Kevin Jones

Giulio Zizzo, Chris Hankin, Sergio Maffeis et al.

Neural networks are increasingly used for intrusion detection on industrial control systems (ICS). With neural networks being vulnerable to adversarial examples, attackers who wish to cause damage to an ICS can attempt to hide their attacks from detection by using adversarial example techniques. In this work we address the domain specific challenges of constructing such attacks against autoregressive based intrusion detection systems (IDS) in an ICS setting. We model an attacker that can compromise a subset of sensors in a ICS which has a LSTM based IDS. The attacker manipulates the data sent to the IDS, and seeks to hide the presence of real cyber-physical attacks occurring in the ICS. We evaluate our adversarial attack methodology on the Secure Water Treatment system when examining solely continuous data, and on data containing a mixture of discrete and continuous variables. In the continuous data domain our attack successfully hides the cyber-physical attacks requiring 2.87 out of 12 monitored sensors to be compromised on average. With both discrete and continuous data our attack required, on average, 3.74 out of 26 monitored sensors to be compromised.

Giulio Zizzo, Chris Hankin, Sergio Maffeis et al.

Deep learning methods have shown state of the art performance in a range of tasks from computer vision to natural language processing. However, it is well known that such systems are vulnerable to attackers who craft inputs in order to cause misclassification. The level of perturbation an attacker needs to introduce in order to cause such a misclassification can be extremely small, and often imperceptible. This is of significant security concern, particularly where misclassification can cause harm to humans. We thus propose Deep Latent Defence, an architecture which seeks to combine adversarial training with a detection system. At its core Deep Latent Defence has a adversarially trained neural network. A series of encoders take the intermediate layer representation of data as it passes though the network and project it to a latent space which we use for detecting adversarial samples via a $k$-nn classifier. We present results using both grey and white box attackers, as well as an adaptive $L_{\infty}$ bounded attack which was constructed specifically to try and evade our defence. We find that even under the strongest attacker model that we have investigated our defence is able to offer significant defensive benefits.

Matilda Rhode, Pete Burnap, Kevin Jones

Static malware analysis is well-suited to endpoint anti-virus systems as it can be conducted quickly by examining the features of an executable piece of code and matching it to previously observed malicious code. However, static code analysis can be vulnerable to code obfuscation techniques. Behavioural data collected during file execution is more difficult to obfuscate, but takes a relatively long time to capture - typically up to 5 minutes, meaning the malicious payload has likely already been delivered by the time it is detected. In this paper we investigate the possibility of predicting whether or not an executable is malicious based on a short snapshot of behavioural data. We find that an ensemble of recurrent neural networks are able to predict whether an executable is malicious or benign within the first 5 seconds of execution with 94% accuracy. This is the first time general types of malicious file have been predicted to be malicious during execution rather than using a complete activity log file post-execution, and enables cyber security endpoint protection to be advanced to use behavioural data for blocking malicious payloads rather than detecting them post-execution and having to repair the damage.