Key Research Methods in Cybersecurity Analysis

First principles are about stripping a problem down to its most basic, unshakeable truths and building understanding from there. In cybersecurity, I see packet capture and analysis as that bedrock—the first principle for getting to authoritative information. Think about it: a hacker can sneak into a machine or network and cover their tracks by deleting logs, wiping files, or erasing system breadcrumbs. They can manipulate what’s left behind to mislead or obscure. But what they can’t erase are the packets—the raw data transmissions that carried their actions across the network. Those packets are the fundamental truth of what happened, untouched by post-attack cleanup. It’s like this: if cybersecurity is about knowing what’s real in a system, you start by asking, “What’s the most basic evidence of activity?” Logs can be altered. Files can be deleted. But packets—the actual bits of data flying between points—are the atomic level of network truth. They existed to enable the attack, and they don’t vanish unless you miss capturing them. So, to me, packet analysis is the first principle of zero-data analysis in cybersecurity. You grab that raw stream, break it down—source, destination, payload, timing—and rebuild the picture from there. No assumptions, no reliance on what the system tells you after the fact. Just the physics of data movement. For instance, a hacker might use a remote exploit to compromise a server, then scrub the event logs. Traditional forensics might hit a dead end. But if you’ve captured the packets, you’ve got the exploit’s signature—the TCP handshake, the malicious payload, the command-and-control chatter. It’s not just evidence; it’s the starting point. From that, you reason upward: What did these packets do? How did they interact with the target? What’s the real story? This isn’t to say other methods—log analysis, memory forensics—aren’t useful. They’re just not the root. Packets are where you begin when everything else can be faked or erased. It’s the cybersecurity equivalent of saying, “Energy moves things” in physics—data moves through packets, and that’s where the truth lives.

I am excited to share my graduate research conducted at Northeastern University (Khoury College of Computer Sciences) under the guidance of Prof. Themis A. Papageorge. I extend my gratitude to my TAs, Utkarsha S. & Sujith Morusu, for their valuable support and technical guidance throughout this project. This 57-page study analyses cyber-physical vulnerabilities in a 16-node Boston metropolitan energy distribution network using: • Complex Network Analysis • Model-Based Risk Assessment (MBRA) • Resilience threshold modeling (γ₀ = 0.728) • Fault Tree Analysis (FTA) • Targeted vs. random cyber-physical attack simulations Key findings include: - Identification of Tier-1 critical substations - Network operating close to fragility boundary (γ ≈ 0.667) - Development of a $76.7M resilience optimization strategy - Demonstration of strong prevention ROI for critical nodes The full paper is attached below. #Cybersecurity #CriticalInfrastructure #EnergySecurity #OTSecurity #ICS #RiskModeling #NortheasternUniversity

🤖 𝐅𝐢𝐧𝐝𝐢𝐧𝐠 0𝐝𝐚𝐲𝐬 𝐰𝐢𝐭𝐡 𝐬𝐭𝐚𝐭𝐢𝐜 𝐚𝐧𝐚𝐥𝐲𝐬𝐢𝐬 (𝐒𝐀𝐒𝐓) 𝐚𝐧𝐝 𝐀𝐈 An approach that's found 19+ vulnerabilities in open source projects, including RCE, authentication bypasses, and IDORs. ZeroPath's Raphael Karger describes how their program analysis (SAST) + AI vulnerability detection approach works: 1. Use AI agents to investigate what apps are in a repo (e.g. for monorepos, microservices) and gather basic data about how they work. 2. Generate ASTs for the code using tree-sitter and build a call graph. 3. Enrich the graph with contextual info like endpoints (request paths, HTTP methods), middleware, AuthN/AuthZ mechanisms. 3. Find taint-style vulnerabilities (SQLi, XSS, …) using static analysis, leverage AI (tree-of-thoughts, ReAct) to find business logic flaws and AuthN/AuthZ issues. 4. To validate findings, they use the Monte Carlo Tree Self-refine (MCTSr) algorithm. More details and graphs here: https://lnkd.in/gZWjVV7p #cybersecurity #ai

🛡️ Advanced Threat Modeling: Methodologies & Implementation Strategies Threat modeling is one of the most powerful yet underutilized practices in cybersecurity. As systems grow more complex and interconnected, the ability to anticipate, analyze, and mitigate threats before they materialize is critical for building resilient architectures. That’s why I created this guide: Advanced Threat Modeling: Methodologies and Implementation Strategies for Security Architects. 📌 What’s inside? • Fundamentals & Core Principles → Systematic, attacker-focused, risk-prioritized approaches • Methodologies Deep-Dive → STRIDE, PASTA, DREAD, Attack Trees • Practical Techniques → Data Flow Diagrams (DFDs), trust boundaries, STRIDE-per-element analysis • Integration with DevSecOps → Threat Model as Code, validation with security testing • Tool Comparisons → OWASP Threat Dragon, Microsoft TMT, IriusRisk, ThreatModeler • Case Studies → Financial services & healthcare implementations • Future Trends → AI-enhanced modeling, supply chain focus, cloud-native approaches 💡 Key takeaway: Threat modeling isn’t just a security exercise—it’s a business enabler. Done right, it reduces vulnerabilities, lowers remediation costs, and embeds security into the development lifecycle. 👉 Download the full paper and let’s discuss: How are you integrating threat modeling into your DevSecOps pipelines? #ThreatModeling #CyberSecurity #DevSecOps #RiskManagement #Architecture #ApplicationSecurity #InfoSec #SecurityArchitect

🚨 Structured Analytic Techniques (SATs) 🚨 In cybersecurity science roles, dealing with complexity, ambiguity, and uncertainty is a daily challenge. That’s where Structured Analytic Techniques (SATs) come into play! SATs are systematic, evidence-based methods designed to enhance decision-making and problem-solving. They help cybersecurity professionals by: ✅ Exposing Assumptions ✅ Challenging Cognitive Biases ✅ Encouraging Creativity ✅ Improving Transparency From threat intelligence analysis to incident response and risk management, SATs empower cybersecurity professionals across all roles to handle the toughest challenges effectively. 🔍 Why are SATs crucial in cybersecurity science? * They counter biases like confirmation bias and groupthink. * They provide structured approaches to model adversary behavior and explore alternative scenarios. * They foster collaboration by creating shared frameworks for complex problem-solving. SATs align perfectly with the 7 core themes of cybersecurity science, enhancing measurable security, agility, human factors, and more. For example: * Risk Analysts use SATs like Indicators of Change to assess rare, high-impact scenarios. * Forensic Investigators leverage ACH to ensure all possible explanations for evidence are rigorously tested. * SOC Analysts employ techniques like brainstorming and red-teaming to remain resilient under pressure. As cyber threats evolve, SATs will continue to be an essential part of our toolkit, enabling us to outthink adversaries, adapt to change, and protect our digital ecosystems with confidence. 💡 Let’s embrace these techniques to strengthen our analytical rigor and make more defensible, informed decisions. Curious to dive deeper? Check out the article! 🚀