System for anomaly detection on can bus data with sparse and low rank decomposition of transfer entropy matrix

A low-rank decomposition and entropy transfer technology, applied in transmission systems, digital transmission systems, bus networks, etc.

Active Publication Date: 2019-04-26
HRL LAB
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  • Abstract
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  • Application Information

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Problems solved by technology

However, detecting additional insertions can be more challenging if normal groupings are not periodic
Also, their method is unlikely to be applicable to other types of attacks, such as changing the order of packets

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  • System for anomaly detection on can bus data with sparse and low rank decomposition of transfer entropy matrix
  • System for anomaly detection on can bus data with sparse and low rank decomposition of transfer entropy matrix
  • System for anomaly detection on can bus data with sparse and low rank decomposition of transfer entropy matrix

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Embodiment Construction

[0027] The present invention relates to an anomaly detection system, and more particularly, to a system for detecting anomalies with respect to CAN bus data utilizing sparse and low-rank decomposition. The following description is presented to enable one of ordinary skill in the art to make and use the invention, and to incorporate it in the context of a particular application. Various modifications and multiple uses for different application aspects will be apparent to those skilled in the art, and the general principles defined herein may be applied to a wide variety of aspects. Thus, the invention is not intended to be limited to the aspects presented but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

[0028] In the following detailed description, numerous specific details are set forth in order to provide a more thorough understanding of the present invention. It will be apparent, however, to those skilled in the ar...

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Abstract

Described is a system for detecting cyber intrusions based on analysis of network traffic. During operation, the system performs a statistical analysis of message tuning on network traffic to producea temporal dependency matrix representative of temporal dependency between different message types in the network traffic. The sets of temporal dependency matrices are decomposed into component matrices, where at least one component matrix represents typical properties of these matrices and at least one other component matrix represents atypical properties of the matrices. A new temporal dependency matrix is generated based on new network traffic. Finally, anomalous behavior is detected in the new network traffic by comparing component matrices of the new temporal dependency matrix, with component matrices of the temporal dependency matrices tinder normal operating conditions.

Description

[0001] government rights [0002] This invention was made with Government support under US Government Contract No. D15PC00223, entitled "Side Channel Causal Analysis for Design of Cyber-Physical Security." The government has certain rights in this invention. [0003] Cross References to Related Applications [0004] This is US Nonprovisional Patent Application Serial No. 62 / 405,716 filed October 7, 2016, the entire contents of which are hereby incorporated by reference. technical field [0005] The present invention relates to an anomaly detection system, and more particularly, to a system for detecting anomalies with respect to CAN bus data utilizing sparse and low-rank decomposition. Background technique [0006] Anomaly detection is a process that can detect abnormal data to prevent attacks or intrusions of malicious data. Many known attacks against cars involve some form of spoofing or altering CAN bus messages. For example, if an attacker can put another module into...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): H04L29/06H04L12/40
CPCH04L12/40H04L63/1425H04W4/40H04L2012/40215H04L9/002H04L2209/84H04W12/126G06F13/3625H04L1/0681H04L63/0218
Inventor 倪康宇D·W·佩顿
Owner HRL LAB
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