PSON: a Serialization Format for Iot Sensor Networks
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Deserialization Vulnerability by Abdelazim Mohammed(@Intx0x80)
Deserialization vulnerability By Abdelazim Mohammed(@intx0x80) Thanks to: Mazin Ahmed (@mazen160) Asim Jaweesh(@Jaw33sh) 1 | P a g e Table of Contents Serialization (marshaling): ............................................................................................................................ 4 Deserialization (unmarshaling): .................................................................................................................... 4 Programming language support serialization: ............................................................................................... 4 Risk for using serialization: .......................................................................................................................... 5 Serialization in Java ...................................................................................................................................... 6 Deserialization vulnerability in Java: ............................................................................................................ 6 Code flow work........................................................................................................................................... 11 Vulnerability Detection: .............................................................................................................................. 12 CVE: ........................................................................................................................................................... 17 Tools: ......................................................................................................................................................... -
Document Databases, JSON, Mongodb 17
MI-PDB, MIE-PDB: Advanced Database Systems http://www.ksi.mff.cuni.cz/~svoboda/courses/2015-2-MIE-PDB/ Lecture 13: Document Databases, JSON, MongoDB 17. 5. 2016 Lecturer: Martin Svoboda [email protected] Authors: Irena Holubová, Martin Svoboda Faculty of Mathematics and Physics, Charles University in Prague Course NDBI040: Big Data Management and NoSQL Databases Document Databases Basic Characteristics Documents are the main concept Stored and retrieved XML, JSON, … Documents are Self-describing Hierarchical tree data structures Can consist of maps, collections, scalar values, nested documents, … Documents in a collection are expected to be similar Their schema can differ Document databases store documents in the value part of the key-value store Key-value stores where the value is examinable Document Databases Suitable Use Cases Event Logging Many different applications want to log events Type of data being captured keeps changing Events can be sharded by the name of the application or type of event Content Management Systems, Blogging Platforms Managing user comments, user registrations, profiles, web-facing documents, … Web Analytics or Real-Time Analytics Parts of the document can be updated New metrics can be easily added without schema changes E-Commerce Applications Flexible schema for products and orders Evolving data models without expensive data migration Document Databases When Not to Use Complex Transactions Spanning Different Operations Atomic cross-document operations Some document databases do support (e.g., RavenDB) Queries against Varying Aggregate Structure Design of aggregate is constantly changing → we need to save the aggregates at the lowest level of granularity i.e., to normalize the data Document Databases Representatives Lotus Notes Storage Facility JSON JavaScript Object Notation Introduction • JSON = JavaScript Object Notation . -
Smart Grid Serialization Comparison
