Package Summary

Data Acquisition and Queries

See Querying and Importing Data


Extensible query library targeting Log Analytics or OData endpoints. Built-in parameterized queries allow complex queries to be run from a single function call. Add your own queries using a simple YAML schema.

See Data Provider Library

Sample notebook - Data Queries Notebook

Data Processing and Enrichment



The TILookup class can lookup IoCs across multiple TI providers. builtin providers include AlienVault OTX, IBM XForce, VirusTotal and Azure Sentinel.

The input can be a single IoC observable or a pandas DataFrame containing multiple observables. Depending on the provider, you may require an account and an API key. Some providers also enforce throttling (especially for free tiers), which might affect performing bulk lookups.

See Threat Intel Lookup

Sample notebook - TILookup Usage Notebook



Wrapper class around Virus Total API. Input can be a single IoC observable or a pandas DataFrame containing multiple observables. Processing requires a Virus Total account and API key and processing performance is limited to the number of requests per minute for the account type that you have. Support IoC Types:

  • Filehash
  • URL
  • DNS Domain
  • IPv4 Address

Sample notebook - VTLookup Usage Notebook



Geographic location lookup for IP addresses. This module has two classes for different services:

  • GeoLiteLookup - Maxmind Geolite (see
  • IPStackLookup - IPStack (see Both services offer a free tier for non-commercial use. However, a paid tier will normally get you more accuracy, more detail and a higher throughput rate. Maxmind geolite uses a downloadable database, while IPStack is an online lookup (API key required).

See GeoIP Lookup

Sample notebook - GeoIP Lookup Usage Notebook

Azure Data

This package contains functionality for enriching data regarding Azure host details with additional host details exposed via the Azure API.

See Azure Data Enrichment



Base64 and archive (gz, zip, tar) extractor. Input can either be a single string or a specified column of a pandas dataframe. It will try to identify any base64 encoded strings and decode them. If the result looks like one of the supported archive types it will unpack the contents. The results of each decode/unpack are rechecked for further base64 content and will recurse down up to 20 levels (default can be overridden). Output is to a decoded string (for single string input) or a DataFrame (for dataframe input).

See Base64 Decoding and Unpacking

Sample notebook - Base64Unpack Usage Notebook



Uses a set of builtin regular expressions to look for Indicator of Compromise (IoC) patterns. Input can be a single string or a pandas dataframe with one or more columns specified as input.

The following types are built-in:

  • IPv4 and IPv6
  • URL
  • DNS domain
  • Hashes (MD5, SHA1, SHA256)
  • Windows file paths
  • Linux file paths (this is kind of noisy because a legal linux file path can have almost any character) You can modify or add to the regular expressions used at runtime.

Output is a dictionary of matches (for single string input) or a DataFrame (for dataframe input).

See IoC Extraction

Sample notebook - IoCExtract Usage Notebook



Module to load and decode Linux audit logs. It collapses messages sharing the same message ID into single events, decodes hex-encoded data fields and performs some event-specific formatting and normalization (e.g. for process start events it will re-assemble the process command line arguments into a single string).



Module to support the investigation of Linux hosts through Syslog. Includes functions to create host records, cluster logon events, and identify user sessions containing suspicious activity.



Module to investigation of command line activity. Allows for the detection of known malicious commands as well as suspicious patterns of behaviour.



Module to support investigation of domain names and URLs with functions to validate a domain name and screenshot a URL.

Security Analysis

Anomalous Sequence Detection


Detect unusual sequences of events in your Office, Active Directory or other log data. You can extract sessions (e.g. activity initiated by the same account) and identify and visualize unusual sequences of activity. For example, detecting an attacker setting a mail forwarding rule on someone’s mailbox.

See Anomalous Sessions

Sample notebook - Anomalous Sequence Notebook

Time Series

msticpy.analysis.timeseries and msticpy.sectools.timeseries

Time series analysis allows you to identify unusual patterns in your log data taking into account normal seasonal variations (e.g. the regular ebb and flow of events over hours of the day, days of the week, etc.). Using both analysis and visualization highlights unusual traffic flows or event activity for any data set.

See Time Series Analysis and Anomalies Visualization

Sample notebook - Time Series



This module is intended to be used to summarize large numbers of events into clusters of different patterns. High volume repeating events can often make it difficult to see unique and interesting items.

The module contains functions to generate clusterable features from string data. For example, an administration command that does some maintenance on thousands of servers with a commandline such as: install-update -hostname {host.fqdn} -tmp:/tmp/{GUID}/rollback can be collapsed into a single cluster pattern by ignoring the character values in the string and using delimiters or tokens to group the values.

This is an unsupervised learning module implemented using SciKit Learn DBScan.

See Event Clustering

Sample notebook - Event Clustering Notebook



Similar to the eventcluster module but a little bit more experimental (read ‘less tested’). It uses SkLearn Isolation Forest to identify outlier events in a single data set or using one data set as training data and another on which to predict outliers.


This is a collection of display and utility modules designed to make working with security data in Jupyter notebooks quicker and easier.

See Displaying/Visualizing Data

Process tree

msticpy.nbtools.process_tree - process tree visualization.

The process tree functionality has two main components:

  • Process Tree creation - taking a process creation log from a host and building the parent-child relationships between processes in the data set.
  • Process Tree visualization - this takes the processed output displays an interactive process tree using Bokeh plots.

There are a set of utility functions to extract individual and partial trees from the processed data set.

See ProcessTree

Sample notebook - Process Tree Visualization

Event timeline

msticpy.nbtools.timeline - event timeline visualization.

Display any log events on an interactive timeline. Using the Bokeh Visualization Library the timeline control enables you to visualize one or more event streams, interactively zoom into specific time slots and view event details for plotted events.

See Event Timeline

Sample notebook - Event Timeline Visualization

Notebook widgets


Common functionality such as list pickers, time boundary settings, saving and retrieving environment variables into a single line callable command.

See Notebook Widgets

Sample notebook - Event Clustering Notebook

Display functions


Common display of things like alerts, events in a slightly more consumable way than print()


SecurityAlert and SecurityEvent



Encapsulation classes for alerts and events. Each has a standard ‘entities’ property reflecting the entities found in the alert or event. These can also be used as meta-parameters for many of the queries. For example the query: qry.list_host_logons(query_times, alert) will extract the value for the hostname query parameter from the alert.



Entity classes (e.g. Host, Account, IPAddress) used in Azure Security Center and Azure Sentinel alerts and in many of the modules of msticpy.

Each entity encapsulates one or more properties related to the entity.

Supported Platforms and Packages

  • msticpy is OS-independent
  • Requires Python 3.6 or later
  • See requirements.txt for more details and version requirements.