Operational Semantics with Hierarchical Abstract Syntax Graphs*
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Network Monitoring & Management Using Cacti
Network Monitoring & Management Using Cacti Network Startup Resource Center www.nsrc.org These materials are licensed under the Creative Commons Attribution-NonCommercial 4.0 International license (http://creativecommons.org/licenses/by-nc/4.0/) Introduction Network Monitoring Tools Availability Reliability Performance Cact monitors the performance and usage of devices. Introduction Cacti: Uses RRDtool, PHP and stores data in MySQL. It supports the use of SNMP and graphics with RRDtool. “Cacti is a complete frontend to RRDTool, it stores all of the necessary information to create graphs and populate them with data in a MySQL database. The frontend is completely PHP driven. Along with being able to maintain Graphs, Data Sources, and Round Robin Archives in a database, cacti handles the data gathering. There is also SNMP support for those used to creating traffic graphs with MRTG.” Introduction • A tool to monitor, store and present network and system/server statistics • Designed around RRDTool with a special emphasis on the graphical interface • Almost all of Cacti's functionality can be configured via the Web. • You can find Cacti here: http://www.cacti.net/ Getting RRDtool • Round Robin Database for time series data storage • Command line based • From the author of MRTG • Made to be faster and more flexible • Includes CGI and Graphing tools, plus APIs • Solves the Historical Trends and Simple Interface problems as well as storage issues Find RRDtool here:http://oss.oetiker.ch/rrdtool/ RRDtool Database Format General Description 1. Cacti is written as a group of PHP scripts. 2. The key script is “poller.php”, which runs every 5 minutes (by default). -
Munin Documentation Release 2.0.44
Munin Documentation Release 2.0.44 Stig Sandbeck Mathisen <[email protected]> Dec 20, 2018 Contents 1 Munin installation 3 1.1 Prerequisites.............................................3 1.2 Installing Munin...........................................4 1.3 Initial configuration.........................................7 1.4 Getting help.............................................8 1.5 Upgrading Munin from 1.x to 2.x..................................8 2 The Munin master 9 2.1 Role..................................................9 2.2 Components.............................................9 2.3 Configuration.............................................9 2.4 Other documentation.........................................9 3 The Munin node 13 3.1 Role.................................................. 13 3.2 Configuration............................................. 13 3.3 Other documentation......................................... 13 4 The Munin plugin 15 4.1 Role.................................................. 15 4.2 Other documentation......................................... 15 5 Documenting Munin 21 5.1 Nomenclature............................................ 21 6 Reference 25 6.1 Man pages.............................................. 25 6.2 Other reference material....................................... 40 7 Examples 43 7.1 Apache virtualhost configuration.................................. 43 7.2 lighttpd configuration........................................ 44 7.3 nginx configuration.......................................... 45 7.4 Graph aggregation -
Operational Semantics Page 1
Operational Semantics Page 1 Operational Semantics Class notes for a lecture given by Mooly Sagiv Tel Aviv University 24/5/2007 By Roy Ganor and Uri Juhasz Reference Semantics with Applications, H. Nielson and F. Nielson, Chapter 2. http://www.daimi.au.dk/~bra8130/Wiley_book/wiley.html Introduction Semantics is the study of meaning of languages. Formal semantics for programming languages is the study of formalization of the practice of computer programming. A computer language consists of a (formal) syntax – describing the actual structure of programs and the semantics – which describes the meaning of programs. Many tools exist for programming languages (compilers, interpreters, verification tools, … ), the basis on which these tools can cooperate is the formal semantics of the language. Formal Semantics When designing a programming language, formal semantics gives an (hopefully) unambiguous definition of what a program written in the language should do. This definition has several uses: • People learning the language can understand the subtleties of its use • The model over which the semantics is defined (the semantic domain) can indicate what the requirements are for implementing the language (as a compiler/interpreter/…) • Global properties of any program written in the language, and any state occurring in such a program, can be understood from the formal semantics • Implementers of tools for the language (parsers, compilers, interpreters, debuggers etc) have a formal reference for their tool and a formal definition of its correctness/completeness -
Automatic Placement of Authorization Hooks in the Linux Security Modules Framework