Downloaded from orbit.dtu.dk on: Sep 28, 2021 Smart Grid Serialization Comparison Petersen, Bo Søborg; Bindner, Henrik W.; You, Shi; Poulsen, Bjarne Published in: Computing Conference 2017 Link to article, DOI: 10.1109/SAI.2017.8252264 Publication date: 2017 Document Version Peer reviewed version Link back to DTU Orbit Citation (APA): Petersen, B. S., Bindner, H. W., You, S., & Poulsen, B. (2017). Smart Grid Serialization Comparison. In Computing Conference 2017 (pp. 1339-1346). IEEE. https://doi.org/10.1109/SAI.2017.8252264 General rights Copyright and moral rights for the publications made accessible in the public portal are retained by the authors and/or other copyright owners and it is a condition of accessing publications that users recognise and abide by the legal requirements associated with these rights. Users may download and print one copy of any publication from the public portal for the purpose of private study or research. You may not further distribute the material or use it for any profit-making activity or commercial gain You may freely distribute the URL identifying the publication in the public portal If you believe that this document breaches copyright please contact us providing details, and we will remove access to the work immediately and investigate your claim. Computing Conference 2017 18-20 July 2017 | London, UK Smart Grid Serialization Comparision Comparision of serialization for distributed control in the context of the Internet of Things Bo Petersen, Henrik Bindner, Shi You Bjarne Poulsen DTU Electrical Engineering DTU Compute Technical University of Denmark Technical University of Denmark Lyngby, Denmark Lyngby, Denmark [email protected], [email protected], [email protected] [email protected] Abstract—Communication between DERs and System to ensure that the control messages are received within a given Operators is required to provide Demand Response and solve timeframe, depending on the needs of the power grid. -
Spindle Documentation Release 2.0.0
spindle Documentation Release 2.0.0 Jorge Ortiz, Jason Liszka June 08, 2016 Contents 1 Thrift 3 1.1 Data model................................................3 1.2 Interface definition language (IDL)...................................4 1.3 Serialization formats...........................................4 2 Records 5 2.1 Creating a record.............................................5 2.2 Reading/writing records.........................................6 2.3 Record interface methods........................................6 2.4 Other methods..............................................7 2.5 Mutable trait...............................................7 2.6 Raw class.................................................7 2.7 Priming..................................................7 2.8 Proxies..................................................8 2.9 Reflection.................................................8 2.10 Field descriptors.............................................8 3 Custom types 9 3.1 Enhanced types..............................................9 3.2 Bitfields..................................................9 3.3 Type-safe IDs............................................... 10 4 Enums 13 4.1 Enum value methods........................................... 13 4.2 Companion object methods....................................... 13 4.3 Matching and unknown values...................................... 14 4.4 Serializing to string............................................ 14 4.5 Examples................................................. 14 5 Working -
Protolite: Highly Optimized Protocol Buffer Serializers
Package ‘protolite’ July 28, 2021 Type Package Title Highly Optimized Protocol Buffer Serializers Author Jeroen Ooms Maintainer Jeroen Ooms <[email protected]> Description Pure C++ implementations for reading and writing several common data formats based on Google protocol-buffers. Currently supports 'rexp.proto' for serialized R objects, 'geobuf.proto' for binary geojson, and 'mvt.proto' for vector tiles. This package uses the auto-generated C++ code by protobuf-compiler, hence the entire serialization is optimized at compile time. The 'RProtoBuf' package on the other hand uses the protobuf runtime library to provide a general- purpose toolkit for reading and writing arbitrary protocol-buffer data in R. Version 2.1.1 License MIT + file LICENSE URL https://github.com/jeroen/protolite BugReports https://github.com/jeroen/protolite/issues SystemRequirements libprotobuf and protobuf-compiler LinkingTo Rcpp Imports Rcpp (>= 0.12.12), jsonlite Suggests spelling, curl, testthat, RProtoBuf, sf RoxygenNote 6.1.99.9001 Encoding UTF-8 Language en-US NeedsCompilation yes Repository CRAN Date/Publication 2021-07-28 12:20:02 UTC R topics documented: geobuf . .2 mapbox . .2 serialize_pb . .3 1 2 mapbox Index 5 geobuf Geobuf Description The geobuf format is an optimized binary format for storing geojson data with protocol buffers. These functions are compatible with the geobuf2json and json2geobuf utilities from the geobuf npm package. Usage read_geobuf(x, as_data_frame = TRUE) geobuf2json(x, pretty = FALSE) json2geobuf(json, decimals = 6) Arguments x file path or raw vector with the serialized geobuf.proto message as_data_frame simplify geojson data into data frames pretty indent json, see jsonlite::toJSON json a text string with geojson data decimals how many decimals (digits behind the dot) to store for numbers mapbox Mapbox Vector Tiles Description Read Mapbox vector-tile (mvt) files and returns the list of layers. -
