Hash Functions Integrity Checks
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Shell Scripting with Bash
Introduction to Shell Scripting with Bash Charles Jahnke Research Computing Services Information Services & Technology Topics for Today ● Introductions ● Basic Terminology ● How to get help ● Command-line vs. Scripting ● Variables ● Handling Arguments ● Standard I/O, Pipes, and Redirection ● Control Structures (loops and If statements) ● SCC Job Submission Example Research Computing Services Research Computing Services (RCS) A group within Information Services & Technology at Boston University provides computing, storage, and visualization resources and services to support research that has specialized or highly intensive computation, storage, bandwidth, or graphics requirements. Three Primary Services: ● Research Computation ● Research Visualization ● Research Consulting and Training Breadth of Research on the Shared Computing Cluster (SCC) Me ● Research Facilitator and Administrator ● Background in biomedical engineering, bioinformatics, and IT systems ● Offices on both CRC and BUMC ○ Most of our staff on the Charles River Campus, some dedicated to BUMC ● Contact: [email protected] You ● Who has experience programming? ● Using Linux? ● Using the Shared Computing Cluster (SCC)? Basic Terminology The Command-line The line on which commands are typed and passed to the shell. Username Hostname Current Directory [username@scc1 ~]$ Prompt Command Line (input) The Shell ● The interface between the user and the operating system ● Program that interprets and executes input ● Provides: ○ Built-in commands ○ Programming control structures ○ Environment -
Powerview Command Reference
PowerView Command Reference TRACE32 Online Help TRACE32 Directory TRACE32 Index TRACE32 Documents ...................................................................................................................... PowerView User Interface ............................................................................................................ PowerView Command Reference .............................................................................................1 History ...................................................................................................................................... 12 ABORT ...................................................................................................................................... 13 ABORT Abort driver program 13 AREA ........................................................................................................................................ 14 AREA Message windows 14 AREA.CLEAR Clear area 15 AREA.CLOSE Close output file 15 AREA.Create Create or modify message area 16 AREA.Delete Delete message area 17 AREA.List Display a detailed list off all message areas 18 AREA.OPEN Open output file 20 AREA.PIPE Redirect area to stdout 21 AREA.RESet Reset areas 21 AREA.SAVE Save AREA window contents to file 21 AREA.Select Select area 22 AREA.STDERR Redirect area to stderr 23 AREA.STDOUT Redirect area to stdout 23 AREA.view Display message area in AREA window 24 AutoSTOre .............................................................................................................................. -
Hash Functions and Thetitle NIST of Shapresentation-3 Competition
The First 30 Years of Cryptographic Hash Functions and theTitle NIST of SHAPresentation-3 Competition Bart Preneel COSIC/Kath. Univ. Leuven (Belgium) Session ID: CRYP-202 Session Classification: Hash functions decoded Insert presenter logo here on slide master Hash functions X.509 Annex D RIPEMD-160 MDC-2 SHA-256 SHA-3 MD2, MD4, MD5 SHA-512 SHA-1 This is an input to a crypto- graphic hash function. The input is a very long string, that is reduced by the hash function to a string of fixed length. There are 1A3FD4128A198FB3CA345932 additional security conditions: it h should be very hard to find an input hashing to a given value (a preimage) or to find two colliding inputs (a collision). Hash function history 101 DES RSA 1980 single block ad hoc length schemes HARDWARE MD2 MD4 1990 SNEFRU double MD5 block security SHA-1 length reduction for RIPEMD-160 factoring, SHA-2 2000 AES permu- DLOG, lattices Whirlpool SOFTWARE tations SHA-3 2010 Applications • digital signatures • data authentication • protection of passwords • confirmation of knowledge/commitment • micropayments • pseudo-random string generation/key derivation • construction of MAC algorithms, stream ciphers, block ciphers,… Agenda Definitions Iterations (modes) Compression functions SHA-{0,1,2,3} Bits and bytes 5 Hash function flavors cryptographic hash function this talk MAC MDC OWHF CRHF UOWHF (TCR) Security requirements (n-bit result) preimage 2nd preimage collision ? x ? ? ? h h h h h h(x) h(x) = h(x‘) h(x) = h(x‘) 2n 2n 2n/2 Informal definitions (1) • no secret parameters -
An Empirical Comparison of Widely Adopted Hash Functions in Digital Forensics: Does the Programming Language and Operating System Make a Difference?
