# Applications of Machine Learning in Cyber Security: A Review
## 6 Shortcomings in Existing IDSs
### Ethical AI and Compliance Aspects
This auditing framework allowed for a series of regular reviews and impact evaluations to gauge success in model performance, to determine conformity with the EU GDPR, and to create a responsible mechanism for consideration of complaints and problems relating to the model. 5. Robustness and security against adversarial attacks: Models used in cyber security must also be formally resistant to adversarial attacks that aim to manipulate underlying vulnerabilities in the training data or the model structure. Such robustness can be achieved through robust training paradigms, such as adversarial training. The Guaranteeing AI Robustness against Deception project by DARPA ([[https://www.darpa.mil/program/guaranteeing-ai-robustness-against-deception](https://www.darpa.mil/program/guaranteeing-ai-robustness-against-deception)]([https://www.darpa.mil/program/guaranteeing-ai-robustness-against-deception](https://www.darpa.mil/program/guaranteeing-ai-robustness-against-deception)), accessed on 3 November 2024) is an example of a project that secures ML models against adversarial threats. In this project, models were trained on artificially generated cyber security data to prepare them for real-world malicious attacks. 6. User-centric design and human oversight: In ethical AI within cyber security, user-centric design must ensure human oversight is prioritized. A human-in-the-loop (HITL) approach allows for human intervention if automation fails to suffice, thus providing a layer of ethical decision-making. The Umbrella Security platform ([[https://umbrella.cisco.com/](https://umbrella.cisco.com/)]([https://umbrella.cisco.com/](https://umbrella.cisco.com/)), accessed on 3 November 2024) used by Cisco effectively implements HITL strategies, flagging uncertain cases for human review. 7. Compliance: Integrating frameworks such as GDPR, EU AI Act, and NIST's AI risk management framework will ensure the legal use and applications of ethical AI. Periodic evaluations ensure the models remain compliant with pressure from regulation amendments. The NIST's AI Compliance for Federal Cyber Security project requires the creation of federal cyber security models complying with the NIST ([[https://www.nist.gov/itl/ai-risk-management-framework](https://www.nist.gov/itl/ai-risk-management-framework)]([https://www.nist.gov/itl/ai-risk-management-framework](https://www.nist.gov/itl/ai-risk-management-framework)), accessed on 3 November 2024 ) principles for AI ethics, fairness, and robustness, setting a template for cyber security teams throughout the U.S. to maintain policy compliance and set a standard for ethical AI across public cyber security. A diversified approach must therefore be put in place to integrate AI principles of an ethical nature into cyber security. By employing techniques such as explainable AI, federated learning, adversarial training, and human-in-the-loop artificial intelligence, cyber security projects may solidify the grounds for ethics that pattern model adherence and model robustness to meet compliance requirements within a rapidly shifting paradigm of cyber security.