Multi-IF : An Approach to Anomaly Detection in Self-Driving Systems

by   Kun Cheng, et al.

Autonomous driving vehicles (ADVs) are implemented with rich software functions and equipped with many sensors, which in turn brings broad attack surface. Moreover, the execution environment of ADVs is often open and complex. Hence, ADVs are always at risk of safety and security threats. This paper proposes a fast method called Multi-IF, using multiple invocation features of system calls to detect anomalies in self-driving systems. Since self-driving functions take most of the computation resources and upgrade frequently, Multi-IF is designed to work under such resource constraints and support frequent updates. Given the collected sequences of system calls, the combination of different syntax patterns is used to analyze and construct feature vectors of those sequences. By taking the feature vectors as inputs, one-class support vector machine is adopted to determine whether the current sequence of system calls is abnormal, which is trained with the feature vectors from the normal sequences. The evaluations on both simulated and real data prove that the proposed method is effective in identifying the abnormal behavior after minutes of feature extraction and training. Further comparisons with the existing methods on the ADFA-LD data set also validate that the proposed approach achieves a higher accuracy with less time overhead.


page 18

page 19

page 20

page 23


A Benchmark for Unsupervised Anomaly Detection in Multi-Agent Trajectories

Human intuition allows to detect abnormal driving scenarios in situation...

Towards Defensive Autonomous Driving: Collecting and Probing Driving Demonstrations of Mixed Qualities

Designing or learning an autonomous driving policy is undoubtedly a chal...

Anomaly Detection in Multi-Agent Trajectories for Automated Driving

Human drivers can recognise fast abnormal driving situations to avoid ac...

Guardauto: A Decentralized Runtime Protection System for Autonomous Driving

Due to the broad attack surface and the lack of runtime protection, pote...

Large Scale Autonomous Driving Scenarios Clustering with Self-supervised Feature Extraction

The clustering of autonomous driving scenario data can substantially ben...

Composition and Application of Current Advanced Driving Assistance System: A Review

Due to the growing awareness of driving safety and the development of so...

Scenario-Based Test Reduction and Prioritization for Multi-Module Autonomous Driving Systems

When developing autonomous driving systems (ADS), developers often need ...

Please sign up or login with your details

Forgot password? Click here to reset