Lamar Alessa
Penetration Tester
Independent Security Researcher
Lamar Alessa is a Cybersecurity Researcher and Bug Bounty Hunter who has discovered 80+ critical vulnerabilities. She is a Black Hat competitor, holding elite certifications including eWPT and CCEP.
Lamar specializes in web application penetration testing and AI-assisted security workflows. Recognized in global tech forums, she actively competes in top-tier cybersecurity hackathons.
Currently pursuing a Computer Science degree at PMAU (Prince Mosaed bin Abdulrahman University), Lamar combines technical execution with threat research to build safer digital ecosystems through vulnerability disclosure
Speaker sessions
Briefings and Arsenals | Confirmed Speakers | No Session Assigned
Placeholder session for confirmed speakers without an assigned session, allowing them to be pushed to Swapcard and Visit.
- 07:30
- Tue
- 01 Dec
Stage:
Briefing Stage 2
Sessions Type:
Keynote
Breaking the 100ms Barrier: Real-Time Explainable AI for Proactive Zero-Day IoT Protection
Resource-constrained Internet of Things (IoT) edge devices face severe vulnerabilities against zero-day exploits due to their computational limitations. While Deep Learning (DL) Intrusion Detection Systems (IDS) offer high detection rates, their "black-box" nature lacks transparency. Traditional Explainable AI (XAI) frameworks (such as SHAP and LIME) incur significant response latency on edge hardware�often exceeding 170ms to 223ms�making real-time mitigation impossible and converting security responses into reactive post-incident measures.This session presents a novel proactive edge-defense framework that breaks the computational explanation barrier for resource-constrained IoT environments. We introduce a dual-quantization methodology applied simultaneously to a one-class Autoencoder-DNN model (ASAE-DNN) and the internal evaluation loop of the LIME explainability unit. By reducing computational precision from 32-bit to 8-bit, our architecture achieves an aggressive 74% reduction in model size while retaining a 97.35% detection recall against unknown zero-day attack vectors.Crucially, the dual-quantized XAI unit lowers explanation latency to 88ms, crossing the critical 100ms proactive operation threshold. This enables feature-guided, automated device isolation driven by instant, trustworthy evidence before system compromise occurs. We evaluate explanation reliability using quantitative faithfulness (92%) and robustness metrics, proving that edge-level transparency and ultra-low latency can co-exist effectively.
- 17:30
- Thu
- 03 Dec
Stage:
Briefing Stage 2
Sessions Type:
Keynote