It is a research field where Machine Learning meets computer security to enable the adoption of ML techniques in adversarial environments like malware detection, biometric recognition, and spam filtering.
In an adversarial attack, images undergo very minute and inconspicuous changes so that the ML model misclassifies the image. Such attacks are almost impossible to detect, making security systems extremely vulnerable to malicious impersonation. Such attacks can also exploit the control systems in automatic vehicles, causing them to make dangerous mistakes while in use.
Applications of Adversarial Machine Learning
The prime purpose of AML techniques is to prevent the intentional manipulation of inputs so that the model makes inaccurate predictions and decisions. AML techniques also improve the security and reliability of applications like fraud detection, medical diagnosis, and autonomous vehicle controls.
AML is used in:
1. Anomaly detection
AML can detect unusual behavior in anomaly detection systems by analyzing real-time data from machines. By studying the data, AML algorithms can identify anomalies that signify an impending attack or system malfunction.
2. Malware detection
In malware detection systems, AML identifies fresh and unfamiliar malware with the help of behavioral patterns in AML algorithms detected in real time.
3. Intrusion detection
AML algorithms analyze real-time network traffic to spot out unusual behavioral patterns and anomalies on computer networks. With AML, intrusion detection systems can sense impending cyber attacks.
4. Adversarial detection
By gathering and analyzing real-time inputs from ML models, AML algorithms can detect and prevent adversarial attacks and malicious attacks.
Generally, real-time AML applications are becoming increasingly important in the AI industry, with applications in many fields. With such significant advances in AI, ML models are becoming more secure, reliable, and suitable for high-stake applications.
