Description:Field of Invention
The aim of this invention is related to the Information Security and Network Security domain. It addresses the detection and mitigation of Distributed Denial-of-Service (DDoS) attacks using an AI-driven enhanced detection mechanism. For identifying malicious traffic, the system employs machine learning algorithms such as Logistic Regression, K-Nearest Neighbors (KNN), and Random Forest to distinguish between legitimate and abnormal traffic patterns. It leverages intelligent classification, anomaly detection, and adaptive learning models to improve the accuracy, speed, and reliability of DDoS detection. The invention enhances network resilience by enabling real-time detection and prevention of DDoS attacks, thereby reducing downtime and resource exhaustion. It can be adoptable to different domains like cloud computing, Internet of Things (IoT) networks, online services, banking services, enterprise applications, and critical infrastructure systems for enhanced cyber security and service availability.
Objectives of the Invention
The goal of this invention is to develop an advanced, intelligent, and comprehensive framework for detecting and mitigating Distributed Denial-of-Service (DDoS) attacks across diverse digital platforms and network infrastructures. This invention addresses the inherent limitations of conventional DDoS detection systems such as high false positive rates, limited scalability, delayed response times, and inability to adapt to evolving attack patterns, by providing an AI-driven end-to-end detection and prevention solution. The framework utilizes machine learning algorithms including Logistic Regression, K-Nearest Neighbors (KNN), and Random Forest as core techniques for classification and anomaly detection, enabling real-time traffic analysis and automated decision-making. By continuously learning from new traffic data, the system adapts dynamically to emerging threats and zero-day attacks, thereby enhancing resilience against large-scale disruptions. The invention strengthens network availability, service continuity, and cyber security posture, making it adoptable to cloud environments, IoT networks, financial systems, online services, and critical infrastructure sectors that are highly vulnerable to DDoS attacks.
Background of the Invention
The traditional DDoS detection systems rely heavily on rule-based mechanisms, threshold monitoring, or signature-driven techniques to identify malicious traffic patterns. However, these methods suffer from significant drawbacks that compromise detection accuracy and overall network security.
Conventional systems frequently misclassify legitimate high-volume traffic surges (such as flash crowds) as DDoS attacks, resulting in false alarms. At the same time, they fail to detect low-rate, stealthy, or zero-day attacks that do not match known signatures (Kumar et al. [2023]).
Signature-based approaches require prior knowledge of attack patterns. Attackers continuously modify payloads to evade detection, rendering traditional methods ineffective against multi-vector or evolving DDoS strategies (Li et al. [2022]).
With the rapid expansion of cloud computing and IoT environments, the sheer volume and velocity of traffic overwhelms traditional threshold-based and statistical models, leading to delayed or missed detection (Chen et al. [2024]).
Manual intervention or static configurations often slow down the response process, increasing downtime, financial loss, and service disruption in real-world deployments.
To combat these challenges, AI-driven DDoS detection has emerged as a promising and adaptive alternative. This innovative approach leverages machine learning algorithms such as Logistic Regression, K-Nearest Neighbors (KNN), and Random Forest to intelligently classify traffic as normal or malicious. By drawing upon anomaly detection, adaptive learning, and intelligent classification, AI systems continuously refine their models with new data, making them resilient to emerging and zero-day attacks.
By replacing or enhancing traditional detection mechanisms with AI-driven intelligence, these systems offer:
Improved Accuracy: Reduction in false positives and negatives through refined classification. Real-Time Adaptability: Continuous learning ensures robustness against novel attack vectors. Scalability: Capable of handling massive traffic volumes in cloud, IoT, and enterprise networks. Automated Response: Faster, intelligent mitigation without relying on manual interventions.
