Blackbox_Malware_Sequel_Collab_Diagram.pptx

Blackbox malware" can refer to two main concepts: a type of adversarial attack against AI-driven malware detectors, where attackers have no internal knowledge of the detection model and try to bypass it by observing only inputs and outputs, or malicious software disguised as a legitimate, unknown process (like the blackbox.exe file), which can slow down a computer or redirect users to unwanted sites. The former is an advanced technique in cybersecurity and machine learning, while the latter is a more traditional form of malware that uses an innocent-sounding name to hide its presence.

1. Sequel & Collaborative Diagram for Blackbox Malware Detection Enhancing Detection Robustness in Real-World Applications

2. Sequel: Future Scope – Real-Time, Federated Detection Next Steps in Project Development: 1. Deploy real-time malware scanning using FastAPI with TensorFlow Lite. 2. Integrate Federated Learning to enhance privacy and edge-device collaboration. 3. Harden defenses using adversarial training against dynamic attacks. 4. Leverage autoencoders and graph neural networks (GNNs) for feature abstraction. 5. Continuously enrich dataset using threat intelligence APIs (e.g., VirusTotal).

3. Collaborative Diagram: Interaction Between Key Components Attacker (Malware Developer) Min-Max Optimization Engine Differential Privacy Mechanism Malware Detection (Logistic Regression)