Spam appears in various forms and the current trend in spamming is moving towards multimedia spam objects. Image spam is a new type of spam attacks which attempts to bypass the spam filters that mostly text-based. Spa...
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Spam appears in various forms and the current trend in spamming is moving towards multimedia spam objects. Image spam is a new type of spam attacks which attempts to bypass the spam filters that mostly text-based. Spamming attacks the users in many ways and these are usually countered by having a server to filter the spammers. This paper provides a fully-distributed pattern recognition system within p2p networks using the distributed associative memory tree (DASMET) algorithm to detect spam which is cost-efficient and not prone to a single point of failure, unlike the server-based systems. This algorithm is scalable for large and frequently updated data sets, and specifically designed for data sets that consist of similar occurring *** have evaluated our system against centralised state-of-the-art algorithms (NN, k-NN, naive Bayes, BpNN and RBFN) and distributed p2p-based algorithms (Ivote-DpV, ensemble k-NN, ensemble naive Bayes, and p2p-GN). The experimental results show that our method is highly accurate with a 98 to 99% accuracy rate, and incurs a small number of messages-in the best-case, it requires only two messages per recall test. In summary, our experimental results show that the DAS-MET performs best with a relatively small amount of resources for the spam detection compared to other distributed methods.
Application classification from Netflow traces is a challenging process due to the lack of payload information. It is even becoming more challenging when the applications running in the network tend to hide under well...
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ISBN:
(纸本)9781424488650
Application classification from Netflow traces is a challenging process due to the lack of payload information. It is even becoming more challenging when the applications running in the network tend to hide under well known ports, encrypt packets and are distributed. In this paper, we propose an in-the-box apparatus for Netflow classification of Bit Torrent applications. The apparatus includes several device optimizations and requires low processing power. It was developed by reverse engineering the Bit Torrent protocol and by identifying connection patterns. The accuracy of the algorithm reaches high values specifically for Bit Torrent peers that use the DHT protocol.
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