Tencentdb for Tcaplusdb Getting Started
TencentDB for TcaplusDB TencentDB for TcaplusDB Getting Started Product Documentation ©2013-2019 Tencent Cloud. All rights reserved. Page 1 of 32 TencentDB for TcaplusDB Copyright Notice ©2013-2019 Tencent Cloud. All rights reserved. Copyright in this document is exclusively owned by Tencent Cloud. You must not reproduce, modify, copy or distribute in any way, in whole or in part, the contents of this document without Tencent Cloud's the prior written consent. Trademark Notice All trademarks associated with Tencent Cloud and its services are owned by Tencent Cloud Computing (Beijing) Company Limited and its affiliated companies. Trademarks of third parties referred to in this document are owned by their respective proprietors. Service Statement This document is intended to provide users with general information about Tencent Cloud's products and services only and does not form part of Tencent Cloud's terms and conditions. Tencent Cloud's products or services are subject to change. Specific products and services and the standards applicable to them are exclusively provided for in Tencent Cloud's applicable terms and conditions. ©2013-2019 Tencent Cloud. All rights reserved. Page 2 of 32 TencentDB for TcaplusDB Contents Getting Started Basic Concepts Cluster Table Group Table Index Data Types Read/Write Capacity Mode Table Definition in ProtoBuf Table Definition in TDR Creating Cluster Creating Table Group Creating Table Getting Access Point Information Access TcaplusDB ©2013-2019 Tencent Cloud. All rights reserved. Page 3 of 32 TencentDB for TcaplusDB Getting Started Basic Concepts Cluster Last updated:2020-07-31 11:15:59 Cluster Overview A cluster is the basic TcaplusDB management unit, which provides independent TcaplusDB service for the business. -
Rdf Repository Replacing Relational Database
RDF REPOSITORY REPLACING RELATIONAL DATABASE 1B.Srinivasa Rao, 2Dr.G.Appa Rao 1,2Department of CSE, GITAM University Email:[email protected],[email protected] Abstract-- This study is to propose a flexible enable it. One such technology is RDF (Resource information storage mechanism based on the Description Framework)[2]. RDF is a directed, principles of Semantic Web that enables labelled graph for representing information in the information to be searched rather than queried. Web. This can be perceived as a repository In this study, a prototype is developed where without any predefined structure the focus is on the information rather than the The information stored in the traditional structure. Here information is stored in a RDBMS’s requires structure to be defined structure that is constructed on the fly. Entities upfront. On the contrary, information could be in the system are connected and form a graph, very complex to structure upfront despite the similar to the web of data in the Internet. This tremendous potential offered by the existing data is persisted in a peculiar way to optimize database systems. In the ever changing world, querying on this graph of data. All information another important characteristic of information relating to a subject is persisted closely so that in a system that impacts its structure is the reqeusting any information of a subject could be modification/enhancement to the system. This is handled in one call. Also, the information is a big concern with many software systems that maintained in triples so that the entire exist today and there is no tidy approach to deal relationship from subject to object via the with the problem. -
Software License Agreement (EULA)