Automatic Placement of Authorization Hooks in the Linux Security Modules Framework Vinod Ganapathy [email protected] University of Wisconsin, Madison Joint work with Trent Jaeger Somesh Jha [email protected] [email protected] Pennsylvania State University University of Wisconsin, Madison Context of this talk Authorization policies and their enforcement Three concepts: Subjects (e.g., users, processes) Objects (e.g., system resources) Security-sensitive operations on objects. Authorization policy: A set of triples: (Subject, Object, Operation) Key question: How to ensure that the authorization policy is enforced? CCS 2005 Automatic Placement of Authorization Hooks in the Linux Security Modules Framework 2 Enforcing authorization policies Reference monitor consults the policy. Application queries monitor at appropriate locations. Can I perform operation OP? Reference Monitor Application to Policy be secured Yes/No (subj.,obj.,oper.) (subj.,obj.,oper.) (subj.,obj.,oper.) (subj.,obj.,oper.) (subj.,obj.,oper.) CCS 2005 Automatic Placement of Authorization Hooks in the Linux Security Modules Framework 3 Linux security modules framework Framework for authorization policy enforcement. Uses a reference monitor-based architecture. Integrated into Linux-2.6 Linux Kernel Reference Monitor Policy (subj.,obj.,oper.) (subj.,obj.,oper.) (subj.,obj.,oper.) (subj.,obj.,oper.) (subj.,obj.,oper.) CCS 2005 Automatic Placement of Authorization Hooks in the Linux Security Modules Framework 4 Linux security modules framework Reference monitor calls (hooks) placed appropriately in the Linux kernel. Each hook is an authorization query. Linux Kernel Reference Monitor Policy (subj.,obj.,oper.) (subj.,obj.,oper.) (subj.,obj.,oper.) (subj.,obj.,oper.) (subj.,obj.,oper.) Hooks CCS 2005 Automatic Placement of Authorization Hooks in the Linux Security Modules Framework 5 Linux security modules framework Authorization query of the form: (subj., obj., oper.)? Kernel performs operation only if query succeeds. -
Masked Graph Modeling for Molecule Generation ✉ Omar Mahmood 1, Elman Mansimov2, Richard Bonneau 3 & Kyunghyun Cho 2
ARTICLE https://doi.org/10.1038/s41467-021-23415-2 OPEN Masked graph modeling for molecule generation ✉ Omar Mahmood 1, Elman Mansimov2, Richard Bonneau 3 & Kyunghyun Cho 2 De novo, in-silico design of molecules is a challenging problem with applications in drug discovery and material design. We introduce a masked graph model, which learns a dis- tribution over graphs by capturing conditional distributions over unobserved nodes (atoms) and edges (bonds) given observed ones. We train and then sample from our model by iteratively masking and replacing different parts of initialized graphs. We evaluate our 1234567890():,; approach on the QM9 and ChEMBL datasets using the GuacaMol distribution-learning benchmark. We find that validity, KL-divergence and Fréchet ChemNet Distance scores are anti-correlated with novelty, and that we can trade off between these metrics more effec- tively than existing models. On distributional metrics, our model outperforms previously proposed graph-based approaches and is competitive with SMILES-based approaches. Finally, we show our model generates molecules with desired values of specified properties while maintaining physiochemical similarity to the training distribution. 1 Center for Data Science, New York University, New York, NY, USA. 2 Department of Computer Science, Courant Institute of Mathematical Sciences, New ✉ York, NY, USA. 3 Center for Genomics and Systems Biology, New York University, New York, NY, USA. email: [email protected] NATURE COMMUNICATIONS | (2021) 12:3156 | https://doi.org/10.1038/s41467-021-23415-2 | www.nature.com/naturecommunications 1 ARTICLE NATURE COMMUNICATIONS | https://doi.org/10.1038/s41467-021-23415-2 he design of de novo molecules in-silico with desired prop- enable more informative comparisons between models that use Terties is an essential part of drug discovery and materials different molecular representations. -
Denotational Semantics
Denotational Semantics CS 6520, Spring 2006 1 Denotations So far in class, we have studied operational semantics in depth. In operational semantics, we define a language by describing the way that it behaves. In a sense, no attempt is made to attach a “meaning” to terms, outside the way that they are evaluated. For example, the symbol ’elephant doesn’t mean anything in particular within the language; it’s up to a programmer to mentally associate meaning to the symbol while defining a program useful for zookeeppers. Similarly, the definition of a sort function has no inherent meaning in the operational view outside of a particular program. Nevertheless, the programmer knows that sort manipulates lists in a useful way: it makes animals in a list easier for a zookeeper to find. In denotational semantics, we define a language by assigning a mathematical meaning to functions; i.e., we say that each expression denotes a particular mathematical object. We might say, for example, that a sort implementation denotes the mathematical sort function, which has certain properties independent of the programming language used to implement it. In other words, operational semantics defines evaluation by sourceExpression1 −→ sourceExpression2 whereas denotational semantics defines evaluation by means means sourceExpression1 → mathematicalEntity1 = mathematicalEntity2 ← sourceExpression2 One advantage of the denotational approach is that we can exploit existing theories by mapping source expressions to mathematical objects in the theory. The denotation of expressions in a language is typically defined using a structurally-recursive definition over expressions. By convention, if e is a source expression, then [[e]] means “the denotation of e”, or “the mathematical object represented by e”. -
