Rapid detection of spammers through collaborative information sharing across multiple service providers

Azad, Muhammad Ajmal; Morla, Ricardo

doi:10.1016/j.future.2017.12.026

Cited by 16 publications

(8 citation statements)

References 54 publications

(69 reference statements)

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“…Naturally, collaboration among service providers would improve the detection time and detection accuracy because of the collective use of information from many autonomous collaborating service providers. A very few works have been reported that incorporate collaboration among service providers for rating the subscribers [137] [138] [139] [140,141].These systems could improve the detection accuracy but brings the challenges of privacy preservation and collaboration overheads.…”

Section: Robo or Telemarketing Call Detection Systemsmentioning

confidence: 99%

Consumer-facing technology fraud: Economics, attack methods and potential solutions

Ali

Azad

Centeno

et al. 2019

Future Generation Computer Systems

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Section: Robo or Telemarketing Call Detection Systemsmentioning

confidence: 99%

Consumer-facing technology fraud: Economics, attack methods and potential solutions

Ali

Azad

Centeno

et al. 2019

Future Generation Computer Systems

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“…Specifically, a trustworthy score is computed by these reputation systems based on the past behavior so as to identify malicious nodes in the communication patterns. Similarly in [32], authors have proposed a collaborative spit detection system that enables the service providers to be in collaboration with the end users without any transfer of private data. They argue that the proposed system maintains the privacy of the users with the resultant of high true positive rate and ensuring small false positive rate.…”

Section: C Anomaly Detection and Defense Techniques In Cloudsmentioning

confidence: 99%

Detection of Malware Attacks on Virtual Machines for a Self-Heal Approach in Cloud Computing using VM Snapshots

Joseph

Mukesh

2018

JCOMSS

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Abstract-Cloud Computing strives to be dynamic as a service oriented architecture (SoA). The services in the SoA are rendered in terms of private, public and in many other commercial domain aspects. These services should be secured and thus are very vital to the cloud infrastructure. In order, to secure and maintain resilience in the cloud, it not only has to have the ability to identify the known threats but also to new challenges that target the infrastructure of a cloud. In this paper, we introduce and discuss a detection method of malwares from the VM memory snapshot analysis and the corresponding VM snapshots are classified into attacked and non-attacked VM snapshots. As snapshots are always taken to be a backup in the backup servers, this approach could reduce the overhead of the backup server with a self-healing capability of the VMs in the local cloud infrastructure itself without any compromised VM in the backup server. A machine learning approach is projected here to classify the attacked and non attacked snapshots. The features of the snapshots are gathered from the API calls of VM instances. Our proposed scheme has a high detection accuracy of about 93% while having the capability to classify and detect different types of malwares with respect to the VM snapshots. Finally the paper exhibits an algorithm using snapshots to detect and thus to selfheal. The self-healing approach with machine learning algorithms can determine new threats with some prior knowledge of its functionality.

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“…Ajmal et al [14] proposed a framework to achieve privacypreserving collaboration across multiple service providers to combat telecoms spam. Azad and Morla [15], [16] introduced a method to filter smart spammers in a decentralized schema with privacy-aware. Their model needs improvement on matching strategy for they only focus on the distance of two signatures.…”

Section: Introductionmentioning

confidence: 99%

Cooperative Fraud Detection Model With Privacy-Preserving in Real CDR Datasets

2019

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The researchers have shown broad concern about detection and recognition of fraudsters since telecommunication operators and the individual user are both suffering significant losses from fraud activities. Researchers have proposed various solutions to counter fraudulent activity. However, those methods may lose effectiveness in fraud detection because fraudsters always tend to cover their tracks by roaming among different telecommunication operators. What is more, due to the lack of real data, researchers have to do simulations in a virtual scenario, which makes their models and results less persuasive. In our previous paper, we proposed a novel strategy with high accuracy and security through cooperation among mobile telecommunication operators. In this manuscript, we will validate it in a real-world scenario using real Call Detail Records(CDR) data. We apply the Latent Dirichlet Allocation (LDA) model to profile users. Then we use a method based on Maximum Mean Discrepancy (MMD) to compare the distribution of samples to match roaming fraudsters. Cooperation between telecommunication operators may boost the accuracy of detection while the potential risk of privacy leakage exists. A strategy based on Differential Privacy(DP) is used to address this problem. We demonstrate that it can detect the fraudsters without revealing private data. Our model was validated using simulated dataset and showed its effectiveness. In this manuscript, experiments are performed on real CDRs data, and the result shows that our method has impressive performance in the real-world scenario as well.

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Rapid detection of spammers through collaborative information sharing across multiple service providers

Cited by 16 publications

References 54 publications

Consumer-facing technology fraud: Economics, attack methods and potential solutions

Consumer-facing technology fraud: Economics, attack methods and potential solutions

Detection of Malware Attacks on Virtual Machines for a Self-Heal Approach in Cloud Computing using VM Snapshots

Cooperative Fraud Detection Model With Privacy-Preserving in Real CDR Datasets

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