Whether it is hacking an enterprise system or wreaking havoc on critical energy grid infrastructure, cyberattacks are escalating at a staggering rate globally. Accenture* Security reported a triple-digit increase (125 percent)1 in network intrusions across industries and geographies in the first half of 2021. Several factors have expanded ways that unauthorized individuals could enter a system via the network, including the massive growth of devices attached to the cloud.
Protecting customer data and system integrity is one of the most important challenges facing the enterprise today. This AI reference kit helps enterprise system administrators and network engineers detect network intrusion threats, which may compromise customer data, systems, IP security, and other assets.
Along with Intel® software, this kit delivers faster, more efficient, and accurate results from model training for threat category classification.
In collaboration with Accenture*, Intel developed this network intrusion detection reference kit. When integrated into an application, it may help detect network threats to the enterprise system administrators and network engineers. The kit offers protection for enterprise data, systems, and IP security. When paired with Intel software products, the kit delivers faster more accurate training and threat category classification.
This kit includes:
- Training data
- An open source, trained model
- User guides
- Intel® AI software products
At a Glance
- Industry: Finance, Banking, Government, Infrastructure
- Data preprocessing
- Train and inference a model to classify the input data into threat categories (malignant, benign, outlier).
- Dataset: Network intrusion detection data
- Type of Learning: Supervised learning
- Models: Nu Support Vector Classification (NuSVC) – multiclass classification
- Output: Probability of network intrusion and classification of each threat by category (malignant, benign, outlier).
- Intel® AI Software Products:
- Intel® AI Analytics Toolkit (AI Kit)
- Intel® Extension for Scikit-learn*
Network intrusion detection and classification can be a very compute-intensive for inference workloads, given the large dataset sizes. This experiment showcases how, when paired with Intel software products, the kit can significantly increase the speed of training and inferencing with the added benefit of greater accuracy.
Optimized Intel® AI Software Products for Better Performance.
AI Kit—Achieve end-to-end performance for AI workloads.
The Intel Extension for Scikit-learn helps enable faster and more effective training models. Better trained models help improve the network intrusion detection speed and threat category classification accuracy. The overall benefit to the enterprise is faster time to threat insights to enable an enterprise to take action rapidly.
Performance was tested on Microsoft Azure* Standard_D4_v5 using 3rd generation Intel® Xeon® processors for optimized performance.
To build a network intrusion system, data scientists need to train models with substantial datasets and run inferencing more frequently. This kit is optimized for performance with Intel software products that enable faster training, higher efficiency, and more accuracy on the detections. In addition, the faster speed of training and inference also helps organizations track intrusions in real-time scenarios.
As network attackers become bolder with more fraudulent schemes, ransomware or critical infrastructure attacks and network intrusions increase. As a result, enterprises need to rapidly detect and avert potential catastrophic and costly issues to protect assets, customer data, and trust. The network intrusion detection reference kit with Intel software products enables faster threat detection model training with higher efficiency and more accurate classification of the threats.
This reference kit can also apply to government, infrastructure, and other sectors that require network intrusion security.