2015 Annual ADFSL Conference on Digital Forensics, Security and Law Proceedings May 19th, 11:45 AM An Empirical Comparison of Widely Adopted Hash Functions in Digital Forensics: Does the Programming Language and Operating System Make a Difference? Satyendra Gurjar Cyber Forensics Research and Education Group (UNHcFREG), Tagliatela College of Engineering, ECECS Department, University of New Haven, [email protected] Ibrahim Baggili Cyber Forensics Research and Education Group (UNHcFREG), Tagliatela College of Engineering, ECECS Department, University of New Haven Frank Breitinger CyberFollow F thisorensics and additional Research worksand Education at: https:/ Gr/commons.eroup (UNHcFREG),au.edu/adfsl Tagliatela College of Engineering, ECECS Depar Partment,t of the Univ Aviationersity ofSaf Newety and Hav Securityen Commons, Computer Law Commons, Defense and Security AliceStudies Fischer Commons , Forensic Science and Technology Commons, Information Security Commons, CyberNational For Securityensics Resear Law Commonsch and Education, OS and GrNetworksoup (UNHcFREG), Commons T, Otheragliatela Computer College Sciences of Engineering, Commons ECECS, and theDepar Socialtment, Contr Univol,ersity Law , ofCrime, New andHav enDe,viance AFischer@newha Commons ven.edu Scholarly Commons Citation Gurjar, Satyendra; Baggili, Ibrahim; Breitinger, Frank; and Fischer, Alice, "An Empirical Comparison of Widely Adopted Hash Functions in Digital Forensics: Does the Programming Language and Operating System Make a Difference?" (2015). Annual ADFSL Conference on Digital Forensics, Security and Law. 6. https://commons.erau.edu/adfsl/2015/tuesday/6 This Peer Reviewed Paper is brought to you for free and open access by the Conferences at Scholarly Commons. It has been accepted for inclusion in Annual ADFSL Conference on Digital Forensics, Security and Law by an (c)ADFSL authorized administrator of Scholarly Commons. -
Hash Functions
11 Hash Functions Suppose you share a huge le with a friend, but you are not sure whether you both have the same version of the le. You could send your version of the le to your friend and they could compare to their version. Is there any way to check that involves less communication than this? Let’s call your version of the le x (a string) and your friend’s version y. The goal is to determine whether x = y. A natural approach is to agree on some deterministic function H, compute H¹xº, and send it to your friend. Your friend can compute H¹yº and, since H is deterministic, compare the result to your H¹xº. In order for this method to be fool-proof, we need H to have the property that dierent inputs always map to dierent outputs — in other words, H must be injective (1-to-1). Unfortunately, if H is injective and H : f0; 1gin ! f0; 1gout is injective, then out > in. This means that sending H¹xº is no better/shorter than sending x itself! Let us call a pair ¹x;yº a collision in H if x , y and H¹xº = H¹yº. An injective function has no collisions. One common theme in cryptography is that you don’t always need something to be impossible; it’s often enough for that thing to be just highly unlikely. Instead of saying that H should have no collisions, what if we just say that collisions should be hard (for polynomial-time algorithms) to nd? An H with this property will probably be good enough for anything we care about. -
Lecture 17 the Shell and Shell Scripting Simple Shell Scripts
Lecture 17 The Shell and Shell Scripting In this lecture • The UNIX shell • Simple Shell Scripts • Shell variables • File System commands, IO commands, IO redirection • Command Line Arguments • Evaluating Expr in Shell • Predicates, operators for testing strings, ints and files • If-then-else in Shell • The for, while and do loop in Shell • Writing Shell scripts • Exercises In this course, we need to be familiar with the "UNIX shell". We use it, whether bash, csh, tcsh, zsh, or other variants, to start and stop processes, control the terminal, and to otherwise interact with the system. Many of you have heard of, or made use of "shell scripting", that is the process of providing instructions to shell in a simple, interpreted programming language . To see what shell we are working on, first SSH into unix.andrew.cmu.edu and type echo $SHELL ---- to see the working shell in SSH We will be writing our shell scripts for this particular shell (csh). The shell scripting language does not fit the classic definition of a useful language. It does not have many of the features such as portability, facilities for resource intensive tasks such as recursion or hashing or sorting. It does not have data structures like arrays and hash tables. It does not have facilities for direct access to hardware or good security features. But in many other ways the language of the shell is very powerful -- it has functions, conditionals, loops. It does not support strong data typing -- it is completely untyped (everything is a string). But, the real power of shell program doesn't come from the language itself, but from the diverse library that it can call upon -- any program. -
CMSC 426/626 - Computer Security Fall 2014
