Log parser lizard

(stats on pcaps), tcpflow (deconstruct),tcpcopy (resend data), chaosreader.pl (dump http conversations), CapTipper (emulate web svr of what's in pcap),Xplico (extract data per protocol), networkminer, scapy (BYO packets), editcap (convert formats)Services: FakeDNS, Nginx, fakeMail, Honeyd, INetSimReporting: etherape(graphical network monitor), WiresharkMITM: Burp Suite Proxy (manipulate http between web and client), ettercap (content filtering on fly), sslsniff (MITM attacker),mitmproxy, sslcaudit (test ssl), sslstrip, sslsplitHoneypots: Thug (docker based honeypot), Dionaea (honeypot), Kippo (SSH), Modern Honeypot NetworkRecon: p0f(passive remote fingerprinting), netwox(swiss-army knife of routines), passivednsExtracting protected files from Windowsftk imager (predefined protected entries)ntfscopyWindows Event Log analysisWindows only toolsEvent Log ExplorerLog Parser Lizard - gui for MS LogparserMandiant Highlighter - parse through big text filesLinux friendlyevtx_dumppython-evtx (WilliB) Can recover from corrupted evtxlibevtx (Metz)evtwalk/evtx_view (tzworks-commercial)Registry Viewing and ParsingExploring the registryregfdump - Dump registry CLIRegistry Explorer (Windows)MiTeC Windows Registry Recovery (Windows)Yaru (tzworks-commercial)Regshot (Windows)All-in-one registry parsercafae (tzworks-commercial)RECmd (Windows)RegRipper (WINE friendly)Prefetch analysisPECmd (Windows)pf (tzworks-commercial)MiTec Windows File Analyzer (also view lnk, shortcut, index.dat, recyclebin)Amcache parsingamcache.pyAmcacheParser (Windows)LNK parsingLECmd (Windows)lp (tzworks-commercial)MiTec Windows File AnalyzerJumplist parsingJLECmd (Windows)jmp (tzworks-commercial)Shellbags parsingsbags (tzworks-commercial)Shellbags Explorer (Windows)AppCompatCache / ShimcacheAppCompatCacheParser (Zimmerman)ShimCacheParser (Mandian)wacu (tzworks-commercial) use -all_controlsets optionshims (tzworks-commercial)**NTFS MFT, Journal, INDX **ntfsdir (NTFS Directory Enum, tzworks-commercial))ntfswalk (NTFS Metadata Extractor, tzworks-commercial))AnalyzeMFTINDXParse.pywisp (INDX Slack Parser, tzworks-commercial)jp (Journal Parser, tzworks-commercial)**Scheduled Task (AT) job parserjobparser.py**RecentFileCache reader (24hrs)rfc.plKeyword Extraction and DecodingDeobfuscate: floss, unXOR, XORStrings, ex_pe_xor, XORSearch, brxor.py, xortool, NoMoreXOR, XORBruteForcer, BalbuzardExtract strings: floss, string sifter(rank_strings), pestr, strings.exe (wine)Examine Document FilesPDF: pdfid, pdf-parser (Didier Stevens), AnalyzePDF, Pdfobjflow, peepdf, Origami, PDF X-RAY,Continued: PDFtk, swf_mastah, qpdf, pdfresurrectOffice Macro newgen: mraptor, olevbaoleid: analyses OLE files to detect specific characteristics usually

Chuyển đến nội dung chính Trình duyệt này không còn được hỗ trợ nữa. Hãy nâng cấp lên Microsoft Edge để tận dụng các tính năng mới nhất, bản cập nhật bảo mật và hỗ trợ kỹ thuật. IIS Logs and Log Parser Studio Reports Bài viết01/26/2023 Trong bài viết này -->Applies to: Exchange Server 2013Analyzing Log Parser Studio ReportsLog Parser Studio is a utility that allows you to search through and create reports from several types of log files, including those for Internet Information Services (IIS). It builds on top of Log Parser 2.2 and has a full user interface for easy creation and management of related SQL queries.Download Log Parser Studio and then review Introducing: Log Parser Studio.Remember that in Exchange 2013, all traffic has to go through IIS. This means analyzing IIS logs is the best way to get a complete picture of the number of connections that are hitting a server, of protocol-specific information about the connections, and of the users who are most impacting performance. Over twenty new reports have been developed