Privacy-Preserving Passive DNS

Papadopoulos, Pavlos; Pitropakis, Nikolaos; Buchanan, William J.; Lo, Owen; Katsikas, Sokratis K.

doi:10.3390/computers9030064

Cited by 18 publications

(15 citation statements)

References 30 publications

(30 reference statements)

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“…DNS is used to set up a domain name for the IIS server so that it can be accessed without using the webserver’s IP address. The DNS service intakes requests to resolve the more human-friendly website’s domain name to the webserver’s IP address [ 88 ]. Microsoft suggests that one method of testing whether a target machine is a functioning DNS server is by utilising a PowerShell command [ 89 ].…”

Section: Methodology and Architecturementioning

confidence: 99%

Ransomware: Analysing the Impact on Windows Active Directory Domain Services

McDonald

Papadopoulos

Pitropakis

et al. 2022

Sensors

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Ransomware has become an increasingly popular type of malware across the past decade and continues to rise in popularity due to its high profitability. Organisations and enterprises have become prime targets for ransomware as they are more likely to succumb to ransom demands as part of operating expenses to counter the cost incurred from downtime. Despite the prevalence of ransomware as a threat towards organisations, there is very little information outlining how ransomware affects Windows Server environments, and particularly its proprietary domain services such as Active Directory. Hence, we aim to increase the cyber situational awareness of organisations and corporations that utilise these environments. Dynamic analysis was performed using three ransomware variants to uncover how crypto-ransomware affects Windows Server-specific services and processes. Our work outlines the practical investigation undertaken as WannaCry, TeslaCrypt, and Jigsaw were acquired and tested against several domain services. The findings showed that none of the three variants stopped the processes and decidedly left all domain services untouched. However, although the services remained operational, they became uniquely dysfunctional as ransomware encrypted the files pertaining to those services.

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Section: Methodology and Architecturementioning

confidence: 99%

Ransomware: Analysing the Impact on Windows Active Directory Domain Services

McDonald

Papadopoulos

Pitropakis

et al. 2022

Sensors

Self Cite

View full text Add to dashboard Cite

show abstract

“…The architecture we propose involves configuring a DNS server inside a Docker container [54] which enables the autonomous management of this service. An independently managed DNS allows for a refined configuration of zone parameters and makes it possible to propagate updates since the parameters can be managed internally quickly.…”

Section: Dns Servermentioning

confidence: 99%

Deploying Efficiently Modern Applications on Cloud

Perri¹,

Simonetti²,

Gervasi³

2022

Preprint

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This study analyses some of the leading technologies for the construction and configuration of IT infrastructures to provide services to users. For modern applications, guaranteeing service continuity even in very high computational load or network problems is essential. Our configuration has among the main objectives of being highly available (HA) and horizontally scalable, that is, able to increase the computational resources that can be delivered when needed and reduce them when they are no longer necessary. Various architectural possibilities are analysed, and the central schemes used to tackle problems of this type are also described in terms of disaster recovery. The benefits offered by virtualisation technologies are highlighted and are bought with modern techniques for managing Docker containers that will be used to build the back-end of a sample infrastructure related to a use-case we have developed. In addition to this, an in-depth analysis is reported on the central autoscaling policies that can help manage high loads of requests from users to the services provided by the infrastructure. The results we have presented show an average response time of 21.7 milliseconds with a standard deviation of 76.3 milliseconds showing excellent responsiveness. Some peaks are associated with high-stress events for the infrastructure, but the response time does not exceed 2 seconds even in this case. The results of the considered use case studied for nine months are presented and discussed. In the study period, we improved the back-end configuration and defined the main metrics to deploy the web application efficiently.

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“…There are works that presented the use of another emerging technology such as blockchain, which could be combined with ML. In the work of [104,105] the authors' presented infrastructures that could protect certain private data from the stored records and display of other non-private. However, the feasibility of performing ML in data stored in their blockchain has not been tested and remains an open question.…”

Section: Related Workmentioning

confidence: 99%

Privacy and Trust Redefined in Federated Machine Learning

Papadopoulos

Abramson

Hall

et al. 2021

MAKE

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A common privacy issue in traditional machine learning is that data needs to be disclosed for the training procedures. In situations with highly sensitive data such as healthcare records, accessing this information is challenging and often prohibited. Luckily, privacy-preserving technologies have been developed to overcome this hurdle by distributing the computation of the training and ensuring the data privacy to their owners. The distribution of the computation to multiple participating entities introduces new privacy complications and risks. In this paper, we present a privacy-preserving decentralised workflow that facilitates trusted federated learning among participants. Our proof-of-concept defines a trust framework instantiated using decentralised identity technologies being developed under Hyperledger projects Aries/Indy/Ursa. Only entities in possession of Verifiable Credentials issued from the appropriate authorities are able to establish secure, authenticated communication channels authorised to participate in a federated learning workflow related to mental health data.

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Privacy-Preserving Passive DNS

Cited by 18 publications

References 30 publications

Ransomware: Analysing the Impact on Windows Active Directory Domain Services

Ransomware: Analysing the Impact on Windows Active Directory Domain Services

Deploying Efficiently Modern Applications on Cloud

Privacy and Trust Redefined in Federated Machine Learning

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