Authentication and Passwords CMSC 426/626 - Computer Security Fall 2014 Outline • Types of authentication • Vulnerabilities of password authentication • Linux password authentication • Windows Password authentication • Cracking techniques Goals of Authentication • Identification - provide a claimed identity to the system. • Verification - establish validity of the provided identity. We’re not talking about message authentication, e.g. the use of digital signatures. Means of Authentication • Something you know, e.g. password • Something you have, e.g. USB dongle or Common Access Card (CAC) • Something you are, e.g. fingerprint • Something you do, e.g. hand writing Password-Based Authentication • User provides identity and password; system verifies that the password is correct for the given identity. • Identity determines access and privileges. • Identity can be used for Discretionary Access Control, e.g. to give another user access to a file. Password Hashing Password • System stores hash of the user password, not the plain text password. Hash Algorithm • Commonly used technique, e.g. UNIX password hashing. Password Hash Password Vulnerabilities Assume the authentication system stores hashed passwords. There are eight attack strategies. • Off-line Dictionary Attack - get hold of the password file, test a collection (dictionary) of possible passwords. ‣ Most systems protect the password file, but attackers sometimes get hold of one. • Specific Account Attack - given a specific user account, try popular passwords. ‣ Most systems use lockout mechanisms to make these attacks difficult. • Popular Password Attack - given a popular password, try it on multiple accounts. ‣ Harder to defend against - have to look for patterns in failed access attempts. • Targeted Password Guessing - use what you know about a user to intelligently guess their password. -
Jellyfish Count -M 21 -S 100M -T 10 -C Reads.Fasta
Jellysh 2 User Guide December 13, 2013 Contents 1 Getting started 2 1.1 Counting all k-mers . .2 1.1.1 Counting k-mers in sequencing reads . .3 1.1.2 Counting k-mers in a genome . .3 1.2 Counting high-frequency k-mers . .3 1.2.1 One pass method . .3 1.2.2 Two pass method . .4 2 FAQ 5 2.1 How to read compressed les (or other format)?newmacroname . .5 2.2 How to read multiple les at once? . .5 2.3 How to reduce the output size? . .6 3 Subcommands 7 3.1 histo .............................................7 3.2 dump .............................................7 3.3 query .............................................7 3.4 info ..............................................8 3.5 merge ............................................8 3.6 cite . .8 1 Chapter 1 Getting started 1.1 Counting all k-mers The basic command to count all k-mers is as follows: jellyfish count -m 21 -s 100M -t 10 -C reads.fasta This will count canonical (-C) 21-mers (-m 21), using a hash with 100 million elements (-s 100 M) and 10 threads (-t 10) in the sequences in the le reads.fasta. The output is written in the le 'mer_counts.jf' by default (change with -o switch). To compute the histogram of the k-mer occurrences, use the histo subcommand (see section 3.1): jellyfish histo mer_counts.jf To query the counts of a particular k-mer, use the query subcommand (see section 3.3): jellyfish query mer_counts.jf AACGTTG To output all the counts for all the k-mers in the le, use the dump subcommand (see section 3.2): jellyfish dump mer_counts.jf > mer_counts_dumps.fa To get some information on how, when and where this jellysh le was generated, use the info subcommand (see section 3.4): jellyfish info mer_counts.jf For more detail information, see the relevant sections in this document. -
The First Cryptographic Hash Workshop
Workshop Report The First Cryptographic Hash Workshop Gaithersburg, MD Oct. 31-Nov. 1, 2005 Report prepared by Shu-jen Chang and Morris Dworkin Information Technology Laboratory, National Institute of Standards and Technology, Gaithersburg, MD 20899 Available online: http://csrc.nist.gov/groups/ST/hash/documents/HashWshop_2005_Report.pdf 1. Introduction On Oct. 31-Nov. 1, 2005, 180 members of the global cryptographic community gathered in Gaithersburg, MD to attend the first Cryptographic Hash Workshop. The workshop was organized in response to a recent attack on the NIST-approved Secure Hash Algorithm SHA-1. The purpose of the workshop was to discuss this attack, assess the status of other NIST-approved hash algorithms, and discuss possible near-and long-term options. 2. Workshop Program The workshop program consisted of two days of presentations of papers that were submitted to the workshop and panel discussion sessions that NIST organized. The main topics for the discussions included the SHA-1 and SHA-2 hash functions, future research of hash functions, and NIST’s strategy. The program is available at http://csrc.nist.gov/groups/ST/hash/first_workshop.html. This report only briefly summarizes the presentations, because the above web site for the program includes links to the speakers' slides and papers. The main ideas of the discussion sessions, however, are described in considerable detail; in fact, the panelists and attendees are often paraphrased very closely, to minimize misinterpretation. Their statements are not necessarily presented in the order that they occurred, in order to organize them according to NIST's questions for each session. 3. -
