**Cybersecurity AI Challenge: "Evasive Evasion"**

Cybersecurity AI Challenge: "Evasive Evasion"

In today's threat landscape, attackers are constantly evolving their tactics to evade detection by defensive AI systems. In this challenge, we'll push the boundaries of AI-powered cybersecurity by creating an evasion scenario that goes beyond traditional deception techniques.

Challenge Overview:

You are tasked with developing an AI-powered evader that can evade detection by a state-of-the-art Machine Learning (ML) based Intrusion Detection System (IDS). Your evader should be able to manipulate the input data to the IDS in a way that exploits its weaknesses and remains undetected.

Constraints:

1. The evader must be able to modify the input data in real-time, using a combination of noise injection, data compression, and data manipulation techniques.
2. The evader should be designed to evade detection by a ML-based IDS that uses a combination of supervised and unsupervised learning algorithms.
3. The evader must be scalable to evade detection on a large dataset of network traffic.
4. The evader cannot use any known evasion techniques, such as protocol spoofing or packet forgery.
5. The evader must be designed to operate within a constrained environment, with limited computational resources.

Evaluation Criteria:

1. Evasion rate: How often is the evader able to evade detection by the IDS?
2. Detection latency: How quickly can the IDS detect the evader?
3. Resource utilization: How much computational resources does the evader consume?
4. Adaptability: How well can the evader adapt to changes in the IDS's behavior?

Dataset:

You will be provided with a dataset of network traffic, including normal and malicious traffic. The dataset will be used to train and evaluate the ML-based IDS.

Submission Requirements:

1. Submit a detailed description of your evader design, including the algorithms and techniques used.
2. Provide a working implementation of the evader, along with a dataset of network traffic that demonstrates its capabilities.
3. Evaluate the performance of your evader on the provided dataset, using the evaluation criteria outlined above.

Prizes:

The winner of this challenge will receive a cash prize of $10,000 and a feature in a leading cybersecurity publication. The winner will also be recognized as a leading expert in AI-powered cybersecurity evasion techniques.

Submission Deadline:

The submission deadline is January 15, 2026. Late submissions will not be accepted.

Rules:

This challenge is open to individual researchers and teams. By submitting an entry, you agree to the rules and terms of the challenge.

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