This is a research survey that acts like a “buyers guide plus textbook” for using AI to catch hackers. It reviews how different machine‑learning and deep‑learning techniques can watch network and system traffic, learn what normal looks like, and automatically flag or block suspicious behavior in real time.
Cyber teams are overwhelmed by huge volumes of security logs and alerts, while attackers are getting more sophisticated. Traditional, rules-based intrusion detection misses new attack patterns and generates many false alarms. This survey organizes and evaluates modern ML/DL approaches so organizations can design or select IDS solutions that detect complex and novel threats more accurately and with less manual effort.
Not a product but a survey; the defensibility lies in synthesized knowledge: comprehensive taxonomy of IDS techniques, comparative analysis of ML/DL models, and mapping to emerging cybersecurity challenges that can guide vendors and CISOs in system design and procurement.
Early Majority
Unlike a single vendor product, this is a broad survey that compares many ML and DL approaches, highlights open challenges (e.g., adversarial attacks on IDS, encrypted traffic, IoT/industrial environments), and can inform both new product architectures and upgrades to existing IDS/IPS solutions.
Think of this as a playbook for turning your IT monitoring tools into a smart “control tower” that spots problems early, understands what’s going wrong across systems, and often fixes or routes issues automatically—using ServiceNow’s AIOps capabilities as the backbone.
Think of it as an AI control tower for your IT operations: it watches logs, alerts, and metrics 24/7, spots problems early, and suggests or triggers fixes automatically so your systems stay healthy with less manual firefighting.
Think of AIOps as an always-on "control tower" for your IT systems that watches all logs, alerts, and metrics at once, spots real problems in the noise, and suggests or triggers fixes before users feel the pain.