Distributed Network Intrusion Detection An Immunological Approach Steven Hofmeyr Stephanie Forrest Patrik D’haeseleer Dept. of Computer Science University of New Mexico Albuquerque, NM { steveah, forrest,

Introduction Intrusion detection: –Assume that systems are not secure. –Attempt to detect violations of security policy (intrusions) by monitoring and analyzing system behavior. –Construct a model of normal behavior and look for deviations from the model (anomaly detection). Building the model (defining self): –TCP/IP traffic over a broadcast LAN. –Based on Network Security Monitor (NSM). Every computer on the network should participate in IDS: –Distributed detection –Use negative-selection algorithm Diversity of protection: –Permutation masks

Background: Defining Self The right approach: –Anomaly detection –Sparsely connected graph –Normal patterns reasonably stable –Attackers highly likely to perturb graph Disadvantages: –Heavyweight –Single point of failure –Not scalable NSM: Network Security Monitor (UCDavis) {Mukherjee et al. Network Intrusion Detection. IEEE Network, pp26-41, 1994}

The Biological Viewpoint Self (proteins) = normal datapath triples Nonself (proteins) = triples generated during an attack Universe = Self  Nonself Anomaly detection: –Detection system trained on self –Detection system classifies new triples as self (normal) or nonself (anomalous) NSM: a single monolithic detector matching self (positive detection)

How the Immune System Distributes Detection Advantages of distributed negative detection: –Localized (no communication costs) –Scalable –Tunable –Robust (no single point of failure) –Negative selection algorithm minimizes false positives Immune system: Many small detectors matching nonself (negative detection).

The Negative Selection Algorithm 1.Randomly generate a detector string. 2.Does the detector string match self? NO YES 3.If no, accept If yes, go to 1. (regenerate). ACCEPT REJECT Results in a set of valid detectors

Applying Negative Detection to Network Traffic Representation: –SYN packet triples mapped to 49-bit strings Generalized detection: –Partial matching with r-contiguous bits rule Consequences of Partial Matching: –Advantage: Lightweight (few detectors per host) –Disadvantage: Holes limit detection Holes

Problem: Holes limit detection for any partial match rule. Solution: A different permutation mask for each host. Overcoming Holes Result: In the broadcast network, detection is limited by the intersection of all hole sets.

Experimental Setup UNM CS subnet of 50 machines on a switched segment. – bit string detectors per machine Training set (self): –Collected over 43 days – TCP SYN packets –3763 unique binary self strings Normal test set (supposedly self): –Collected over 7 days – TCP SYN packets –626 unique binary self strings Abnormal test set (nonself): –8 different incidents, 7 real occurrences, 1 synthetic –Real abnormal behavior includes: massive portscanning, limited probing, address-space probing, local host compromise –Synthetic: 200 random connections between internal (LAN) hosts

Experimental Results Low false positives: –P(false positive per self string) = –55 strings, but only 10 unique –Effectively: under 2 false alarms per day High detection rates with few detectors –100% successful detection: 8 out of 8 abnormal incidents detected –Only 100 detectors per host Permutation masks improve detection –Up to an order of magnitude improvement –Overcomes hole limitation Normal is reasonably stable.

The Problem of Incomplete Self Sets (Suppose the training set is incomplete) Activation threshold: –Detector is not activated on every match. –Must have exceeded x matches before activation. –No time horizon. –Helps with stealth attacks (distributed in time). –Reduced false positives by an order of magnitude. Adaptive activation: –Tune local activation thresholds dynamically. –Whenever a detector matches its first pattern, the activation threshold for that computer is reduced by 1. –Has a time horizon (threshold gradually returns to default value). –Hypothesized to help with distributed coordinated attacks.

Experimental Results Intrusions with and without permutation masks

Experimental and Theoretical Results: Permutation Masks Overcome the Hole Limit

Pushing the Immune Metaphor The analogy thus far: –Distributed networks and immunology – Combining negative detection and network intrusion detection –Diversity via permutation masks For the future: –Distributed generation of detectors –Dynamic detector sets –Adaptation and memory (misuse detection)