Al (2022) Demonstrated How "Deepmentioning

confidence: 99%

Ensembling of Attention-based Recurrent Units for Detection and Mitigation of Multiple Attacks in Cloud

2023

IJACSA

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In the recent years, number of threats to network security increases exponentially as the Internet users which poses serious threat in cloud storage application. Detection and defending against the multiple threats are currently a hot topic in industry and considered as one of the challenging research in academia. Many methodologies and algorithms devised to predict the different attacks. Still, most of the methods cannot simultaneously achieve high performance of prediction with a small number of false alarm rates. In this scenario, Deep Learning (DL) algorithms are appropriate and intelligent to categorize the multiple attacks. Still, most of the existing DL techniques are computationally inefficient that may degrade the performance in predicting the both normal and attack information. To overcome this aforementioned problem, this paper proposes the hybrid combination of attention maps with deep recurrent networks to mitigate the multiple attacks with low computational overhead. Initially, the pre-processing step is proposed to the inputs in a specified range. Later on, input data are fed into the Attention Enabled Gated Recurrent Networks (AEGRN) which is used to remove the redundant features and select the optimal features that aids for the better classification. Further to enhance the faster response, deep feed forward layers are proposed to replace the traditional deep neural networks. Numerous metrics for performance, including accuracy, precision, recall, specificity, and F1-score, are examined and analyzed as part of the thorough experimentation utilizing multiple datasets, including NSL-KDD-99, UNSW -2019, and CIDC-001. Comparisons of performance between the method that is suggested and existing models developed with DL are used to demonstrate the proposed algorithm's supremacy. The suggested framework surpasses the other DL models and has the best accuracy in predicting with little computational overhead, according to an investigation.

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“…Thus anomaly detection approaches should be developed for the IoE security. Authors in [90] use log data and graph analytics approaches for anomaly detection. Ryoo et al [91] devise a solution for IoE security and provide actionable insights into the steps takes for the security of IoE ecosystem.…”

Section: B Security Privacy and Trust Challengesmentioning

confidence: 99%

Internet of Everything: Enabling Technologies, Applications, Security and Challenges

Khan¹,

Rafique²,

Haider³

et al. 2022

Preprint

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<p>Internet of Everything (IoE) connects billions of people, devices, objects and things to the Internet for autonomous services provisioning. IoE benefits from the value created by the compound impacts of connecting people, processes, things, and data. The interoperability among heterogeneous components facilitates seamless communication exchange for service provisioning in almost all the fields of life. Apart from providing immense benefits in autonomous services provisioning, it suffers from implementation issues, architectural considerations, and security and privacy vulnerabilities. There is a lack of surveys that comprehensively discuss all the aspects of IoE. Therefore, we design this survey to discuss key effective implementation of IoE. We discuss enabling technologies, architectural components, and expectations for an efficient realization of IoE. We discuss state-of-the-art use cases, synergies, and implementations of IoE that could be used as a road-map for and effective implementation of IoE-enabled systems. We highlight key security, privacy, and trust issues. We identify lethal attack scenarios and highlight countermeasures against these attacks. Finally, we discuss current challenges, their solutions, and future research directions. This survey provides key insights for implementing future IoE systems.</p>

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

Cited by 26 publications

References 40 publications

Deep learning approach to security enforcement in cloud workflow orchestration

Deep learning approach to security enforcement in cloud workflow orchestration

Ensembling of Attention-based Recurrent Units for Detection and Mitigation of Multiple Attacks in Cloud

Internet of Everything: Enabling Technologies, Applications, Security and Challenges

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