Alfred, Find the Attacker - A Primer on AI and ML Applications in the IT Security Domain
Explore AI and ML applications in IT security, covering classification, outlier detection, and user behavior analysis for enhanced protection and threat detection.
Why the LinkedIn Hack Could Be Your Biggest AppSec Threat
Explore account takeover threats, their long-term impact, and effective defense strategies. Learn about hacking techniques, credential stuffing, and tools to protect against evolving cyber risks.
Detect Unknown Threats with User and Entity Behavioral Analytics in Azure Sentinel
Explore Azure Sentinel's User and Entity Behavioral Analytics to identify anomalies, detect threats, and enhance threat hunting and response through behavioral analytics and entity profiling.
Defense Against Rapidly Morphing DDOS
Explore defenses against rapidly evolving DDoS attacks, examining the ProtonMail case and evaluating mitigation strategies for SOC operators facing dynamic, high-speed threats.
InfoSec Deep Learning in Action
Explore deep learning applications in cybersecurity, including use cases, data pipelines, algorithms, and real-world deployment challenges. Learn how AI can combat ransomware and other cyber threats.
SCADA Attack Detection
Explore ICS/SCADA attack detection techniques, relevant logs, and ML-based anomaly detection methods. Includes practical demonstrations and insights for enhancing industrial cybersecurity.
Mushrooms Not Honey - Making Deceptive Defenses More Toxic for Attackers
Explore innovative deceptive defense strategies inspired by attacker techniques. Learn about the Troop suite and its approach to achieving deterrence through dynamic and toxic defensive measures.
Stealthier Attacks and Smarter Defending With TLS Fingerprinting
Explore TLS fingerprinting for enhanced attack stealth and improved defense strategies. Learn techniques for anomaly detection, anti-forensics, and fingerprint-based routing in network security.
Scalable Anomaly Detection - With Zero Machine Learning
Explore Netflix's innovative approach to detecting anomalies in complex distributed systems using stream processing and simple algorithms, without relying on machine learning.
Democratizing AI - Back-Fitting End-to-End Machine Learning
Explore LinkedIn's journey in democratizing AI, enhancing modeler efficiency, and scaling machine learning across engineering teams through innovative infrastructure and practices.
CloudCluster - Unearthing the Functional Structure of a Cloud Service
Unearths cloud service functional structure using VM-to-VM traffic matrix clustering. Aids management by revealing design patterns, enabling cost reduction, and identifying anomalies and misconfigurations.
Spike Detection in Alert Correlation at LinkedIn
Discover how LinkedIn uses spike detection in alert correlation to quickly identify root causes of outages in their complex microservices architecture, reducing false positives and engineer toil.
Owfuzz - WiFi Nightmare
Explore WiFi security vulnerabilities and attack surfaces using Owfuzz, a fuzzing tool for detecting protocol flaws in WiFi implementations, including WIFI6 and WPA3.
Five-Sigma Network Events - and How to Find Them
Discover techniques for identifying unusual network behavior using Python. Learn to create custom tools for anomaly detection, scaling approaches, and data analysis in complex network systems.
Hunting Supply Chain Threats Using Anomaly Detection
Explore a case study on detecting supply chain threats through anomaly detection in Cloud API logs, offering insights into advanced cybersecurity techniques.
An Incremental Learner for Language-Based Anomaly Detection in XML
Incremental learning approach for XML anomaly detection, inferring types and datatypes to create extension point-free automaton for improved stream validation and attack prevention.
Making & Breaking Machine Learning Anomaly Detectors in Real Life
Explore machine learning anomaly detectors in cybersecurity, their creation, implementation, and potential vulnerabilities. Gain insights into real-world applications and challenges in this cutting-edge field.
Realtime-Cognitive IoT Using DeepLearning and Online Learning
Explore DeepLearning4J on Apache Spark for IoT anomaly detection using LSTM autoencoder neural networks. Learn scalable machine learning techniques for real-time cognitive IoT applications.
Anomaly Detection in Real Time - Simplicity Is the Ultimate Sophistication
Aprenda a detectar anomalias em tempo real usando técnicas simples e eficazes. Descubra como criar um modelo estatístico para monitorar tráfego web, eventos de busca e cliques em anúncios.
The Top 5 Things I Learned While Building Anomaly Detection Algorithms for IT Ops
Insights on building effective anomaly detection algorithms for IT operations, covering common challenges, Gaussian techniques, and non-parametric methods for improved signal extraction from metric data.
Apache Kafka and KSQL in Action - Let's Build a Streaming Data Pipeline
Build real-time streaming data pipelines using Apache Kafka, Kafka Connect, and KSQL. Learn to filter events, enrich data streams, and perform streaming aggregates for anomaly detection.