for Log Parser Studio, for the purpose of troubleshooting Exchange 2013 performance issues.Log Parser Studio Reporting for Exchange 2013 Performance IssuesTo gain a comprehensive understanding of overall load in your Exchange 2013 environment, use the following reporting and compare the numbers against each server.The Log Parser Studio download .zip file contains the Log Parser Studio reports listed here, and additional troubleshooting-related reports.IIS: Requests Per Hour. Feed in IIS logs from either the Default Web Site (W3SVC1 directory) or the Backend Website (W3SVC2 directory), but not both at the same time.ACTIVESYNC_WP: Clients by percent. Calculates all ActiveSync requests broken down by user-agent and percentage of each client to the total number of requests.ACTIVESYNC_WP: Requests per hour (CSV). Lists the ActiveSync requests per hour and sends the results to a CSV file.ACTIVESYNC_WP: Requests per user (CSV). Lists ActiveSync requests per user and sends the results to a CSV file.ACTIVESYNC_WP: Requests per user (Top 10k). Lists ActiveSync requests per user for the top 10,000 users.ACTIVESYNC_WP: Top Talkers (CSV). Lists the top ActiveSync clients from highest to lowest request count and sends the result to a CSV file.EWS_WP: Clients by percent. Calculates all EWS requests broken down by user-agent and percentage of each client to the total number of requests.EWS_WP: Requests per hour (CSV). Lists the total number of EWS requests per hour.EWS_WP: Requests per user (CSV). Lists EWS requests per user and sends the results to a CSV file.EWS_WP: Requests per user (Top 10k). Lists EWS requests per user for the top 10,000 users.EWS_WP: Top Talkers (CSV). Lists the top EWS clients from highest to lowest request count.OLA_WP: Errors, per user, per hour, per day. Outlook

Identify the resulting log entries. The outer element must be an object.In the example above, the outer element is "entries". Using this, we can define our conversion pattern as:{ entries: [ { "firstName":"%S{First Name}", "lastName":"%S{Last Name}", "employeeId":"%S{Employee Id}", "dateJoined":"%d" } ]}If the outer object containing our log entries was a child of another object, it would not be necessary define the additional outer object. LogViewPlus will always traverse an object hierarchy automatically. Log entry identifiers only need to exist at the log entry root.Compact Log Event Format (CLEF)LogViewPlus is a great tool for viewing CLEF log entries, for example messages created by Serilog. CLEF stands for Compact Log Event Format and it is a method of producing log entries where the log message data is extracted and stored as separate fields withing the log entry. This helps ensure the log entries are machine readable.You can view CLEF log messages in LogViewPlus by adding a parser hint when processing the JSON message. For example, consider the following CLEF log entry:{ "@t": "2020-04-25T04:03:29.3546056Z", "@mt": "Connection id '{Id}' accepted.", "Properties": { "Id": "0HMA0H" }}This log entry can be parsed using the JSON parser and the pattern:{ "@t": "%d", "@mt": "%m{-parserhint:CLEF}" }Notice that the pattern configuration contains a parser hint which is including using the special parameter -parserhint:CLEF. This instruction tells the parser that the log entry message may be formatted to include data from within the JSON message. The included data may be found either at the root level or (more commonly) within a 'Properties' child element.Parsing our sample log entry using the CLEF parser hint will produce a log entry message that is easier to read. In our example, this message would be:Connection id '0HMA0H' accepted.Notice how the connection ID has been extracted from the property data and inserted into the log entry message.

