17,911 research outputs found
Side-channel based intrusion detection for industrial control systems
Industrial Control Systems are under increased scrutiny. Their security is
historically sub-par, and although measures are being taken by the
manufacturers to remedy this, the large installed base of legacy systems cannot
easily be updated with state-of-the-art security measures. We propose a system
that uses electromagnetic side-channel measurements to detect behavioural
changes of the software running on industrial control systems. To demonstrate
the feasibility of this method, we show it is possible to profile and
distinguish between even small changes in programs on Siemens S7-317 PLCs,
using methods from cryptographic side-channel analysis.Comment: 12 pages, 7 figures. For associated code, see
https://polvanaubel.com/research/em-ics/code
Securing Real-Time Internet-of-Things
Modern embedded and cyber-physical systems are ubiquitous. A large number of
critical cyber-physical systems have real-time requirements (e.g., avionics,
automobiles, power grids, manufacturing systems, industrial control systems,
etc.). Recent developments and new functionality requires real-time embedded
devices to be connected to the Internet. This gives rise to the real-time
Internet-of-things (RT-IoT) that promises a better user experience through
stronger connectivity and efficient use of next-generation embedded devices.
However RT- IoT are also increasingly becoming targets for cyber-attacks which
is exacerbated by this increased connectivity. This paper gives an introduction
to RT-IoT systems, an outlook of current approaches and possible research
challenges towards secure RT- IoT frameworks
Deep Predictive Coding Neural Network for RF Anomaly Detection in Wireless Networks
Intrusion detection has become one of the most critical tasks in a wireless
network to prevent service outages that can take long to fix. The sheer variety
of anomalous events necessitates adopting cognitive anomaly detection methods
instead of the traditional signature-based detection techniques. This paper
proposes an anomaly detection methodology for wireless systems that is based on
monitoring and analyzing radio frequency (RF) spectrum activities. Our
detection technique leverages an existing solution for the video prediction
problem, and uses it on image sequences generated from monitoring the wireless
spectrum. The deep predictive coding network is trained with images
corresponding to the normal behavior of the system, and whenever there is an
anomaly, its detection is triggered by the deviation between the actual and
predicted behavior. For our analysis, we use the images generated from the
time-frequency spectrograms and spectral correlation functions of the received
RF signal. We test our technique on a dataset which contains anomalies such as
jamming, chirping of transmitters, spectrum hijacking, and node failure, and
evaluate its performance using standard classifier metrics: detection ratio,
and false alarm rate. Simulation results demonstrate that the proposed
methodology effectively detects many unforeseen anomalous events in real time.
We discuss the applications, which encompass industrial IoT, autonomous vehicle
control and mission-critical communications services.Comment: 7 pages, 7 figures, Communications Workshop ICC'1
On Ladder Logic Bombs in Industrial Control Systems
In industrial control systems, devices such as Programmable Logic Controllers
(PLCs) are commonly used to directly interact with sensors and actuators, and
perform local automatic control. PLCs run software on two different layers: a)
firmware (i.e. the OS) and b) control logic (processing sensor readings to
determine control actions). In this work, we discuss ladder logic bombs, i.e.
malware written in ladder logic (or one of the other IEC 61131-3-compatible
languages). Such malware would be inserted by an attacker into existing control
logic on a PLC, and either persistently change the behavior, or wait for
specific trigger signals to activate malicious behaviour. For example, the LLB
could replace legitimate sensor readings with manipulated values. We see the
concept of LLBs as a generalization of attacks such as the Stuxnet attack. We
introduce LLBs on an abstract level, and then demonstrate several designs based
on real PLC devices in our lab. In particular, we also focus on stealthy LLBs,
i.e. LLBs that are hard to detect by human operators manually validating the
program running in PLCs. In addition to introducing vulnerabilities on the
logic layer, we also discuss countermeasures and we propose two detection
techniques.Comment: 11 pages, 14 figures, 2 tables, 1 algorith
- …