The Need to Support of Data Flow Graph Visualization of Forensic Lucid Programs, Forensic Evidence, and their Evaluation by GIPSY

Lucid programs are data-flow programs and can be visually represented as data flow graphs (DFGs) and composed visually. Forensic Lucid, a Lucid dialect, is a language to specify and reason about cyberforensic cases. It includes the encoding of the evidence (representing the context of evaluation) and the crime scene modeling in order to validate claims against the model and perform event reconstruction, potentially within large swaths of digital evidence. To aid investigators to model the scene and evaluate it, instead of typing a Forensic Lucid program, we propose to expand the design and implementation of the Lucid DFG programming onto Forensic Lucid case modeling and specification to enhance the usability of the language and the system and its behavior. We briefly discuss the related work on visual programming an DFG modeling in an attempt to define and select one approach or a composition of approaches for Forensic Lucid based on various criteria such as previous implementation, wide use, formal backing in terms of semantics and translation. In the end, we solicit the readers' constructive, opinions, feedback, comments, and recommendations within the context of this short discussion.Comment: 11 pages, 7 figures, index; extended abstract presented at VizSec'10 at http://www.vizsec2010.org/posters ; short paper accepted at PST'1

Reasoning About a Simulated Printer Case Investigation with Forensic Lucid

In this work we model the ACME (a fictitious company name) "printer case incident" and make its specification in Forensic Lucid, a Lucid- and intensional-logic-based programming language for cyberforensic analysis and event reconstruction specification. The printer case involves a dispute between two parties that was previously solved using the finite-state automata (FSA) approach, and is now re-done in a more usable way in Forensic Lucid. Our simulation is based on the said case modeling by encoding concepts like evidence and the related witness accounts as an evidential statement context in a Forensic Lucid program, which is an input to the transition function that models the possible deductions in the case. We then invoke the transition function (actually its reverse) with the evidential statement context to see if the evidence we encoded agrees with one's claims and then attempt to reconstruct the sequence of events that may explain the claim or disprove it.Comment: 18 pages, 3 figures, 7 listings, TOC, index; this article closely relates to arXiv:0906.0049 and arXiv:0904.3789 but to remain stand-alone repeats some of the background and introductory content; abstract presented at HSC'09 and the full updated paper at ICDF2C'11. This is an updated/edited version after ICDF2C proceedings with more references and correction

MARFL: An Intensional Language for Demand-Driven Management of Machine Learning Backends

Artificial Intelligence (AI) is a rapidly evolving field that has transformed numerous industries and one of its key applications, Pattern Recognition, has been instrumental to the success of Large Language Models like ChatGPT, Bard, etc. However, scripting these advanced systems can be complex and challenging for some users. In this research, we propose a simpler scripting language to perform complex pattern recognition tasks. We introduce a new intensional programming language, MARFL, which is an extension of the Lucid family supported by General Intensional Programming System (GIPSY). Our solution focuses on providing syntax and semantics for MARFL, which enables scripting of Modular A* Recognition Framework (MARF)-based applications as context aware, where the notion of context represents fine-grained configuration details of a given MARF instance. We adapt the concept of context to provide an easily comprehensible language that can perform complex pattern recognition tasks on a demand-driven system such as GIPSY. Our solution is also generic enough to handle other machine learning backends such as PyTorch or TensorFlow in the future. We also provide a complete implementation of our approach, including a new compiler component and MARFL-specific execution engines within GIPSY. Our work extends the use of intensional programming to modeling and executing scripted pattern recognition tasks, which can be used for implementing different algorithmic specifications. Additionally, we utilize the demand-driven distributed computing capabilities of GIPSY to enable an efficient and scalable execution

Encoding Forensic Multimedia Evidence from MARF Applications as Forensic Lucid Expressions

In this work we summarize biometric evidence as well as file type evidence extraction “exported” as formal Forensic Lucid language expression in the form of higher-order intensional contexts for further case analysis by a system that interprets Forensic Lucid expressions for claim verification and event reconstruction. The digital evidence is exported from the Modular Audio Recognition Framework (MARF)’s applications runs on a set of data comprising biometric voice recordings for speaker, gender, spoken accent, etc. as well as more general file type analysis using signal and pattern recognition processing techniques. The focus is in translation aspect of the extracted evidence into formal Forensic Lucid expressions for further analysis

