A Survey on Using Kolmogorov Complexity in Cybersecurity

Resende, João S.; Martins, Rolando; Antunes, Luís

doi:10.3390/e21121196

Cited by 7 publications

(5 citation statements)

References 74 publications

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“…The use of all training data, following the approach in [ 58 ], could further improve the results here presented. Moreover, additional efforts are required to test the suitability of our methodology in multiclass classification problems in cybersecurity (as those in [ 59 , 60 ]).…”

Section: Discussionmentioning

confidence: 99%

A Compression-Based Method for Detecting Anomalies in Textual Data

Torre-Abaitua

Lago-Fernández

Arroyo

2021

Entropy

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Nowadays, information and communications technology systems are fundamental assets of our social and economical model, and thus they should be properly protected against the malicious activity of cybercriminals. Defence mechanisms are generally articulated around tools that trace and store information in several ways, the simplest one being the generation of plain text files coined as security logs. Such log files are usually inspected, in a semi-automatic way, by security analysts to detect events that may affect system integrity, confidentiality and availability. On this basis, we propose a parameter-free method to detect security incidents from structured text regardless its nature. We use the Normalized Compression Distance to obtain a set of features that can be used by a Support Vector Machine to classify events from a heterogeneous cybersecurity environment. In particular, we explore and validate the application of our method in four different cybersecurity domains: HTTP anomaly identification, spam detection, Domain Generation Algorithms tracking and sentiment analysis. The results obtained show the validity and flexibility of our approach in different security scenarios with a low configuration burden.

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Section: Discussionmentioning

confidence: 99%

A Compression-Based Method for Detecting Anomalies in Textual Data

Torre-Abaitua

Lago-Fernández

Arroyo

2021

Entropy

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“…Besides those previously mentioned topics, other research directions to enhance cybersecurity were also paid attention. For instance, in [78] the authors conducted a survey about the use of Kolmogorov complexity in the security and privacy domains. The adoption of these technologies in the cybersecurity realm was inspired by the feature free nature, and the absence of a need to tune the parameters.…”

Section: Discussionmentioning

confidence: 99%

Artificial Intelligence in the Cyber Domain: Offense and Defense

2020

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Artificial intelligence techniques have grown rapidly in recent years, and their applications in practice can be seen in many fields, ranging from facial recognition to image analysis. In the cybersecurity domain, AI-based techniques can provide better cyber defense tools and help adversaries improve methods of attack. However, malicious actors are aware of the new prospects too and will probably attempt to use them for nefarious purposes. This survey paper aims at providing an overview of how artificial intelligence can be used in the context of cybersecurity in both offense and defense.

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“…The literature provides several examples of the computation of complexity (or entropy) profiles or equivalent approaches for many applications [ 17 , 18 ]. For example, in the area of computational security [ 19 ], these profiles have been used for mimicking antiviruses [ 20 ] and detecting malware [ 21 ]. Although there has been success in the application of other distinct areas—namely, with the classification of galaxy clusters [ 22 ] and finding characteristic fingerprints in lithium-ion cells [ 23 ]—the use of these profiles has been widely applied to biological sequence analysis [ 24–26 ].…”

Section: Introductionmentioning

confidence: 99%

AlcoR: alignment-free simulation, mapping, and visualization of low-complexity regions in biological data

Silva,

Qi,

Pinho

et al. 2022

GigaScience

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Background Low-complexity data analysis is the area that addresses the search and quantification of regions in sequences of elements that contain low-complexity or repetitive elements. For example, these can be tandem repeats, inverted repeats, homopolymer tails, GC-biased regions, similar genes, and hairpins, among many others. Identifying these regions is crucial because of their association with regulatory and structural characteristics. Moreover, their identification provides positional and quantity information where standard assembly methodologies face significant difficulties because of substantial higher depth coverage (mountains), ambiguous read mapping, or where sequencing or reconstruction defects may occur. However, the capability to distinguish low-complexity regions (LCRs) in genomic and proteomic sequences is a challenge that depends on the model’s ability to find them automatically. Low-complexity patterns can be implicit through specific or combined sources, such as algorithmic or probabilistic, and recurring to different spatial distances—namely, local, medium, or distant associations. Findings This article addresses the challenge of automatically modeling and distinguishing LCRs, providing a new method and tool (AlcoR) for efficient and accurate segmentation and visualization of these regions in genomic and proteomic sequences. The method enables the use of models with different memories, providing the ability to distinguish local from distant low-complexity patterns. The method is reference and alignment free, providing additional methodologies for testing, including a highly flexible simulation method for generating biological sequences (DNA or protein) with different complexity levels, sequence masking, and a visualization tool for automatic computation of the LCR maps into an ideogram style. We provide illustrative demonstrations using synthetic, nearly synthetic, and natural sequences showing the high efficiency and accuracy of AlcoR. As large-scale results, we use AlcoR to unprecedentedly provide a whole-chromosome low-complexity map of a recent complete human genome and the haplotype-resolved chromosome pairs of a heterozygous diploid African cassava cultivar. Conclusions The AlcoR method provides the ability of fast sequence characterization through data complexity analysis, ideally for scenarios entangling the presence of new or unknown sequences. AlcoR is implemented in C language using multithreading to increase the computational speed, is flexible for multiple applications, and does not contain external dependencies. The tool accepts any sequence in FASTA format. The source code is freely provided at https://github.com/cobilab/alcor.

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A Survey on Using Kolmogorov Complexity in Cybersecurity

Cited by 7 publications

References 74 publications

A Compression-Based Method for Detecting Anomalies in Textual Data

A Compression-Based Method for Detecting Anomalies in Textual Data

Artificial Intelligence in the Cyber Domain: Offense and Defense

AlcoR: alignment-free simulation, mapping, and visualization of low-complexity regions in biological data

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