IN= OUT=em1 SRC=192.168.1.23 DST=192.168.1.20 LEN=84 TOS=0x00 PREC=0x00 TTL=64 ID=0 DF PROTO=ICMP TYPE=8 CODE=0 ID=59228 SEQ=2Aug 4 13:23:00 centos kernel: IPTables-Dropped: IN=em1 OUT= MAC=a2:be:d2:ab:11:af:e2:f2:00:00 SRC=192.168.2.115 DST=192.168.1.23 LEN=52 TOS=0x00 PREC=0x00 TTL=127 ID=9434 DF PROTO=TCP SPT=58428 DPT=443 WINDOW=8192 RES=0x00 SYN URGP=0Further parsersThere are several other parsers in syslog-ng. The XML parser can parse XML formatted log messages, typically used by Windows applications. There is a dedicated parser for Linux Audit logs. There are many non-standard date formats. The date parser can help in this case, which can be configured using templates. It saves the date to the sender date.SCL: syslog-ng configuration libraryAs mentioned earlier, the syslog-ng configuration library has many parsers. These are implemented in configuration, combining several of the existing parsers.The Apache parser can parse Apache access logs. It builds on the CSV parser, but also combines it with the date parser and rewrites part of the results to be more human readable.The sudo parser can extract information from sudo log messages, so it is easy to alert on log messages if something nasty happens.Log messages from Cisco devices are similar to syslog messages, however, quite often they cannot be parsed by syslog parsers, as they do not comply with specifications. The Cisco parser of syslog-ng can parse many kinds of Cisco log messages. Of course, not all Cisco log messages, only those for which we received log samples.Python parserThe Python parser was first released in syslog-ng 3.10. It can parse complex data formats, where simply combining various built-in parsers is not enough. It can also be used to enrich log messages from external data sources, like SQL, DNS or whois.The main drawback of the Python parser is speed and resource usage. C is a lot more efficient. However, for the vast majority of users, this is not a bottleneck. Python also has the advantage that it does not need compilation or a dedicated development environment. For these reasons, the Python scripts are also easier to spread among users than native C.Application adapters, Enterprise wide message modelAs mentioned earlier, the syslog-ng configuration library contains a number of parsers. These are also called Application Adapters. There is a growing list of parsers. Using these you can easily parse log messages automatically, without any additional configuration. This is possible, because Application Adapters are enabled for the system() source since syslog-ng version 3.13.The Enterprise wide message model (EWMM) allows forwarding name-value pairs between syslog-ng instances. It is made possible by using JSON formatting. It can also forward the original raw message. It is important, as by default, syslog-ng does not send the original message, but what it can reconstruct form it using templates. The original, often broken, formatting is lost. However, some log analytics software expects to receive the broken message format instead of the standards compliant one.ExampleYou might have seen this example configuration a few times before if you followed my tutorial series. This is a good example for Application Adapters. You do not see any parser declarations in the configuration, but you can

This is the tenth part of my syslog-ng tutorial. Last time, we learned about syslog-ng filters. Today, we learn about message parsing using syslog-ng.You can watch the video or read the text below.Parsing createsname-value pairs from log messages using parsers. It is probably the most interesting but also the most complex part of syslog-ng.By default, syslog-ng tries to parse all incoming log messages as if they were formatted according to the RFC 3164 or old/BSD syslog specification. This creates a number of macros, including MESSAGE, which contains the actual log message. You can then use other parsers to further parse the content of the MESSAGE macro. It does not stop here: you can parse the content of the resulting macros as well. This way, you can create complex parser chains that extract useful information from log messages.When we learned about sources, I mentioned the no-parse flag. This way, RFC 3164 parsing is disabled, and you can parse the whole message. This is useful for a JSON or CSV formatted log message.Why is message parsing important? There are two main use cases. Having log messages available as name-value pairs allows a lot more precise filtering. For example, you can create alerts within syslog-ng for a specific username in login messages. It is also possible to save/forward only relevant data from a longer log message, saving significant amount of storage and/or network traffic.PatternDB parserThe PatternDB message parser can extract information from unstructured messages into name-value pairs. Not just that, as it can also add status fields to log messages based on message text and do message classification, like LogCheck.Of course, syslog-ng does