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

Efficient template attacks

This is the accepted manuscript version. The final published version is available from http://link.springer.com/chapter/10.1007/978-3-319-08302-5_17.Template attacks remain a powerful side-channel technique to eavesdrop on tamper-resistant hardware. They model the probability distribution of leaking signals and noise to guide a search for secret data values. In practice, several numerical obstacles can arise when implementing such attacks with multivariate normal distributions. We propose efficient methods to avoid these. We also demonstrate how to achieve significant performance improvements, both in terms of information extracted and computational cost, by pooling covariance estimates across all data values. We provide a detailed and systematic overview of many different options for implementing such attacks. Our experimental evaluation of all these methods based on measuring the supply current of a byte-load instruction executed in an unprotected 8-bit microcontroller leads to practical guidance for choosing an attack algorithm.Omar Choudary is a recipient of the Google Europe Fellowship in Mobile Security, and this research is supported in part by this Google Fellowship

Template attacks on different devices

Template attacks remain a most powerful side-channel technique to eavesdrop on tamper-resistant hardware. They use a profiling step to compute the parameters of a multivariate normal distribution from a training device and an attack step in which the parameters obtained during profiling are used to infer some secret value (e.g. cryptographic key) on a target device. Evaluations using the same device for both profiling and attack can miss practical problems that appear when using different devices. Recent studies showed that variability caused by the use of either different devices or different acquisition campaigns on the same device can have a strong impact on the performance of template attacks. In this paper, we explore further the effects that lead to this decrease of performance, using four different Atmel XMEGA 256 A3U 8-bit devices. We show that a main difference between devices is a DC offset and we show that this appears even if we use the same device in different acquisition campaigns. We then explore several variants of the template attack to compensate for these differences. Our results show that a careful choice of compression method and parameters is the key to improving the performance of these attacks across different devices. In particular we show how to maximise the performance of template attacks when using Fisher's Linear Discriminant Analysis or Principal Component Analysis. Overall, we can reduce the entropy of an unknown 8-bit value below 1.5 bits even when using different devices.Omar Choudary is a recipient of the Google Europe Fellowship in Mobile Security, and this research is supported in part by this Google Fellowship. The opinions expressed in this paper do not represent the views of Google unless otherwise explicitly stated.This is the author accepted manuscript. The final version is available from Springer at http://link.springer.com/chapter/10.1007%2F978-3-319-10175-0_13

Intelligent Log Analysis for Anomaly Detection

Computer logs are a rich source of information that can be analyzed to detect various issues. The large volumes of logs limit the effectiveness of manual approaches to log analysis. The earliest automated log analysis tools take a rule-based approach, which can only detect known issues with existing rules. On the other hand, anomaly detection approaches can detect new or unknown issues. This is achieved by looking for unusual behavior different from the norm, often utilizing machine learning (ML) or deep learning (DL) models. In this project, we evaluated various ML and DL techniques used for log anomaly detection. We propose a hybrid neural network (NN) we call CausalConvLSTM for modeling log sequences, which takes advantage of both Convolutional Neural Network and Long Short-Term Memory Network\u27s strengths. Furthermore, we evaluated and proposed a concrete strategy for retraining NN anomaly detection models to maintain a low false-positive rate in a drifting environment

Efficient, portable template attacks

Template attacks recover data values processed by tamper-resistant devices from side-channel waveforms, such as supply-current fluctuations (power analysis) or electromagnetic emissions. They first profile a device to generate multivariate statistics of the waveforms emitted for each of a set of known processed values, which then identify maximum-likelihood candidates of unknown processed values during an attack. We identify several practical obstacles arising in the implementation of template attacks, ranging from numerical errors to the incompatibility of templates across different devices, and propose and compare several solutions. We identify pooled covariance matrices and prior dimensionality reduction through Fisher's Linear Discriminant Analysis as particularly efficient and effective, especially where many attack traces can be acquired. We evaluate alternative algorithms not only for the task of recovering key bytes from a hardware implementation of the Advanced Encryption Standard; we even reconstruct the value transferred by an individual byte-load instruction, with success rates reaching 85% (or a guessing entropy of less than a quarter bit remaining) after 1000 attack traces, thereby demonstrating direct eavesdropping of 8-bit parallel data lines. Using different devices during the profiling and attack phase can substantially reduce the effectiveness of template attacks. We demonstrate that the same problem can also occur across different measurement campaigns with the same device and that DC offsets (e.g. due to temperature drift) are a significant cause. We improve the portability of template parameters across devices by manipulating the DC content of the eigenvectors that form the projection matrix used for dimensionality reduction of the waveforms

Efficient Stochastic Methods: Profiled Attacks Beyond 8 Bits

Template attacks and stochastic models are among the most powerful side-channel attacks. However, they can be computationally expensive when processing a large number of samples. Various compression techniques have been used very successfully to reduce the data dimensionality prior to applying template attacks, most notably Principal Component Analysis (PCA) and Fisher’s Linear Discriminant Analysis (LDA). These make the attacks more efficient computationally and help the profiling phase to converge faster. We show how these ideas can also be applied to implement stochastic models more efficiently, and we also show that they can be applied and evaluated even for more than eight unknown data bits at once.This is the author accepted manuscript. The final version is available from Springer via http://dx.doi.org/10.1007/978-3-319-16763-3_