Quantum Weakest Preconditions
Quantum Weakest Preconditions Ellie D’Hondt ∗ Prakash Panangaden †‡ Abstract We develop a notion of predicate transformer and, in particular, the weak- est precondition, appropriate for quantum computation. We show that there is a Stone-type duality between the usual state-transformer semantics and the weakest precondition semantics. Rather than trying to reduce quantum com- putation to probabilistic programming we develop a notion that is directly taken from concepts used in quantum computation. The proof that weak- est preconditions exist for completely positive maps follows from the Kraus representation theorem. As an example we give the semantics of Selinger’s language in terms of our weakest preconditions. 1 Introduction Quantum computation is a field of research that is rapidly acquiring a place as a significant topic in computer science. To be sure, there are still essential tech- nological and conceptual problems to overcome in building functional quantum computers. Nevertheless there are fundamental new insights into quantum com- putability [Deu85, DJ92], quantum algorithms [Gro96, Gro97, Sho94, Sho97] and into the nature of quantum mechanics itself [Per95], particularly with the emer- gence of quantum information theory [NC00]. These developments inspire one to consider the problems of programming full-fledged general purpose quantum computers. Much of the theoretical re- search is aimed at using the new tools available - superposition, entanglement and linearity - for algorithmic efficiency. However, the fact that quantum algorithms are currently done at a very low level only - comparable to classical computing 60 years ago - is a much less explored aspect of the field. The present paper is ∗Centrum Leo Apostel , Vrije Universiteit Brussel, Brussels, Belgium, [email protected]. -
Making Classes Provable Through Contracts, Models and Frames
View metadata, citation and similar papers at core.ac.uk brought to you by CORE provided by CiteSeerX DISS. ETH NO. 17610 Making classes provable through contracts, models and frames A dissertation submitted to the SWISS FEDERAL INSTITUTE OF TECHNOLOGY ZURICH (ETH Zurich)¨ for the degree of Doctor of Sciences presented by Bernd Schoeller Diplom-Informatiker, TU Berlin born April 5th, 1974 citizen of Federal Republic of Germany accepted on the recommendation of Prof. Dr. Bertrand Meyer, examiner Prof. Dr. Martin Odersky, co-examiner Prof. Dr. Jonathan S. Ostroff, co-examiner 2007 ABSTRACT Software correctness is a relation between code and a specification of the expected behavior of the software component. Without proper specifica- tions, correct software cannot be defined. The Design by Contract methodology is a way to tightly integrate spec- ifications into software development. It has proved to be a light-weight and at the same time powerful description technique that is accepted by software developers. In its more than 20 years of existence, it has demon- strated many uses: documentation, understanding object-oriented inheri- tance, runtime assertion checking, or fully automated testing. This thesis approaches the formal verification of contracted code. It conducts an analysis of Eiffel and how contracts are expressed in the lan- guage as it is now. It formalizes the programming language providing an operational semantics and a formal list of correctness conditions in terms of this operational semantics. It introduces the concept of axiomatic classes and provides a full library of axiomatic classes, called the mathematical model library to overcome prob- lems of contracts on unbounded data structures. -
A Denotational Semantics Approach to Functional and Logic Programming
A Denotational Semantics Approach to Functional and Logic Programming TR89-030 August, 1989 Frank S.K. Silbermann The University of North Carolina at Chapel Hill Department of Computer Science CB#3175, Sitterson Hall Chapel Hill, NC 27599-3175 UNC is an Equal OpportunityjAfflrmative Action Institution. A Denotational Semantics Approach to Functional and Logic Programming by FrankS. K. Silbermann A dissertation submitted to the faculty of the University of North Carolina at Chapel Hill in par tial fulfillment of the requirements for the degree of Doctor of Philosophy in Computer Science. Chapel Hill 1989 @1989 Frank S. K. Silbermann ALL RIGHTS RESERVED 11 FRANK STEVEN KENT SILBERMANN. A Denotational Semantics Approach to Functional and Logic Programming (Under the direction of Bharat Jayaraman.) ABSTRACT This dissertation addresses the problem of incorporating into lazy higher-order functional programming the relational programming capability of Horn logic. The language design is based on set abstraction, a feature whose denotational semantics has until now not been rigorously defined. A novel approach is taken in constructing an operational semantics directly from the denotational description. The main results of this dissertation are: (i) Relative set abstraction can combine lazy higher-order functional program ming with not only first-order Horn logic, but also with a useful subset of higher order Horn logic. Sets, as well as functions, can be treated as first-class objects. (ii) Angelic powerdomains provide the semantic foundation for relative set ab straction. (iii) The computation rule appropriate for this language is a modified parallel outermost, rather than the more familiar left-most rule. (iv) Optimizations incorporating ideas from narrowing and resolution greatly improve the efficiency of the interpreter, while maintaining correctness. -
