Machine learning in cybersecurity

1. Data Quality and Quantity:

One of the foremost challenges in integrating ML for cybersecurity is the acquisition of high-quality data in sufficient quantity.

2. Overfitting and Underfitting:

The nuances of overfitting and underfitting pose significant hurdles in ML model development. 

3. Model Maintenance and Monitoring

The dynamic nature of cyber threats necessitates continuous monitoring and maintenance of ML models

4. Imbalanced Datasets:

The inherent class imbalance between malicious and benign data exacerbates the challenge of effectively training ML models.

5. False Positives and Negatives:

The occurrence of false positives and false negatives undermines the reliability of ML-based cybersecurity solutions.

6. Adversarial Attacks:

The susceptibility of ML models to adversarial attacks constitutes a grave concern in cybersecurity.

7. Lack of Skilled Professionals:

The burgeoning demand for cybersecurity specialists exacerbates the scarcity of skilled professionals proficient in ML techniques.

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