Imperial College London

Prof. Deeph Chana

Faculty of EngineeringInstitute for Security Science & Technology

Co-Director of Institute for Security Science & Technology
 
 
 
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Contact

 

+44 (0)20 7594 6397d.chana

 
 
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Location

 

Sherfield BuildingSouth Kensington Campus

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Summary

 

Publications

Publication Type
Year
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7 results found

Feng C, Li T, Chana D, 2017, Multi-level Anomaly Detection in Industrial Control Systems via Package Signatures and LSTM Networks, 2017 47th Annual IEEE/IFIP International Conference on Dependable Systems and Networks (DSN), Pages: 261-272

Conference paper

Reid CB, Betcke MM, Chana D, Speller RDet al., 2011, The development of a pseudo-3D imaging system (tomosynthesis) for security screening of passenger baggage, NUCLEAR INSTRUMENTS & METHODS IN PHYSICS RESEARCH SECTION A-ACCELERATORS SPECTROMETERS DETECTORS AND ASSOCIATED EQUIPMENT, Vol: 652, Pages: 108-111, ISSN: 0168-9002

Journal article

Ignatyev K, Munro PRT, Chana D, Speller RD, Olivo Aet al., 2011, A New Generation of X-ray Baggage Scanners Based on a Different Physical Principle, MATERIALS, Vol: 4, Pages: 1846-1860, ISSN: 1996-1944

Journal article

Ignatyev K, Munro PRT, Chana D, Speller RD, Olivo Aet al., 2011, Coded apertures allow high-energy x-ray phase contrast imaging with laboratory sources, JOURNAL OF APPLIED PHYSICS, Vol: 110, ISSN: 0021-8979

Journal article

Olivo A, Chana D, Speller R, 2008, A preliminary investigation of the potential of phase contrast x-ray imaging in the field of homeland security, JOURNAL OF PHYSICS D-APPLIED PHYSICS, Vol: 41, ISSN: 0022-3727

Journal article

Powell K, Chana D, Fish D, Thompson Cet al., 1999, Restoration and frequency analysis of smeared CCD images, APPLIED OPTICS, Vol: 38, Pages: 1343-1347, ISSN: 1559-128X

Journal article

Feng C, Li T, Zhu Z, Chana Det al., A Deep Learning-based Framework for Conducting Stealthy Attacks in Industrial Control Systems

Industrial control systems (ICS), which in many cases are components ofcritical national infrastructure, are increasingly being connected to othernetworks and the wider internet motivated by factors such as enhancedoperational functionality and improved efficiency. However, set in thiscontext, it is easy to see that the cyber attack surface of these systems isexpanding, making it more important than ever that innovative solutions forsecuring ICS be developed and that the limitations of these solutions are wellunderstood. The development of anomaly based intrusion detection techniques hasprovided capability for protecting ICS from the serious physical damage thatcyber breaches are capable of delivering to them by monitoring sensor andcontrol signals for abnormal activity. Recently, the use of so-called stealthyattacks has been demonstrated where the injection of false sensor measurementscan be used to mimic normal control system signals, thereby defeating anomalydetectors whilst still delivering attack objectives. In this paper we define adeep learning-based framework which allows an attacker to conduct stealthyattacks with minimal a-priori knowledge of the target ICS. Specifically, weshow that by intercepting the sensor and/or control signals in an ICS for aperiod of time, a malicious program is able to automatically learn to generatehigh-quality stealthy attacks which can achieve specific attack goals whilstbypassing a black box anomaly detector. Furthermore, we demonstrate theeffectiveness of our framework for conducting stealthy attacks using tworeal-world ICS case studies. We contend that our results motivate greaterattention on this area by the security community as we demonstrate thatcurrently assumed barriers for the successful execution of such attacks arerelaxed.

Journal article

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