{"data":{"id":"10.6084/m9.figshare.5907055","type":"dois","attributes":{"doi":"10.6084/m9.figshare.5907055","prefix":"10.6084","suffix":"m9.figshare.5907055","identifiers":[],"alternateIdentifiers":[],"creators":[{"name":"Češka, Milan","nameType":"Personal","givenName":"Milan","familyName":"Češka","affiliation":[],"nameIdentifiers":[]},{"name":"Vojtěch Havlena","affiliation":[],"nameIdentifiers":[]},{"name":"Lukáš Holík","affiliation":[],"nameIdentifiers":[]},{"name":"Ondřej Lengál","affiliation":[],"nameIdentifiers":[]},{"name":"Tomáš Vojnar","affiliation":[],"nameIdentifiers":[]}],"titles":[{"title":"APPREAL (APProximate REduction of Automata and Languages) tool used to obtain experimental results in TACAS '18 paper: Approximate Reduction of Finite Automata for High-Speed Network Intrusion Detection"}],"publisher":"figshare","container":{},"publicationYear":2018,"subjects":[{"subject":"Applied Computer Science"},{"subject":"Theoretical Computer Science"},{"subject":"80303 Computer System Security","subjectScheme":"FOR"},{"subject":"FOS: Computer and information sciences","schemeUri":"http://www.oecd.org/science/inno/38235147.pdf","subjectScheme":"Fields of Science and Technology (FOS)"},{"subject":"FOS: Computer and information sciences","subjectScheme":"Fields of Science and Technology (FOS)"},{"subject":"100503 Computer Communications Networks","subjectScheme":"FOR"},{"subject":"FOS: Electrical engineering, electronic engineering, information engineering","schemeUri":"http://www.oecd.org/science/inno/38235147.pdf","subjectScheme":"Fields of Science and Technology (FOS)"},{"subject":"FOS: Electrical engineering, electronic engineering, information engineering","subjectScheme":"Fields of Science and Technology (FOS)"}],"contributors":[],"dates":[{"date":"2018-02-20","dateType":"Created"},{"date":"2018-04-13","dateType":"Updated"},{"date":"2018","dateType":"Issued"}],"language":null,"types":{"ris":"DATA","bibtex":"misc","citeproc":"dataset","schemaOrg":"Dataset","resourceType":"Dataset","resourceTypeGeneral":"Dataset"},"relatedIdentifiers":[{"relationType":"IsSupplementTo","relatedIdentifier":"10.1007/978-3-319-89963-3_9","relatedIdentifierType":"DOI"}],"relatedItems":[],"sizes":["65417954 Bytes"],"formats":[],"version":null,"rightsList":[{"rights":"GPL 2.0+","rightsUri":"https://www.gnu.org/licenses/gpl-2.0.html"}],"descriptions":[{"description":"This is an artifact containing the tool APPREAL (APProximate REduction of Automata and Languages) used to obtain experimental results in the following paper accepted for publication at TACAS'18:\u003cbr\u003eM. Ceska, V. Havlena, L. Holik, O. Lengal, and T. Vojnar. \u003ci\u003eApproximate Reduction of Finite Automata for High-Speed Network Intrusion Detection.\u003c/i\u003e\u003cbr\u003eThe approach outlines in the related TACAS'18 paper is implemented in a Python prototype in this repository. Other file type include openly accessible C-specific \u003cb\u003e.c\u003c/b\u003e and\u003cb\u003e .h \u003c/b\u003eformats, .html, Perl (.pcre) and openly-accessible text file formats.\u003cbr\u003eThe subdirectory \u003cb\u003e.src \u003c/b\u003econtains source code relating to the reduction of NFAs (nondeterministic finite automata) obtained from PCREs (Perl compatible regular expressions) that occur in [Snort](https://www.snort.org) rules:\u003cbr\u003e* `experiments/` - the setting of our experiments* `netbench/` - the Netbench tool that we use to transform PCREs into NFAs* `preproc/` - a bunch of small programs used for pre-processing network traffic PCAP files* `reduce/` - the tool performing the reduction itself* `regexps/` - regular expressions that we have collected\u003cbr\u003eThe subdirectory \u003cb\u003e.packages \u003c/b\u003econtains required software packages and a shell file with bash script (install.sh) to install these packages,\u003cbr\u003e\u003cb\u003eVirtual Machine\u003c/b\u003e\u003cbr\u003eThe artifact is prepared to run on the TACAS'18 artifact evaluation virtual machine available here: https://doi.org/10.6084/m9.figshare.5896615.v1\u003cbr\u003e2048 MiB of memory should be sufficient to reproduce the results from Tables 1a and 1b in the related proceedings paper, for reproducing the experiments from Tables 2 and 3, it is recommended to set the memory to 8192 MiB or more.\u003cbr\u003eDetailed instructions on how to reproduce results from the TACAS'18 paper are available in \u003cb\u003eREADME.txt\u003c/b\u003e\u003cb\u003e\u003cbr\u003e\u003c/b\u003e\u003cb\u003eBackground\u003c/b\u003e\u003cb\u003e\u003cbr\u003e\u003c/b\u003eThe related TACAS'18 paper considers the problem of approximate reduction of non-deterministic automata that appear in hardware-accelerated network intrusion detection systems (NIDSes). We define an error distance of a reduced automaton from the original one as the probability of packets being incorrectly classified by the reduced automaton (wrt the probabilistic distribution of packets in the network traffic). We use this notion to design an approximate reduction procedure that achieves a great size reduction (much beyond the state-of-the-art language preserving techniques) with a controlled and small error. We have implemented our approach and evaluated it on use cases from SNORT, a popular NIDS. Our results provide experimental evidence that the method can be highly efficient in practice, allowing NIDSes to follow the rapid growth in the speed of networks.\u003cbr\u003e","descriptionType":"Abstract"}],"geoLocations":[],"fundingReferences":[],"xml":"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