Husky: Towards a More Efficient and Expressive Distributed Computing Framework
Husky: Towards a More Efficient and Expressive Distributed Computing Framework Fan Yang Jinfeng Li James Cheng Department of Computer Science and Engineering The Chinese University of Hong Kong ffyang,jfli,[email protected] ABSTRACT tends Spark, and Gelly [3] extends Flink, to expose to programmers Finding efficient, expressive and yet intuitive programming models fine-grained control over the access patterns on vertices and their for data-parallel computing system is an important and open prob- communication patterns. lem. Systems like Hadoop and Spark have been widely adopted Over-simplified functional or declarative programming interfaces for massive data processing, as coarse-grained primitives like map with only coarse-grained primitives also limit the flexibility in de- and reduce are succinct and easy to master. However, sometimes signing efficient distributed algorithms. For instance, there is re- over-simplified API hinders programmers from more fine-grained cent interest in machine learning algorithms that compute by fre- control and designing more efficient algorithms. Developers may quently and asynchronously accessing and mutating global states have to resort to sophisticated domain-specific languages (DSLs), (e.g., some entries in a large global key-value table) in a fine-grained or even low-level layers like MPI, but this raises development cost— manner [14, 19, 24, 30]. It is not clear how to program such algo- learning many mutually exclusive systems prolongs the develop- rithms using only synchronous and coarse-grained operators (e.g., ment schedule, and the use of low-level tools may result in bug- map, reduce, and join), and even with immutable data abstraction prone programming. -
Daniel Lyons [email protected] Why?
How to Hurt Your Friends with (loop) Daniel Lyons [email protected] Why? Fast Powerful Widely-used Like format you can probably do more with it than you realize. Even though you hate it, you may have to read it in someone else’s code. On my machine, 578 of 1068 library files contain at least one loop form (54.12%). It’s fast as hell. loop as List Comprehension Python: [ f(x) for x in L if p(x) ] Haskell: [ f(x) | x <- L, p(x) ] Erlang: [ f(X) || X <- L, p(X) ]. Loop: (loop for x in L when (p x) collect (f x)) Things You Can Do With loop Variable initialization and stepping Value accumulation Conditional and unconditional execution Pre- and post-loop operations Termination whenever and however you like This is taken from the list of kinds of loop clauses from CLtL Ch. 26 - variable initialization and stepping - value accumulation - termination conditions - unconditional execution - conditional execution - miscellaneous operations Initialization for/as with repeat for and as mean the same thing with is a single ‘let’ statement repeat runs the loop a specified number of times for Is Really Complicated for var from expr to expr for var in expr for var on expr Of course from can also be downfrom or upfrom, and to can also be downto, upto, below or above for var in expr: Loops over every item in the list Optionally by some stepping function for var on expr loops over the cdrs of a list, giving you each successive sublist Loops over each sublist of the list, e.g. -
Linux Command Line: Aliases, Prompts and Scripting
Linux Command Line: Aliases, Prompts and Scri... 1 Linux Command Line: Aliases, Prompts and Scripting By Steven Gordon on Wed, 23/07/2014 - 7:46am Here are a few notes on using the Bash shell that I have used as a demo to students. It covers man pages, aliases, shell prompts, paths and basics of shell scripting. For details, see the many free manuals of Bash shell scripting. 1. Man Pages Man pages are the reference manuals for commands. Reading the man pages is best when you know the command to use, but cannot remember the syntax or options. Simply type man cmd and then read the manual. E.g.: student@netlab01:~$ man ls If you don't know which command to use to perform some task, then there is a basic search feature called apropos which will do a keyword search through an index of man page names and short descriptions. You can run it using either the command apropos or using man -k. E.g.: student@netlab01:~$ man superuser No manual entry for superuser student@netlab01:~$ apropos superuser su (1) - change user ID or become superuser student@netlab01:~$ man -k "new user" newusers (8) - update and create new users in batch useradd (8) - create a new user or update default new user information Some common used commands are actually not standalone program, but commands built-in to the shell. For example, below demonstrates the creation of aliases using alias. But there is no man page for alias. Instead, alias is described as part of the bash shell man page.