Denotational Semantics
Denotational Semantics 8–12 lectures for Part II CST 2010/11 Marcelo Fiore Course web page: http://www.cl.cam.ac.uk/teaching/1011/DenotSem/ 1 Lecture 1 Introduction 2 What is this course about? • General area. Formal methods: Mathematical techniques for the specification, development, and verification of software and hardware systems. • Specific area. Formal semantics: Mathematical theories for ascribing meanings to computer languages. 3 Why do we care? • Rigour. specification of programming languages . justification of program transformations • Insight. generalisations of notions computability . higher-order functions ... data structures 4 • Feedback into language design. continuations . monads • Reasoning principles. Scott induction . Logical relations . Co-induction 5 Styles of formal semantics Operational. Meanings for program phrases defined in terms of the steps of computation they can take during program execution. Axiomatic. Meanings for program phrases defined indirectly via the ax- ioms and rules of some logic of program properties. Denotational. Concerned with giving mathematical models of programming languages. Meanings for program phrases defined abstractly as elements of some suitable mathematical structure. 6 Basic idea of denotational semantics [[−]] Syntax −→ Semantics Recursive program → Partial recursive function Boolean circuit → Boolean function P → [[P ]] Concerns: • Abstract models (i.e. implementation/machine independent). Lectures 2, 3 and 4. • Compositionality. Lectures 5 and 6. • Relationship to computation (e.g. operational semantics). Lectures 7 and 8. 7 Characteristic features of a denotational semantics • Each phrase (= part of a program), P , is given a denotation, [[P ]] — a mathematical object representing the contribution of P to the meaning of any complete program in which it occurs. • The denotation of a phrase is determined just by the denotations of its subphrases (one says that the semantics is compositional). -
A Property Graph Is a Directed Edge-Labeled, Attributed Multigraph
- In IT Security for 10+ years - consulting and research - My first talk at a Data-conference :) - PhD thesis: “Pattern-based Vulnerability Discovery” on using unsupervised machine learning and graph databases for vulnerability discovery - Chief scientist at ShiftLeft Inc. - Modern development processes are optimized for fast feature delivery - Cloud services: multiple deployments a day - Each deployment is a security-related decision - Enforce code-level security policies automatically! Example: enforce a policy for propagation of sensitive data: “Environment variables must not be written to the logger” public void init() { String user = env.getProperty(“sfdc.uname”); String pwd = env.getProperty(“sfdc.pwd”); ... if (!validLogin(user, pwd)) { ... log.error("Invalid username/password: {}/{}", user, pwd); } } Jackson - Critical code execution vulnerabilities. A program is vulnerable if 1. a vulnerable version of jackson-databind is used 2. attacker controlled input is deserialized in a call to “readValue” 3. “Default typing” is enabled globally via a call to “enableDefaultTyping” Jackson Jackson Detecting this particular vulnerability types requires modeling program dependencies, syntax, data flow and method invocations. > cpg.dependency.name(".*jackson-databind.*").version("2.8..*") .and{ cpg.call.name(“.*enableDefaultTyping.*”) } .and{ cpg.method.name(“.*readValue.*”).parameter.index(1).reachableBy( cpg.parameter.evalType(".*HttpServletRequest.*")) }.location Code Property Graph cpg.method.name(“sink”).parameter .reachableBy(cpg.method.name(“source”)) -
Probabilistic Predicate Transformers
Probabilistic Predicate Transformers CARROLL MORGAN, ANNABELLE McIVER, and KAREN SEIDEL Oxford University Probabilistic predicates generalize standard predicates over a state space; with probabilistic pred- icate transformers one thus reasons about imperative programs in terms of probabilistic pre- and postconditions. Probabilistic healthiness conditions generalize the standard ones, characterizing “real” probabilistic programs, and are based on a connection with an underlying relational model for probabilistic execution; in both contexts demonic nondeterminism coexists with probabilis- tic choice. With the healthiness conditions, the associated weakest-precondition calculus seems suitable for exploring the rigorous derivation of small probabilistic programs. Categories and Subject Descriptors: D.2.4 [Program Verification]: Correctness Proofs; D.3.1 [Programming Languages]: Formal Definitions and Theory; F.1.2 [Modes of Computation]: Probabilistic Computation; F.3.1 [Semantics of Programming Languages]: Specifying and Ver- ifying and Reasoning about Programs; G.3 [Probability and Statistics]: Probabilistic Algorithms General Terms: Verification, Theory, Algorithms, Languages Additional Key Words and Phrases: Galois connection, nondeterminism, predicate transformers, probability, program derivation, refinement, weakest preconditions 1. MOTIVATION AND INTRODUCTION The weakest-precondition approach of Dijkstra [1976] has been reasonably success- ful for rigorous derivation of small imperative programs, even (and especially) those involving demonic