A Space-Bounded Anytime Algorithm for the Multiple Longest Common Subsequence Problem

Attacks on operating system access control have become a significant and increasingly common problem. This type of security threat is recorded in a forensic artifact such as an authentication log. Forensic investigators will generally examine the log to analyze such incidents. An anomaly is highly correlated to an attacker's attempts to compromise the system. In this paper, we propose a novel method to automatically detect an anomaly in the access control log of an operating system. The logs will be first preprocessed and then clustered using an improved MajorClust algorithm to get a better cluster. This technique provides parameter-free clustering so that it automatically can produce an analysis report for the forensic investigators. The clustering results will be checked for anomalies based on a score that considers some factors such as the total members in a cluster, the frequency of the events in the log file, and the inter-arrival time of a specific activity. We also provide a graph-based visualization of logs to assist the investigators with easy analysis. Experimental results compiled on an open dataset of a Linux authentication log show that the proposed method achieved the accuracy of 83.14% in the authentication log dataset.

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“…185 posed in the recent literature [21,22]. For each cluster c, we will check for the most common substring occurring in the raw log.…”

Section: Sequence (Mlcs) Technique Solutions Of This Classical Problmentioning

confidence: 99%

Graph clustering and anomaly detection of access control log for forensic purposes

Studiawan

Payne

Sohel

2017

Digital Investigation

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“…The longest common subsequence (LCS) problem is a classic computer science problem and still attracts continuous attention [1][2][3][4]. It is the basis of data comparison programs and widely used by revision control systems for reconciling multiple changes made to a revision-controlled collection of files.…”

Section: Introductionmentioning

confidence: 99%

An Efficient Algorithm for LCS Problem between Two Arbitrary Sequences

2018

Mathematical Problems in Engineering

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The longest common subsequence (LCS) problem is a classic computer science problem. For the essential problem of computing LCS between two arbitrary sequences 1 and 2, this paper proposes an algorithm taking ( + ) space and ( + 2 ) time, where is the total number of elements in the set {( , )| 1[ ] = 2[ ]}. The algorithm can be more efficient than relevant classical algorithms in specific ranges of .

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“…More recently, Yang et al [15] used the observation on monotonically increasing values in the LCS table to identify the “corner points”, where the values on the diagonals change from one row to the next. The corners define a more sparse 2D grid, based on which they determine the LCS .…”

Section: Introductionmentioning

confidence: 99%

A new algorithm for “the LCS problem” with application in compressing genome resequencing data

et al. 2016

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BackgroundThe longest common subsequence (LCS) problem is a classical problem in computer science, and forms the basis of the current best-performing reference-based compression schemes for genome resequencing data.MethodsFirst, we present a new algorithm for the LCS problem. Using the generalized suffix tree, we identify the common substrings shared between the two input sequences. Using the maximal common substrings, we construct a directed acyclic graph (DAG), based on which we determine the LCS as the longest path in the DAG. Then, we introduce an LCS-motivated reference-based compression scheme using the components of the LCS, rather than the LCS itself.ResultsOur basic scheme compressed the Homo sapiens genome (with an original size of 3,080,436,051 bytes) to 15,460,478 bytes. An improvement on the basic method further reduced this to 8,556,708 bytes, or an overall compression ratio of 360. This can be compared to the previous state-of-the-art compression ratios of 157 (Wang and Zhang, 2011) and 171 (Pinho, Pratas, and Garcia, 2011).ConclusionWe propose a new algorithm to address the longest common subsequence problem. Motivated by our LCS algorithm, we introduce a new reference-based compression scheme for genome resequencing data. Comparative results against state-of-the-art reference-based compression algorithms demonstrate the performance of the proposed method.

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A Space-Bounded Anytime Algorithm for the Multiple Longest Common Subsequence Problem

Cited by 27 publications

References 29 publications

Graph clustering and anomaly detection of access control log for forensic purposes

Graph clustering and anomaly detection of access control log for forensic purposes

An Efficient Algorithm for LCS Problem between Two Arbitrary Sequences

A new algorithm for “the LCS problem” with application in compressing genome resequencing data

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