not know what is inside the log messages by itself. It needs an XML database describing log messages. There are some sample XML databases available online, but mostly you are on your own creating these databases for your logs. For example, in case of an SSH login failure can be described as:Parsed: app=sshd, user=root, source_ip=192.168.123.45Added: action=login, status=failureClassified as “violation”JSON parserThe JSON parser turns JSON-based log messages into name-value pairs. Yes, JSON is a structured log format. However, all incoming log messages are treated by syslog-ng as plain text. You have to instruct syslog-ng to use a parser and turn the message into name-value pairs.CSV parserThe CSV parser can parse columnar data into name-value pairs. A typical example is the Apache access log file, even if the fields are not separated by commas. In this example, you can see that each column has a name. Later, one of the resulting name-value pairs, the name of the authenticated user, is used in a file name.parser p_apache { csv-parser(columns("APACHE.CLIENT_IP", "APACHE.IDENT_NAME", "APACHE.USER_NAME", "APACHE.TIMESTAMP", "APACHE.REQUEST_URL", "APACHE.REQUEST_STATUS", "APACHE.CONTENT_LENGTH", "APACHE.REFERER", "APACHE.USER_AGENT", "APACHE.PROCESS_TIME", "APACHE.SERVER_NAME") flags(escape-double-char,strip-whitespace) delimiters(" ") quote-pairs('""[]') );};destination d_file { file("/var/log/messages-${APACHE.USER_NAME:-nouser}"); };log { source(s_local); parser(p_apache); destination(d_file);};Key=value parserThe key=value parser can find key=value pairs in log messages. It was introduced in syslog-ng 3.7. This format is typical for firewall logs, but also used by many other applications. Here are some example log messages:Aug 4 13:22:40 centos kernel: IPTables-Dropped:

JSON ParserNew to LogViewPlus? Find out more about how you can use LogViewPlus to help you analyze JSON log files.LogViewPlus has a built in JSON parser which is capable of analyzing your JSON log files. It does this by parsing your JSON file according to a template. A template is a sample JSON log entry that has certain fields identified with Conversion Specifiers.LogViewPlus will not parse an entire log file as JSON. Rather, it will parse the log file line by line while checking the input structure. Only when the structure represents a complete JSON object will a parse be attempted. This approach allows for monitoring JSON log files in tail mode, but may cause issues if your JSON log is a single block of text without new lines.Because each log entry is parsed separately, our template will only need to match a single log entry. For example, let's look at a simple JSON log enry:{ "firstName":"John", "lastName":"Doe", "employeeId":"12345", "other":"ignore me", "dateJoined":"2014-05-16 10:50:14,125"} This is a JSON log entry with five fields: firstName, lastName, employeedId, other, and dateJoined. What we need to do is replace the field data with a Conversion Specifier that identifies the field data type. This might give us the following mapping.JSON FieldConversion SpecifierLogViewPlus ColumnfirstName%S{First Name}First NamelastName%S{Last Name}Last NameemployeeId%s{Employee Id}Employee IdotherWe want to ignore this field.dateJoined%dDate and TimeTherefore, we could parse this JSON log entry with the template:{ "firstName":"%S{First Name}", "lastName":"%S{Last Name}", "employeeId":"%s{Employee Id}", "dateJoined":"%d"}Notice that in the above template the "other" field has been ignored. To ignore a field we simply do not include it in our template. If one of the elements we were interested in had been a child of a parent node, we would have needed to include the parent node in our template. The important thing is that the template has the full path to the target node.Once we load this template into LogViewPlus it will appear as:To do this, we just need to give LogViewPlus our parsing template as an argument for the JSON parser. We can do this in Parser Mappings:White space will be ignored, so we are free to format the JSON as needed.If our log file contained multiple log entries, LogViewPlus would expect them all to have the same format. New log entries should also be separated by a new line as discussed above.Log files parsed with the JSON parser support automatic pretty-printing by default.Finally, notice the similarities between the JSON Parser and the XML Parser discussed in the next section. Both use the concept of templates, so once you have learned one you have basically learned the other.Parsing Embedded JSONLogViewPlus v2.5.56 and greater can parse JSON log entries are embedded within a parent object. For example, consider the JSON log file:{ logid: "App Log 1", entries: [ { "firstName":"John", "lastName":"Doe", "employeeId":"12345", "other":"ignore me", "dateJoined":"2014-05-16 10:50:14,125" }, { ... } ]}Our conversion pattern for this log file will largely look the same as before with one crucial difference. We must specify the outer element which will act to