This approach offers intelligent network monitoring on a connected computing platform. There could be multiple layers of traffic features (packet size, flow duration, source IP behavior) processed in real time. On the computing system’s analytical engine, multiple algorithms may be deployed concurrently. When the system processes a series of network flows, the approach may also involve collecting traffic metadata and applying AI classifiers to distinguish legitimate versus malicious flows. Methods and systems for enhanced DDoS detection may further comprise retraining AI models dynamically as new traffic patterns emerge (US 11843632 B2), excluding anomalous data from retraining to prevent bias (US 12052280), and using adaptive fuzzy logic for real-time tuning of detection thresholds (US 11991205). Unlike prior approaches, the present invention integrates these techniques into a comprehensive multi-model AI-driven detection framework, enabling accurate, adaptive, and scalable DDoS defense across diverse infrastructures
Summary of the Invention
The present invention strengthens network protection and service availability by incorporating an AI-driven multi-model framework for detecting and mitigating Distributed Denial-of-Service (DDoS) attacks in real time. To achieve robust security, the invention utilizes machine learning algorithms including Logistic Regression, K-Nearest Neighbors (KNN), and Random Forest, which collectively analyze diverse network traffic features such as packet size, flow duration, connection rate, and source IP patterns. The framework supports continuous retraining and adaptive learning, ensuring that detection models remain resilient against evolving attack strategies and zero-day threats. The detection processes are further fortified by multi-layer verification mechanisms, which may include combinations of anomaly detection, traffic behavior profiling, adaptive thresholding, and ensemble classification. Each traffic flow or connection attempt is recorded through a detailed traffic logging and auditing system, providing time stamped records, source/destination identifiers, protocol metadata, and behavioral signatures. This facilitates forensic analysis, intrusion detection, and compliance reporting.
The invention enables end-to-end DDoS prevention, meaning that legacy rule-based and static signature systems can be replaced with adaptive AI-driven classifiers, enhancing scalability across cloud networks, IoT environments, and enterprise infrastructures. System administrators can perform real-time monitoring, attack response, traffic redirection, and mitigation actions through an integrated management interface.A standout feature of the framework is its high degree of configurability, allowing administrators to tailor detection and mitigation policies based on risk thresholds, traffic sensitivity, or application priorities. These configuration options may include dynamic feature weighting (assigning higher importance to certain traffic features), ensemble tuning (adjusting the balance between Logistic Regression, KNN, and Random Forest outputs), and adaptive retraining intervals. This approach reduces the likelihood of both false positives and false negatives, ensuring that legitimate traffic is not disrupted while malicious traffic is accurately identified.
The invention can be deployed in distributed environments, where detection modules operate across multiple network devices or cloud nodes. It is possible to create and save detection models in the cloud, allowing synchronized updates across several monitoring points. When anomalous traffic is detected at any node, the global detection engine can update all distributed models, ensuring coordinated defense and rapid response. This federated intelligence enables organizations to protect against large-scale, multi-vector DDoS attacks and confirm that actual malicious activity, rather than normal traffic fluctuations, is being identified and mitigated.
Detailed Description of the Invention
The present invention discloses an AI-driven framework for detecting and mitigating Distributed Denial-of-Service (DDoS) attacks across heterogeneous computing environments. The invention integrates multiple machine learning models, adaptive learning modules, and automated mitigation mechanisms to provide a scalable, accurate, and resilient solution against evolving network threats.
The invention comprises the following core modules:
Traffic Collection Module – Responsible for capturing raw network traffic data from edge devices, routers, firewalls, and cloud gateways. Traffic features may include packet size, flow duration, source and destination IPs, protocol identifiers, connection rates, and payload metadata.Preprocessing Module – Performs feature extraction, noise removal, normalization, and dimensionality reduction. Data is structured into feature vectors suitable for machine learning analysis.AI-Based Detection Engine – Incorporates an ensemble of classifiers such as Logistic Regression, K-Nearest Neighbors (KNN), and Random Forest. Each classifier generates a probability score indicating whether the traffic is benign or malicious. The ensemble decision-making module aggregates outputs to improve detection accuracy.Adaptive Learning Module – Supports incremental retraining using newly labeled traffic data. This ensures that the detection engine adapts to emerging attack vectors and zero-day exploits without requiring complete model redeployment.
Mitigation and Response Module – Upon classifying traffic as malicious, the system can initiate one or more mitigation strategies, including:
Traffic rate limiting,Blacklisting source IPs or ASNs,Redirecting suspicious traffic to a scrubber,Triggering upstream alerts for coordinated defenseMonitoring and Logging Module – Records all classification decisions, mitigation actions, and network events. Logs include time stamped flow data, protocol information, anomaly scores, and action outcomes to enable compliance reporting, forensic investigations, and continuous improvement.
