AWS AI-Powered Threat Detection Platform

AI-Powered Threat Detection Platform

RUNNING

Platform Status: Monitoring 1.2M log events/hour across 42 AWS accounts. Model accuracy: 94.2%. Last anomaly detected: 12 minutes ago.

Platform Overview

Advanced AI-driven threat detection platform using machine learning to analyze security logs in real-time. The system leverages LSTM neural networks and anomaly detection algorithms to identify security threats with 94% accuracy while reducing false positives by 70%.

94.2%

Model Accuracy

8m

Avg Detection Time

70%

False Positive Reduction

1.2M/hr

Logs Processed

Data Flow Architecture

Data Sources

CloudTrail, VPC, GuardDuty

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Feature Processing

AWS Lambda, Kinesis

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ML Inference

SageMaker, LSTM Models

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Response

Auto-remediation, Alerts

Recent Activity

Time	Event Type	Account	Severity	Status
2 minutes ago	Unauthorized API Access	prod-account-01	HIGH	Blocked
15 minutes ago	Port Scanning Detected	dev-account-03	MEDIUM	Alerted
45 minutes ago	Data Exfiltration Attempt	prod-account-02	HIGH	Blocked
2 hours ago	IAM Policy Drift	prod-account-01	MEDIUM	Fixed

Real-time Threat Detection Dashboard

LIVE

Live Threat Detection Timeline

Normal Activity Threat Detected

Current Threats

Threat ID	Type	Confidence	Source	Impact	Response
THRT-2024-03-15-001	Cryptojacking	96.7%	192.0.2.42	HIGH	Isolated
THRT-2024-03-15-002	Brute Force	92.3%	203.0.113.89	MEDIUM	Blocked
THRT-2024-03-15-003	Data Exfiltration	94.8%	198.51.100.23	HIGH	Contained

Threat Distribution (Last 24 Hours)

42

Unauthorized Access

18

Cryptojacking

15

Data Exfiltration

67

Brute Force

ML Model Registry

LSTM v2.1

94.2%

Accuracy

Primary detection model

PRODUCTION

Isolation Forest v1.3

89.7%

Accuracy

Anomaly detection

STAGING

Autoencoder v1.8

92.1%

Accuracy

Unsupervised learning

DEVELOPMENT

Model Architecture

Input Layer

256 features

Bidirectional LSTM

128 units

Dropout Layer

30% dropout

Output Layer

Sigmoid activation

Model Performance

94.2%

Accuracy

89.4%

Precision

92.1%

Recall

91.6%

F1 Score

Model Training Jobs

Active Training Job

Model: td-xgboost-v2.0

Status: TRAINING

Progress: 85% (Epoch 42/50)

Estimated Completion: 2 hours 15 minutes

Training History

Job ID	Model	Start Time	Duration	Accuracy	Status
train-001	LSTM v2.1	2024-03-10 08:00	4h 30m	94.2%	COMPLETED
train-002	Isolation Forest v1.3	2024-03-05 10:15	2h 15m	89.7%	COMPLETED
train-003	Autoencoder v1.8	2024-03-01 14:30	6h 45m	92.1%	COMPLETED
train-004	XGBoost v2.0	2024-03-15 09:00	3h 30m (ongoing)	91.5%	RUNNING

python
Training Configuration

# Training configuration for LSTM model
training_config = {
    "model_type": "LSTM",
    "version": "2.1",
    "hyperparameters": {
        "learning_rate": 0.001,
        "batch_size": 32,
        "epochs": 50,
        "dropout_rate": 0.3,
        "lstm_units": 128,
        "optimizer": "adam",
        "loss_function": "binary_crossentropy"
    },
    "data_config": {
        "training_samples": 500000,
        "validation_samples": 100000,
        "test_samples": 50000,
        "feature_count": 256,
        "sequence_length": 50
    },
    "monitoring": {
        "early_stopping_patience": 10,
        "checkpoint_frequency": "every_epoch",
        "tensorboard_logging": True
    }
}

# Start training job
training_job = sagemaker_client.create_training_job(
    TrainingJobName=f"threat-detection-{training_config['version']}",
    AlgorithmSpecification={
        "TrainingImage": "custom-lstm-container:latest",
        "TrainingInputMode": "File"
    },
    HyperParameters=training_config["hyperparameters"],
    InputDataConfig=[
        {
            "ChannelName": "training",
            "DataSource": {
                "S3DataSource": {
                    "S3DataType": "S3Prefix",
                    "S3Uri": "s3://ai-threat-detection/training/",
                    "S3DataDistributionType": "FullyReplicated"
                }
            }
        }
    ],
    OutputDataConfig={
        "S3OutputPath": "s3://ai-threat-detection/models/"
    },
    ResourceConfig={
        "InstanceType": "ml.p3.2xlarge",
        "InstanceCount": 2,
        "VolumeSizeInGB": 100
    },
    StoppingCondition={
        "MaxRuntimeInSeconds": 36000
    }
)
              

Performance Metrics

94.2%

Detection Accuracy

2.3%

False Positive Rate

8.2m

Avg Response Time

99.99%

System Uptime

Accuracy Trends

Month	Model	Accuracy	Improvement	False Positives
March 2024	LSTM v2.1	94.2%	+2.1%	2.3%
February 2024	LSTM v2.0	92.1%	+1.8%	3.1%
January 2024	LSTM v1.8	90.3%	+2.5%	4.2%
December 2023	XGBoost v1.5	87.8%	+3.2%	5.8%

Performance Analysis

Performance Improvement: Since deployment, the AI system has reduced average threat detection time from 4 hours to 8 minutes (95% improvement) and decreased false positives by 70% compared to traditional rule-based systems.