June 2019

Conference Paper

Overshadow PLC to Detect Remote Control-Logic Injection Attacks

By:
Yoo, Hyunguk; Kalle, Sushma; Smith, Jared M; Ahmed, Irfan
Journal Name:
Lecture Notes in Computer Science
Page Number:
109-132
Volume:
11543
Issue Number:
DIMVA 2019
Book Title:
Detection of Intrusions and Malware, and Vulnerability Assessment
Publication Date:
June 2019
Conference Name:
International Conference on Detection of Intrusions and Malware, and Vulnerability Assessment (DIMVA 2019)
Conference Location:
Gothenburg, Sweden
Conference Sponsor:
Special Interest Group in Security – Intrusion Detection and Response (SIDAR) of the German Informatics Society (GI)
View DOI Listing:
https://doi.org/10.1007/978-3-030-22038-9_6

Abstract

Programmable logic controllers (PLCs) in industrial control systems (ICS) are vulnerable to remote control logic injection attacks. Attackers target the control logic of a PLC to manipulate the behavior of a physical process such as nuclear plants, power grids, and gas pipelines. Control logic attacks have been studied extensively in the literature, including hiding the transfer of a control logic over the network from both packet header-based signatures, and deep packet inspection. For instance, these attacks transfer a control logic code as data, into small fragments (one-byte per packet), that are further padded with noise data. To detect control logic in ICS network traffic, this paper presents Shade, a novel shadow memory technique that observes the network traffic to maintain a local copy of the current state of a PLC memory. To analyze the memory contents, Shade employs a classification algorithm with 42 unique features categorized into five types at different semantic levels of a control logic code, such as number of rungs, number of consecutive decompiled instructions, and n-grams. We then evaluate Shade against control logic injection attacks on two PLCs, Modicon M221 and MicroLogix 1400 from two ICS vendors, Schneider electric and Allen-Bradley, respectively. The evaluation results show that Shade can detect an attack instance (i.e., identifying at least one attack packet during the transfer of a malicious control logic) accurately without any false alarms.