Incoming traffic is captured by the Traffic Collection Module.Extracted features are passed to the Preprocessing Module for cleaning and normalization.The processed traffic vector is input into the AI-Based Detection Engine.Each classifier (Logistic Regression, KNN, and Random Forest) produces an output score.The Ensemble Decision Maker aggregates classifier scores and outputs a final decision.If traffic is labeled as malicious, the Mitigation Module executes the appropriate action.All events are logged for auditing and adaptive learning.The Adaptive Learning Module periodically retrains models to incorporate new attack patterns.
The invention may be deployed in several configurations:
Cloud Environments – As a distributed detection service integrated into cloud infrastructure, ensuring resilience against large-scale volumetric DDoS attacks.IoT Networks – At IoT gateways, detecting anomalies in low-power device traffic while preventing service disruption.Enterprise Networks – Within data centers, providing both perimeter and internal traffic monitoring to safeguard critical services.Federated Environments – Models may be stored in the cloud and synchronized across multiple detection nodes. When an attack is detected at one node, updated detection rules are propagated globally to all nodes.
The present invention provides the following advantages over conventional systems:
High Accuracy – Ensemble learning reduces false positives and false negatives.
Real-Time Adaptation – Continuous retraining ensures resilience against zero-day and evolving threats.Scalability – Capable of analyzing millions of flows per second in cloud and IoT infrastructures.Automated Mitigation – Reduces dependency on manual interventions, minimizing downtime.
Comprehensive Logging – Facilitates audits, intrusion detection, and compliance reporting.
In one embodiment, the system is deployed at a cloud data center edge. Traffic flows from external clients are mirrored into the Traffic Collection Module. The Preprocessing Module extracts features such as packet inter-arrival times, average flow length, and connection bursts. The AI-Based Detection Engine applies Logistic Regression to detect linear separations, KNN for neighborhood-based anomalies, and Random Forest for high-dimensional classification. The ensemble result indicates that a surge of traffic from multiple distributed IPs exhibits abnormal connection patterns. The Mitigation Module initiates rate-limiting and redirects malicious traffic to a scrubber node, while legitimate client traffic remains unaffected. All events are logged, and the Adaptive Learning Module updates model parameters for future detection
Brief description of Drawing
Figure 1, Data flow diagram
The provided diagram represents the workflow of the AI-driven DDoS detection system, showcasing the key stages of data handling, feature engineering, model building, and evaluation. The process begins with raw dataset ingestion and systematically progresses through preprocessing, normalization, class balancing, and feature reduction, before moving into model training with multiple algorithms and final evaluation. , Claims:The scope of the invention is defined by the following claims:
1. A system/method for detecting Distributed Denial of Service (DDoS) attacks using AI/ML techniques, said system/method comprising the steps of:
a) Ingesting raw network traffic data (1) from diverse sources including packet captures, flow records, and log data;
b) Preprocessing the data to remove noise, standardize formats, and extract key attributes;
c) Applying normalization techniques to ensure uniform scaling of features;
d) Handling class imbalance using resampling or synthetic data generation techniques such as SMOTE;
e) Performing dimensionality reduction and feature selection (e.g., PCA) to optimize model performance;
f) Splitting the data into training and testing sets to ensure fair model evaluation;
g) Building models using machine learning algorithms such as Random Forest, Logistic Regression, K-Nearest Neighbor, Support Vector Machine, and Decision Tree;
h) Evaluating the trained models against established performance metrics (e.g., accuracy, precision, recall, F1-score, ROC-AUC) to determine the best-performing classifier.
2. According to claim 1, wherein the preprocessing stage further comprises filtering irrelevant network headers, detecting missing values, and encoding categorical features into machine-readable form.
3. As per claim 1, wherein the class imbalance handling module employs adaptive synthetic oversampling (ADASYN), under-sampling, or cost-sensitive learning to enhance detection accuracy of minority attack classes.
4. According to claim 1, wherein the evaluation stage generates human-readable reports with visualizations of model performance, confusion matrices, and comparative results across different classifiers.