Using Tabu-Voronoi clustering heuristics with key management scheme for heterogeneous sensor networks

Gagneja, Kanwalinderjit Kaur; Nygard, Kendall E.; Singh, Navninderjit

doi:10.1109/icufn.2012.6261662

“…More specifically, by using a graph of all Bitcoin transactions and a statistical approach, [15] can identify that several large (>50,000 BTC) transactions originated from a single source. Similarly, by combining a statistical approach to a subset of simulated transactions, Androulaki, Elli, et al concludes that the profile of users can be revealed using a behavior-based clustering approach [16]. A clustering technique was also used by Meiklejohn, Sarah, et al, which involves grouping transactions by known users and user types [17].…”

Section: Traceability Of Cryptocurrenciesmentioning

confidence: 99%

Traceability of Cryptocurrency Transactions using Blockchain Analytics

Gagneja¹

2020

IJCDS

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Cryptocurrencies are increasingly used as a medium to conduct illicit activity. The fundamental concept empowering Bitcoin and other cryptocurrencies is the blockchain, which is a distributed ledger technology that transactions are stored on. The entire ledger, which is widely distributed among the peer-to-peer network, is susceptible to the temporal analysis of the transaction to be performed. This type of blockchain analytics threatens the expected privacy of transactions and has resulted in the growing number of privacy-oriented solutions that reduce the traceability and increase the anonymity of crypto transactions. Despite the evolving technological advancements of privacy-oriented features for cryptocurrencies, traceability of transactions is still possible. In this paper, the core features of blockchain are reviewed as well as their resistance to traceability. Existing countermeasures that attempt to obfuscate user activity are also considered. Also, a prototype software solution is proposed that could be used in the incidence response of criminal activity involving cryptocurrencies.

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“…From these results, clearly kNN is the algorithm that should be used in our malware detection process [16][17][18][19].…”

mentioning

confidence: 99%

Untitled

2022

GJES

0

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Smartphones have been an integral part of everyday life for society since their development. Naturally, the popularity of these devices has brought an equal amount of malware designed specifically for these smartphone devices. The struggle to keep these devices secure and in turn the sensitive information stored on these devices from getting into the wrong hands has become an ever-evolving endeavor. With the sheer amount of malware produced coupled with the intelligent, polymorphic nature of the malicious software, it has become increasingly difficult to protect against them. In this paper, we propose a dynamic approach to classifying malicious Android applications that does not rely solely on the signatures of said applications. Instead, we analyze the Android Manifest of the dataset to classify whether an application should be considered malicious or benign.

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“…From these results, clearly kNN is the algorithm that should be used in our malware detection process [16][17][18][19].…”

mentioning

confidence: 99%

Malicious Android Applications’ Classification using Machine Learning

Kaur

¹

2022

GJES

0

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Smartphones have been an integral part of everyday life for society since their development. Naturally, the popularity of these devices has brought an equal amount of malware designed specifically for these smartphone devices. The struggle to keep these devices secure and in turn the sensitive information stored on these devices from getting into the wrong hands has become an ever-evolving endeavor. With the sheer amount of malware produced coupled with the intelligent, polymorphic nature of the malicious software, it has become increasingly difficult to protect against them. In this paper, we propose a dynamic approach to classifying malicious Android applications that does not rely solely on the signatures of said applications. Instead, we analyze the Android Manifest of the dataset to classify whether an application should be considered malicious or benign.

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Using Tabu-Voronoi clustering heuristics with key management scheme for heterogeneous sensor networks

Cited by 9 publications

References 8 publications

Traceability of Cryptocurrency Transactions using Blockchain Analytics

Traceability of Cryptocurrency Transactions using Blockchain Analytics

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Malicious Android Applications’ Classification using Machine Learning

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