Third-party Computer Software AutoVu™ ALPR cameras • angular-animate (https://docs.angularjs.org/api/ngAnimate) licensed under the terms of the MIT License (https://github.com/angular/angular.js/blob/master/LICENSE). © 2010-2016 Google, Inc. http://angularjs.org • angular-base64 (https://github.com/ninjatronic/angular-base64) licensed under the terms of the MIT License (https://github.com/ninjatronic/angular-base64/blob/master/LICENSE). © 2010 Nick Galbreath © 2013 Pete Martin • angular-translate (https://github.com/angular-translate/angular-translate) licensed under the terms of the MIT License (https://github.com/angular-translate/angular-translate/blob/master/LICENSE). © 2014 [email protected] • angular-translate-handler-log (https://github.com/angular-translate/bower-angular-translate-handler-log) licensed under the terms of the MIT License (https://github.com/angular-translate/angular-translate/blob/master/LICENSE). © 2014 [email protected] • angular-translate-loader-static-files (https://github.com/angular-translate/bower-angular-translate-loader-static-files) licensed under the terms of the MIT License (https://github.com/angular-translate/angular-translate/blob/master/LICENSE). © 2014 [email protected] • Angular Google Maps (http://angular-ui.github.io/angular-google-maps/#!/) licensed under the terms of the MIT License (https://opensource.org/licenses/MIT). © 2013-2016 angular-google-maps • AngularJS (http://angularjs.org/) licensed under the terms of the MIT License (https://github.com/angular/angular.js/blob/master/LICENSE). © 2010-2016 Google, Inc. http://angularjs.org • AngularUI Bootstrap (http://angular-ui.github.io/bootstrap/) licensed under the terms of the MIT License (https://github.com/angular- ui/bootstrap/blob/master/LICENSE). -
Serializing C Intermediate Representations for Efficient And
Serializing C intermediate representations for efficient and portable parsing Jeffrey A. Meister1, Jeffrey S. Foster2,∗, and Michael Hicks2 1 Department of Computer Science and Engineering, University of California, San Diego, CA 92093, USA 2 Department of Computer Science, University of Maryland, College Park, MD 20742, USA SUMMARY C static analysis tools often use intermediate representations (IRs) that organize program data in a simple, well-structured manner. However, the C parsers that create IRs are slow, and because they are difficult to write, only a few implementations exist, limiting the languages in which a C static analysis can be written. To solve these problems, we investigate two language-independent, on-disk representations of C IRs: one using XML, and the other using an Internet standard binary encoding called XDR. We benchmark the parsing speeds of both options, finding the XML to be about a factor of two slower than parsing C and the XDR over six times faster. Furthermore, we show that the XML files are far too large at 19 times the size of C source code, while XDR is only 2.2 times the C size. We also demonstrate the portability of our XDR system by presenting a C source code querying tool in Ruby. Our solution and the insights we gained from building it will be useful to analysis authors and other clients of C IRs. We have made our software freely available for download at http://www.cs.umd.edu/projects/PL/scil/. key words: C, static analysis, intermediate representations, parsing, XML, XDR 1. Introduction There is significant interest in writing static analysis tools for C programs. -
Towards a Fully Automated Extraction and Interpretation of Tabular Data Using Machine Learning
UPTEC F 19050 Examensarbete 30 hp August 2019 Towards a fully automated extraction and interpretation of tabular data using machine learning Per Hedbrant Per Hedbrant Master Thesis in Engineering Physics Department of Engineering Sciences Uppsala University Sweden Abstract Towards a fully automated extraction and interpretation of tabular data using machine learning Per Hedbrant Teknisk- naturvetenskaplig fakultet UTH-enheten Motivation A challenge for researchers at CBCS is the ability to efficiently manage the Besöksadress: different data formats that frequently are changed. Significant amount of time is Ångströmlaboratoriet Lägerhyddsvägen 1 spent on manual pre-processing, converting from one format to another. There are Hus 4, Plan 0 currently no solutions that uses pattern recognition to locate and automatically recognise data structures in a spreadsheet. Postadress: Box 536 751 21 Uppsala Problem Definition The desired solution is to build a self-learning Software as-a-Service (SaaS) for Telefon: automated recognition and loading of data stored in arbitrary formats. The aim of 018 – 471 30 03 this study is three-folded: A) Investigate if unsupervised machine learning Telefax: methods can be used to label different types of cells in spreadsheets. B) 018 – 471 30 00 Investigate if a hypothesis-generating algorithm can be used to label different types of cells in spreadsheets. C) Advise on choices of architecture and Hemsida: technologies for the SaaS solution. http://www.teknat.uu.se/student Method A pre-processing framework is built that can read and pre-process any type of spreadsheet into a feature matrix. Different datasets are read and clustered. An investigation on the usefulness of reducing the dimensionality is also done. -
