Evolving attackers against wireless sensor networks using genetic programming

Recent hardware developments have made it possible for the Internet of Things (IoT) to be built. A wide variety of industry sectors, including manufacturing, utilities, agriculture, transportation, and healthcare are actively seeking to incorporate IoT technologies in their operations. The increased connectivity and data sharing that give IoT systems their advantages also increase their vulnerability to attack. In this study, the authors explore the automated generation of attacks using genetic programming (GP), so that defences can be tested objectively in advance of deployment. In the authors' system, the GP-generated attackers targeted publish-subscribe communications within a wireless sensor networks that was protected by an artificial immune intrusion detection system (IDS) taken from the literature. The GP attackers successfully suppressed more legitimate messages than the hand-coded attack used originally to test the IDS, whilst reducing the likelihood of detection. Based on the results, it was possible to reconfigure the IDS to improve its performance. Whilst the experiments were focussed on establishing a proof-of-principle rather than a turnkey solution, they indicate that GP-generated attackers have the potential to improve the protection of systems with large attack surfaces, in a way that is complementary to traditional testing and certification.

[1]  Rodrigo Roman,et al.  Trust management systems for wireless sensor networks: Best practices , 2010, Comput. Commun..

[2]  Malcolm I. Heywood,et al.  Evolutionary computation as an artificial attacker: generating evasion attacks for detector vulnerability testing , 2011, Evol. Intell..

[3]  Rodrigo Roman,et al.  On the features and challenges of security and privacy in distributed internet of things , 2013, Comput. Networks.

[4]  Pabitra Mohan Khilar,et al.  Optimal topological balancing strategy for performance optimisation of consensus-based clock synchronisation protocols in wireless sensor networks: a genetic algorithm-based approach , 2014, IET Wirel. Sens. Syst..

[5]  Antonio Iera,et al.  The Internet of Things: A survey , 2010, Comput. Networks.

[6]  James Decraene,et al.  Evolvable simulations applied to Automated Red Teaming: A preliminary study , 2010, Proceedings of the 2010 Winter Simulation Conference.

[7]  Giuliano Andrea Pagani,et al.  NetAttack: Co-Evolution of Network and Attacker , 2013 .

[8]  R. Steinman,et al.  The dendritic cell system and its role in immunogenicity. , 1991, Annual review of immunology.

[9]  Wenbo Liu,et al.  Routing protocol based on genetic algorithm for energy harvesting-wireless sensor networks , 2013, IET Wirel. Sens. Syst..

[10]  Uwe Aickelin,et al.  Danger Theory: The Link between AIS and IDS? , 2003, ICARIS.

[11]  Wu He,et al.  Internet of Things in Industries: A Survey , 2014, IEEE Transactions on Industrial Informatics.

[12]  J. Doye,et al.  Global Optimization by Basin-Hopping and the Lowest Energy Structures of Lennard-Jones Clusters Containing up to 110 Atoms , 1997, cond-mat/9803344.

[13]  Massimo Marchiori,et al.  Error and attacktolerance of complex network s , 2004 .

[14]  Richard Colbaugh,et al.  Predictability-oriented defense against adaptive adversaries , 2012, 2012 IEEE International Conference on Systems, Man, and Cybernetics (SMC).

[15]  Julie Greensmith,et al.  Articulation and Clarification of the Dendritic Cell Algorithm , 2006, ICARIS.

[16]  Jill Slay,et al.  Lessons Learned from the Maroochy Water Breach , 2007, Critical Infrastructure Protection.

[17]  Daniel R. Tauritz,et al.  Coevolutionary Agent-based Network Defense Lightweight Event System (CANDLES) , 2015, GECCO.

[18]  David J. John,et al.  Evolutionary based moving target cyber defense , 2014, GECCO.

[19]  Jean-Louis Lanet,et al.  Detecting Laser Fault Injection for Smart Cards Using Security Automata , 2013, SSCC.

[20]  Andreas Willig,et al.  A Gilbert-Elliot Bit Error Model and the Efficient Use in Packet Level Simulation , 1999 .

[21]  Malcolm I. Heywood,et al.  Can a good offense be a good defense? Vulnerability testing of anomaly detectors through an artificial arms race , 2011, Appl. Soft Comput..

[22]  Julie Greensmith,et al.  Greensmith, Julie and Aickelin, Uwe and Cayzer, Steve (2005) 'Introducing Dendritic Cells as a Novel Immune- Inspired Algorithm for Anomaly Detection'. In: ICARIS- , 2017 .

[23]  Elaine Shi,et al.  The Sybil attack in sensor networks: analysis & defenses , 2004, Third International Symposium on Information Processing in Sensor Networks, 2004. IPSN 2004.

[24]  Amol P. Bhondekar,et al.  Genetic Algorithm Based Node Placement Methodology For Wireless Sensor Networks , 2009 .

[25]  Deborah Estrin,et al.  Directed diffusion for wireless sensor networking , 2003, TNET.

[26]  Daniel R. Tauritz,et al.  INCREASING INFRASTRUCTURE RESILIENCE THROUGH COMPETITIVE COEVOLUTION , 2009 .

[27]  Lida Xu,et al.  The internet of things: a survey , 2014, Information Systems Frontiers.

[28]  Peter J. Bentley,et al.  Detecting interest cache poisoning in sensor networks using an artificial immune algorithm , 2010, Applied Intelligence.