AI Threat Detection for Unknown and Large-Scale Attacks

Posted on Feb 16, 2026 in Computer Engineering

Capabilities of AI for Unknown or Large-Scale Attacks

AI capabilities help networks detect attacks that evade traditional signature-based systems (unknown attacks) and handle the sheer volume of modern threats (scale) through the following mechanisms:

Behavioral Modeling

AI builds baseline (peacetime) models of every device, application, and service to understand normal behavior. This allows it to detect zero-day attacks (unknown threats) because they deviate from the established norm rather than relying solely on known attack signatures.

Global Visibility and Big Data Analytics

For large-scale attacks like botnets, AI shifts from local packet inspection (which fails when traffic is encrypted or comes from valid IPs) to a big-data approach. It crawls the internet to build a global map of connected devices and correlates this with network telemetry. This enables identification of large-scale botnet attacks by analyzing traffic patterns at a global scale.

Real-time Pattern Recognition

AI algorithms (for example, neural networks) can process vast amounts of data in real time to identify anomalies, such as abnormal spikes in traffic volume or subtle flow characteristics indicative of DDoS attacks—conditions that traditional batch processing might miss.

Examples of SIEM Rule Evasion in Enterprise Networks

Attackers can evade SIEM (Security Information and Event Management) rules by obfuscating command lines while maintaining the original malicious intent. Common evasion techniques include:

Insertion: Injecting valid but unnecessary characters into commands to break simple string matching. Example: schtasks.exe /"create" instead of /create.
Substitution: Replacing characters or flags with functional equivalents that the rule does not expect. Example: curl -0 instead of curl --remote-name.
Omission: Removing optional file extensions or parameters. Example: cscript evil.vbs instead of cscript.exe evil.vbs.
Reordering: Changing the order of command-line arguments to avoid pattern matches. Example: procdump.exe -ma ls instead of procdump.exe ls -ma.
Recoding: Changing the format of data types, such as using integer representations for IP addresses instead of the standard dotted-decimal format.

AI Enumeration of Evasion Rules and Difficulties

No, AI typically does not try to enumerate all possible ways to rewrite rules. Instead of enumeration, systems like AMIDES (Adaptive Misuse Detection System) use supervised learning to infer the intent of an attack by training on the relationship between malicious rules and benign events, rather than only on the specific text of the rule.

Difficulties of the enumeration approach

Combinatorial explosion: The number of possible ways to rewrite and obfuscate a command is effectively infinite. Attempting to statically list every variation (for example, every possible placement of a quote or parameter order) creates an unmanageable number of rules.
Blind spots: Static enumeration inevitably leaves gaps. As defenders write rules for known evasions, adversaries invent new obfuscation techniques, resulting in a perpetual cat-and-mouse game that static rules cannot win.

AI Techniques in “From Specification to Bug Hunting”

The paper “From One Thousand Pages of Specification to Unveiling Hidden Bugs” utilizes Large Language Model (LLM) assisted fuzzing (specifically a tool called mGPTFuzz) to achieve better protocol coverage:

FSM Extraction

It uses LLMs to process massive technical specifications (over 1,000 pages) and extract Finite State Machines (FSMs). This allows the fuzzer to understand the valid states and transitions of an IoT device.

Stateful Fuzzing

By leveraging the extracted FSMs, the AI can generate command sequences that navigate complex device states (stateful fuzzing). This enables discovery of deep, logic-based bugs that are only triggered when the device is in a specific state, which traditional stateless fuzzers would miss.

Semantic Meanings in AI Detection Results

The authors embed semantic meaning to bridge the gap between “black box” AI outputs and human understanding, improving security enforcement in the following ways:

1. XNIDS (Explaining Deep Learning-based NIDS)

Semantic information: The system maps deep-learning features back to “Term Information” and “Important Features”—specific, human-readable network concepts like IP addresses, protocols (TCP/UDP), and ports.

Improvement: This low semantic gap allows the system to generate actionable rules. Instead of just alerting “Anomaly detected,” it can automatically produce precise firewall rules (for example, iptables or OpenFlow) to block the specific malicious traffic flow without disrupting benign services. It also aids troubleshooting by explaining why a packet was flagged.

2. BEAM (Semantics-Aware Routing Anomaly Detection)

Semantic information: BEAM embeds the routing role of an Autonomous System (AS). It characterizes an AS based on its proximity (who it connects to) and hierarchy (provider, peer, or customer relationships) to define expected behavior.

Improvement: By understanding these semantic roles, the AI can perform unsupervised detection of anomalies (for example, BGP hijacks or leaks) without needing labeled training data. It produces interpretable reports that can pinpoint which AS is behaving out of character (for example, a small customer AS suddenly acting like a Tier-1 provider), enabling faster and more accurate incident response.