Intensional Cyberforensics

This work focuses on the application of intensional logic to cyberforensic analysis and its benefits and difficulties are compared with the finite-state-automata approach. This work extends the use of the intensional programming paradigm to the modeling and implementation of a cyberforensics investigation process with backtracing of event reconstruction, in which evidence is modeled by multidimensional hierarchical contexts, and proofs or disproofs of claims are undertaken in an eductive manner of evaluation. This approach is a practical, context-aware improvement over the finite state automata (FSA) approach we have seen in previous work. As a base implementation language model, we use in this approach a new dialect of the Lucid programming language, called Forensic Lucid, and we focus on defining hierarchical contexts based on intensional logic for the distributed evaluation of cyberforensic expressions. We also augment the work with credibility factors surrounding digital evidence and witness accounts, which have not been previously modeled. The Forensic Lucid programming language, used for this intensional cyberforensic analysis, formally presented through its syntax and operational semantics. In large part, the language is based on its predecessor and codecessor Lucid dialects, such as GIPL, Indexical Lucid, Lucx, Objective Lucid, and JOOIP bound by the underlying intensional programming paradigm.Comment: 412 pages, 94 figures, 18 tables, 19 algorithms and listings; PhD thesis; v2 corrects some typos and refs; also available on Spectrum at http://spectrum.library.concordia.ca/977460

Using the General Intensional Programming System (GIPSY) for Evaluation of Higher-Order Intensional Logic (HOIL) Expressions

The General Intensional Programming System (GIPSY) has been built around the Lucid family of intensional programming languages that rely on the higher-order intensional logic (HOIL) to provide context-oriented multidimensional reasoning of intensional expressions. HOIL combines functional programming with various intensional logics to allow explicit context expressions to be evaluated as first-class values that can be passed as parameters to functions and return as results with an appropriate set of operators defined on contexts. GIPSY's frameworks are implemented in Java as a collection of replaceable components for the compilers of various Lucid dialects and the demand-driven eductive evaluation engine that can run distributively. GIPSY provides support for hybrid programming models that couple intensional and imperative languages for a variety of needs. Explicit context expressions limit the scope of evaluation of math expressions (effectively a Lucid program is a mathematics or physics expression constrained by the context) in tensor physics, regular math in multiple dimensions, etc., and for cyberforensic reasoning as one of the use-cases of interest. Thus, GIPSY is a support testbed for HOIL-based languages some of which enable such reasoning, as in formal cyberforensic case analysis with event reconstruction. In this paper we discuss the GIPSY architecture, its evaluation engine and example use-cases.Comment: 14 pages; 8 figure

Intensional Cyberforensics

This work focuses on the application of intensional logic to cyberforensic analysis and its benefits and difficulties are compared with the finite-state-automata approach. This work extends the use of the intensional programming paradigm to the modeling and implementation of a cyberforensics investigation process with backtracing of event reconstruction, in which evidence is modeled by multidimensional hierarchical contexts, and proofs or disproofs of claims are undertaken in an eductive manner of evaluation. This approach is a practical, context-aware improvement over the finite state automata (FSA) approach we have seen in previous work. As a base implementation language model, we use in this approach a new dialect of the Lucid programming language, called Forensic Lucid, and we focus on defining hierarchical contexts based on intensional logic for the distributed evaluation of cyberforensic expressions. We also augment the work with credibility factors surrounding digital evidence and witness accounts, which have not been previously modeled. The Forensic Lucid programming language, used for this intensional cyberforensic analysis, formally presented through its syntax and operational semantics. In large part, the language is based on its predecessor and codecessor Lucid dialects, such as GIPL, Indexical Lucid, Lucx, Objective Lucid, MARFL, and JOOIP bound by the underlying intensional programming paradigm

Formally Specifying and Proving Operational Aspects of Forensic Lucid in Isabelle

A Forensic Lucid intensional programming language has been proposed for intensional cyberforensic analysis. In large part, the language is based on various predecessor and codecessor Lucid dialects bound by the higher-order intensional logic (HOIL) that is behind them. This work formally specifies the operational aspects of the Forensic Lucid language and compiles a theory of its constructs using Isabelle, a proof assistant system.Comment: 23 pages, 3 listings, 3 figures, 1 table, 1 Appendix with theorems, pp. 76--98. TPHOLs 2008 Emerging Trends Proceedings, August 18-21, Montreal, Canada. Editors: Otmane Ait Mohamed and Cesar Munoz and Sofiene Tahar. The individual paper's PDF is at http://users.encs.concordia.ca/~tphols08/TPHOLs2008/ET/76-98.pd