#4015 PC Machine Learning for PC Cybersecurity

#4015 PC Machine Learning for PC Cybersecurity

#4015 Describe a software application to monitor "normal" pc activity on a given machine, to be used for machine learning of a cybersecurity application for that pc

Monitoring "normal" activity to train a cybersecurity machine learning (ML) model requires a software application often referred to as a Behavioral Monitoring Agent or a Host-Based Sensor. In an enterprise context, this is the core of Endpoint Detection and Response (EDR).

For your application, the software's primary job is to create a high-fidelity "digital twin" of your PC's standard operations. This involves capturing telemetry across several distinct system layers.

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1. The Sensor Architecture (Data Ingestion)

To train an ML model effectively, the monitor must act as a transparent "flight recorder" for the operating system.

Key Monitoring Modules:

• Process Monitor: Tracks every execution, parent-child process relationships, and command-line arguments.

  • Example: Identifying that explorer.exe launching cmd.exe is normal, but word.exe launching powershell.exe is a common anomaly.

• Network Flow Tracker: Logs local and remote IP addresses, ports, protocol types, and data volumes. It baselines which servers (like Windows Update or your specific cloud storage) your PC normally talks to.

• File System Watcher: Monitors I/O activity. It doesn't look at file content, but rather the rate and type of changes.

  • ML Value: A sudden burst of file renames or encryptions is the signature of ransomware.

• System Call (Syscall) Auditor: The most granular level. It monitors requests made to the OS kernel (e.g., NtCreateFile, NtOpenProcess).

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2. The Baselining Phase ("Learning Normal")

During the training period (typically 7–14 days), the software operates in a "Passive Observation" mode. It captures data to build a Behavioral Profile.

Dimension	"Normal" Baseline Example
Temporal	User logs in at 8:00 AM; heavy CPU usage during 10:00 AM backups.
Relational	Web browser always connects to specific AWS/Google IP ranges.
Statistical	Average of 50 DNS queries per hour; 200MB outbound data/day.
Privilege	Only specific admin tools ever request "SeDebugPrivilege."

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3. Data Transformation for Machine Learning

Raw logs are useless for ML; they must be converted into Feature Vectors. The monitoring software must perform real-time feature engineering:

1. Categorical Encoding: Converting process names (strings) into numerical values.

2. Aggregation: Instead of single events, the software sends "windows" of activity (e.g., "Number of failed logins in the last 60 seconds").

3. Graph Mapping: Creating a map of how processes interact, which allows the ML model to see the "attack chain" rather than isolated events.

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4. Implementation Strategy

If you are building this as a prototype or for a specific use case, you don't need to write drivers from scratch. You can leverage existing telemetry engines:

• Windows Sysmon (System Monitor): A free Microsoft utility that provides high-level logging of process creations, network connections, and file changes.

• Event Tracing for Windows (ETW): The high-performance backend that provides the raw data Sysmon uses.

• ELK Stack (Elasticsearch, Logstash, Kibana): A common "sink" for this data. You can use Elastic ML to automatically find anomalies in the streams you've collected.

Technical Note: In your 2025 environment, you might consider using eBPF (Extended Berkeley Packet Filter) if you are monitoring Linux-based systems. It allows for high-performance monitoring at the kernel level without the risk of system crashes associated with traditional kernel modules.

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Next Steps

Would you like me to outline a Python-based data pipeline that takes Sysmon logs and converts them into a training dataset for an anomaly detection model?

Yes

To build a functional pipeline, we need to move from raw system logs to a structured format that a Machine Learning model (like an Isolation Forest or LSTM) can digest.

Since you are targeting a PC environment, we will use Windows Sysmon as the primary data source and Python for the processing.

The Pipeline Architecture

The process follows a standard ETL (Extract, Transform, Load) pattern tailored for cybersecurity telemetry.

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1. Data Ingestion: Extracting Sysmon Logs

Sysmon writes events to the Windows Event Log. We can use the winevt-extractor or python-evtx libraries to pull these into a data frame. We focus on Event ID 1 (Process Creation) and Event ID 3 (Network Connection).

Python

import pandas as pd
# Hypothetical function to parse EVTX to DataFrame
def get_sysmon_logs(log_path):
    # Extracts Timestamp, EventID, ProcessName, CommandLine, User, ParentProcess
    return pd.read_csv("sysmon_export.csv") 

df = get_sysmon_logs("C:/Logs/sysmon.evtx")

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2. Feature Engineering: Converting Logic to Numbers

ML models cannot "read" a command line. We must turn strings into numerical features that represent behavior.

• Entropy Scoring: High entropy in a command line (random characters) often indicates obfuscated malware or encryption keys.

• Path Depth: Counting the slashes in a file path. Malware often runs from deep AppData/Local/Temp folders.

• Frequency Analysis (TF-IDF): How rare is this process? chrome.exe is common; asdf.exe is not.

Python

# Example Feature Engineering
df['cmd_length'] = df['CommandLine'].apply(len)
df['is_admin'] = df['User'].apply(lambda x: 1 if 'SYSTEM' in x or 'Administrator' in x else 0)
df['path_depth'] = df['Image'].apply(lambda x: str(x).count('\\'))

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3. The ML Model: Anomaly Detection

For a single PC, we don't have "labels" for what is a virus and what isn't. Therefore, we use Unsupervised Learning. The Isolation Forest algorithm is excellent here because it explicitly looks for "outliers" (anomalies).

