Cryptocurrency hacks list
Cryptocurrency risk scoring services list
RugWatch is a real-time Solana rugpull and honeypot detection bot. It monitors new token launches across major DEX ecosystems, analyzes on-chain risk signals (authorities, liquidity, trading rules), and alerts via Telegram and Discord with a clear risk score.
A Go-based IP security protection package providing real-time threat detection, dynamic risk scoring, device fingerprinting, and multi-layered security mechanisms.
A deep technical article exploring how AI, feature engineering, and static smart-contract analysis uncover rugpull risks before humans detect them. Covers Solidity pattern mining, mint abuse detection, blacklist/fee manipulation signals, ML-inspired scoring models, and how to quantify ERC-20 token scam probability.
A complete Web3 security toolkit combining AI-powered token auditing, ML-based deployer reputation scoring, and live Etherscan V2 data. Includes static analysis for rugpull detection, RandomForest reputation modeling, contract-fetching automation, and Solidity on-chain registries for transparent, reproducible security insights.
A hybrid Solidity + Python security toolkit that analyzes ERC-20 token contracts using static pattern extraction and ML-inspired scoring. Detects mint backdoors, blacklist controls, fee manipulation, trading locks, and rugpull mechanics. Outputs interpretable risk scores, labels, and structured features for deeper analysis.
AI-powered real-time smart contract scanner that connects Machine Learning with Etherscan V2 to analyze newly deployed contracts instantly. Fetches verified Solidity code, performs static risk analysis, computes ML-driven deployer trust scores, and generates full security intelligence pipelines for Web3 threat detection.
A deep technical exploration of how malicious smart-contract developers weaponize fee logic in ERC-20 tokens. Covers dynamic tax flipping, hidden sell traps, fee obfuscation, whitelist-based bypasses, liquidity-drain funnels, attack timelines, forensic analysis, mathematical modeling, and ML-powered detection strategies for tax abuse.
A research-grade framework for extracting, classifying, and analyzing the “genetic” behavior of smart contract tokens. Identifies economic traits, supply mutations, fee patterns, permission risks, upgradeability vectors, and scam species using a structured gene taxonomy with risk scoring, HTML reports, and token comparison tools.
OpenSiteTrust is an open, explainable, and reusable website scoring ecosystem
National-scale vaccine distribution system deployed during COVID-19. Used by Iranian Ministry of Health to optimize allocation for 90 million people. VBA-based optimization engine for crisis response decision support
Python-based enterprise risk engine for log analysis, anomaly detection, KRI-based scoring and automated narrative reporting. Designed for SMEs, cybersecurity teams and governance functions as part of UK Global Talent technical evidence. A collaborative project by Ibrahim Akinyera (AI/ML Lead) and Busayo Odukoya (Technology Risk Expert).
A collection of medical calculations in Python. Example: MELD score for liver disease.
Actuarial data science project analyzing insurance claims to identify potential fraud and assess customer risk. Includes Python preprocessing, feature engineering, and a stakeholder-ready Tableau dashboard.
A deep exploration of how human psychology shapes fraud behavior and how those patterns become measurable signals in transaction data. This article reveals the behavioral, cognitive, and economic forces behind fraud, explaining how ML models detect deviations, anomalies, and intent hidden within financial transactions.
A multi-agent AI system using Google AI Agent SDK and Gemini to analyze real-time route safety (crime, weather, lighting) and optimize navigation.
📈 Automate trading with this arbitrage bot for passive income and explore the potential of crypto investments effortlessly.
Fake review detection using TF-IDF & SVM (AUC 0.98), plus Counterfeit Risk Score with clustering & anomaly detection.
End-to-end ML demo for clinical risk scoring on synthetic tabular patient data (data → features → models → metrics → FastAPI /predict).
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