Wyniki wyszukiwania - BazTech

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Wykorzystanie drzew sufiksowych do efektywnej prezentacji podobieństw sesji z systemu pułapek honeypot

Skłodowski Jakub, Arabas Piotr

Cybersecurity and Law

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2023

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Vol. 9 (1)

298--315

PL

W artykule przestawiono prototyp systemu do analizy danych z sieci interaktywnych pułapek, tzw. honeypotów. Szczególną uwagę zwrócono na algorytm wyszukiwania podobieństw w zbieranych zapisach sesji ssh. Algorytm ten wyszukuje w sesjach uogólnione wzorce z wykorzystaniem drzew sufiksowych. Wzorce te dzięki zaproponowanej metodzie redukcji mogą być następnie wykorzystane do wygodnej prezentacji zarejestrowanych sesji i efektywnego wyszukiwania. Podsumowanie pracy stanowią przykłady wykorzystania algorytmu.

EN

The article presents a prototype of a system for analyzing data from a honeypot network. A special attention is paid to finding similarities in the collected ssh sessions. The algorithm proposed looks for generalized patterns in the session using suffix trees. The patterns can be used for a convenient presentation of the displayed sessions and for searching. The examples of analysis carried out with the help of the algorithm are presented.

2

On Computing Average Common Substring Over Run Length Encoded Sequences

Hooshmand S., Abedin P., Tavakoli N., Thankachan S. V.

Fundamenta Informaticae

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2018

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Vol. 163, nr 3

267--273

EN

The Average Common Substring (ACS) is a popular alignment-free distance measure for phylogeny reconstruction. The ACS of a sequence X[1; x] w.r.t. another sequence Y[1; y] is ACS(X;Y) =[formula] The lcp(., .) of two input sequences is the length of their longest common prefix. The ACS can be computed in O(n) space and time, where n = x + y is the input size. The compressed string matching is the study of string matching problems with the following twist: the input data is in a compressed format and the underling task must be performed with little or no decompression. In this paper, we revisit the ACS problem under this paradigm where the input sequences are given in their run-length encoded format. We present an algorithm to compute ACS(X,Y) in O(N log N) time using O(N) space, where N is the total length of sequences after run-length encoding.

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A Linear Space Data Structure for Range LCP Queries

Ganguly A., Shah R., Patil M., Thankachan S. V.

Fundamenta Informaticae

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2018

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Vol. 163, nr 3

245--251

EN

Range LCP (longest common prefix) is an extension of the classical LCP problem and is defined as follows: Preprocess a string S[1...n] of n characters, such that whenever an interval [i; j] comes as a query, we can report max{LCP(Sp,Sq) i ≤ p < q ≤ j} Here LCP((Sp, Sq) is the longest common prefix of the suffixes of S starting at locations p and q, and LCP((Sp,Sq)) is its length. This problem was first addressed by Amir et al. [ISAAC, 2011]. They showed that the query can be answered in O(log log n) time using an O(n log 1+ε n) space data structure for an arbitrarily small constant ε > 0. In an attempt to reduce the space bound, they presented a linear space data structure of O(d log log n) query time, where d = (j - i + 1). In this paper, we present a new linear space data structure with an improved query time of O[formula].

4

The Weighted Suffix Tree: An Efficient Data Structure for Handling Molecular Weighted Sequences and its Applications

Iliopoulos C.S., Makris C., Panagis J., Perdikuri K., Theodoridis E., Tsakalidis A.

Fundamenta Informaticae

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2006

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Vol. 71, nr 2,3

259-277

EN

In this paper we introduce the Weighted Suffix Tree, an efficient data structure for computing string regularities in weighted sequences of molecular data. Molecular Weighted Sequences can model important biological processes such as the DNA Assembly Process or the DNA-Protein Binding Process. Thus pattern matching or identification of repeated patterns, in biological weighted sequences is a very important procedure in the translation of gene expression and regulation. We present time and space efficient algorithms for constructing the weighted suffix tree and some applications of the proposed data structure to problems taken from the Molecular Biology area such as pattern matching, repeats discovery, discovery of the longest common subsequence of two weighted sequences and computation of covers.