Python

from sklearn.ensemble import IsolationForest

# Select numerical features for the model
features = ['cmd_length', 'is_admin', 'path_depth']
X = df[features]

# Initialize and train the model
# Contamination is the expected % of 'bad' events (e.g., 0.1%)
model = IsolationForest(contamination=0.001, random_state=42)
df['anomaly_score'] = model.fit_predict(X)

# Results: -1 indicates an anomaly, 1 indicates normal
anomalies = df[df['anomaly_score'] == -1]

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4. Real-time Monitoring Logic

Once the model is trained on "Normal" activity (your baseline), the application runs a loop:

1. Poll the Event Log every 30 seconds.

2. Transform the new logs into the same feature vector format.

3. Predict using the trained model.

4. Alert if the score hits -1.

Feature	Normal (Baseline)	Anomaly Detected
Command Length	45 chars (git commit...)	450 chars (Encoded PowerShell)
Parent Process	explorer.exe	winword.exe
Network Port	443 (HTTPS)	4444 (Metasploit Default)

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Challenges to Consider

• Concept Drift: As you install new software, "Normal" changes. You will need a mechanism to periodically retrain the model or a "Mark as Safe" button to update the baseline.

• Performance Overhead: Running an ML inference engine in the background shouldn't eat up your CPU. Using a lightweight library like Scikit-learn or ONNX is preferable over heavy deep-learning frameworks.

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Would you like me to create a specific list of Sysmon Event IDs and the "features" we should extract from each to make the model more robust?

Yes

To build a robust cybersecurity ML model, you need to select Event IDs that capture the "DNA" of system behavior. Tracking everything is too noisy; tracking too little creates blind spots.

Here is the strategic breakdown of Sysmon Event IDs and the specific features you should extract to feed your Python pipeline.

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1. Core Event IDs for Feature Extraction

Event ID	Name	Why it matters	Features to Extract (ML Inputs)
1	Process Creation	The most critical. Shows what is running and who started it.	ParentImage, CommandLine length, Entropy of command line, Company (from PE metadata).
3	Network Connection	Shows data exfiltration or Command & Control (C2) activity.	DestinationPort, DestinationHostname length, Protocol, IsOutbound.
7	Image Loaded	Tracks DLL injection. Malware often loads unusual DLLs into legitimate processes.	ImageLoaded (path), SignatureStatus (Is the DLL signed by a known vendor like Microsoft?).
11	FileCreate	Monitors for ransomware or dropper activity.	TargetFilename extension (e.g., .locked, .tmp), FileDirectory (e.g., C:\Users\...\Temp).
12/13	Registry Event	Detects persistence (malware trying to survive a reboot).	TargetObject (e.g., Run keys, Services), Details (the new value being written).

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2. Advanced Feature Engineering (The "Secret Sauce")

For a high-quality model, don't just use the raw data. Create Derived Features that represent behavioral patterns:

A. The "Ancestry" Feature

Standard OS behavior has a very predictable hierarchy.

• Normal: services.exe → svchost.exe.

• Anomaly: outlook.exe → cmd.exe.

• ML Input: Create a boolean feature UnexpectedParent based on a lookup table of common process trees.

B. Command Line Entropy

Malware often uses "Packers" or obfuscation (Base64) to hide its intent.

• Feature: Calculate the Shannon Entropy of the CommandLine string.

• Logic: H = -\sum p_i \log_2 p_i. A high score (typically > 5.0) in a command line is a strong indicator of encrypted or encoded payloads.

C. Rareness (Frequency Analysis)

If a process runs on your PC that has never been seen in the last 30 days, it is statistically significant.

• Feature: AppOccurrenceCount.

• Implementation: Use a rolling window (e.g., Counter in Python) to track how many times a process name appears. New processes get a "Rareness" flag.

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3. Data Processing Flow

Example Python Feature Extractor:

import math
from collections import Counter

def calculate_entropy(text):
    if not text: return 0
    probs = [n/len(text) for n in Counter(text).values()]
    return -sum(p * math.log2(p) for p in probs)

# Processing a Sysmon Event 1 (Process Create)
def extract_features(event):
    return {
        "proc_name": event['Image'].split('\\')[-1],
        "cmd_entropy": calculate_entropy(event['CommandLine']),
        "is_signed": 1 if event['SignatureStatus'] == 'Valid' else 0,
        "is_temp_dir": 1 if 'AppData\\Local\\Temp' in event['Image'] else 0,
        "parent_is_browser": 1 if 'chrome.exe' in event['ParentImage'].lower() else 0
    }

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4. Operationalizing the Model

Once you have these features, your Isolation Forest or One-Class SVM will look for data points that sit far away from the "cluster" of your daily activity.

Pro Tip: For a cybersecurity application, false positives are your biggest enemy. Use a "Grace Period" of 48 hours when you install new software to let the model "see" the new activity before it starts alerting.

Would you like me to show you how to set up a "Watchdog" script in Python that triggers a system alert (like a notification) when the ML model detects a high-entropy anomaly?

From <https://gemini.google.com/app/5d172c148a59dfd3>  Google Gemini 3.0 Pro (Thinking)