Adversarial Attacks Against Deep Learning Security Models

Cybercriminals exploit vulnerabilities in deep learning models using adversarial techniques:

• Evasion Attacks: Attackers manipulate input data (e.g., modifying malware code slightly) to fool classifiers.
• Countermeasure: Adversarial training, where models are trained on modified attack samples.
• Data Poisoning: Injecting malicious data into the training set to degrade model performance.
• Countermeasure: Data validation and anomaly detection during training.
• Model Extraction Attacks: Reverse-engineering a model’s decision-making process to bypass security measures.
• Countermeasure: Differential privacy techniques to prevent unauthorized access to model internals.