CBOR (RFC 7049) Concise Binary Object Representation
CBOR (RFC 7049) Concise Binary Object Representation Carsten Bormann, 2015-11-01 1 CBOR: Agenda • What is it, and when might I want it? • How does it work? • How do I work with it? 2 CBOR: Agenda • What is it, and when might I want it? • How does it work? • How do I work with it? 3 Slide stolen from Douglas Crockford History of Data Formats • Ad Hoc • Database Model • Document Model • Programming Language Model Box notation TLV 5 XML XSD 6 Slide stolen from Douglas Crockford JSON • JavaScript Object Notation • Minimal • Textual • Subset of JavaScript Values • Strings • Numbers • Booleans • Objects • Arrays • null Array ["Sunday", "Monday", "Tuesday", "Wednesday", "Thursday", "Friday", "Saturday"] [ [0, -1, 0], [1, 0, 0], [0, 0, 1] ] Object { "name": "Jack B. Nimble", "at large": true, "grade": "A", "format": { "type": "rect", "width": 1920, "height": 1080, "interlace": false, "framerate": 24 } } Object Map { "name": "Jack B. Nimble", "at large": true, "grade": "A", "format": { "type": "rect", "width": 1920, "height": 1080, "interlace": false, "framerate": 24 } } JSON limitations • No binary data (byte strings) • Numbers are in decimal, some parsing required • Format requires copying: • Escaping for strings • Base64 for binary • No extensibility (e.g., date format?) • Interoperability issues • I-JSON further reduces functionality (RFC 7493) 12 BSON and friends • Lots of “binary JSON” proposals • Often optimized for data at rest, not protocol use (BSON ➔ MongoDB) • Most are more complex than JSON 13 Why a new binary object format? • Different design goals from current formats – stated up front in the document • Extremely small code size – for work on constrained node networks • Reasonably compact data size – but no compression or even bit-fiddling • Useful to any protocol or application that likes the design goals 14 Concise Binary Object Representation (CBOR) 15 “Sea Boar” “Sea Boar” 16 Design goals (1 of 2) 1. -
Msgpack Documentation Release 1.0
msgpack Documentation Release 1.0 Author 2021-03-18 Contents 1 API reference 3 2 Advanced usage 9 Python Module Index 11 Index 13 i ii msgpack Documentation, Release 1.0 MessagePack is a efficient format for inter language data exchange. Contents 1 msgpack Documentation, Release 1.0 2 Contents CHAPTER 1 API reference msgpack.pack(o, stream, **kwargs) Pack object o and write it to stream See Packer for options. dump() is alias for pack() msgpack.packb(o, **kwargs) Pack object o and return packed bytes See Packer for options. dumps() is alias for packb() msgpack.unpack(stream, **kwargs) Unpack an object from stream. Raises ExtraData when stream contains extra bytes. See Unpacker for options. load() is alias for unpack() msgpack.unpackb(packed, *, object_hook=None, list_hook=None, bool use_list=True, bool raw=False, int timestamp=0, bool strict_map_key=True, unicode_errors=None, object_pairs_hook=None, ext_hook=ExtType, Py_ssize_t max_str_len=-1, Py_ssize_t max_bin_len=-1, Py_ssize_t max_array_len=-1, Py_ssize_t max_map_len=-1, Py_ssize_t max_ext_len=-1) Unpack packed_bytes to object. Returns an unpacked object. Raises ExtraData when packed contains extra bytes. Raises ValueError when packed is incomplete. Raises FormatError when packed is not valid msgpack. Raises StackError when packed contains too nested. Other exceptions can be raised during unpacking. See Unpacker for options. max_xxx_len options are configured automatically from len(packed). loads() is alias for unpackb() 3 msgpack Documentation, Release 1.0 class msgpack.Packer(default=None, *, bool use_single_float=False, bool autoreset=True, bool use_bin_type=True, bool strict_types=False, bool datetime=False, uni- code_errors=None) MessagePack Packer Usage: packer= Packer() astream.write(packer.pack(a)) astream.write(packer.pack(b)) Packer’s constructor has some keyword arguments: Parameters • default (callable) – Convert user type to builtin type that Packer supports.