Traditional cybersecurity architectures incorporate security mechanismsthat provide services such as confidentiality, authenticity, integrity, access control,and non-repudiation. These mechanisms are used extensively to prevent computerand network intrusions and attacks. For instance, access control services preventunauthorized access to cyberresources such as computers, networks, and data. However,the modern Internet security landscape is characterized by attacks that are voluminous,constantly evolving, extremely fast, persistent, and highly sophisticated. These characteristics impose significant challenges on preventive securityservices. Consequently, methodologies that enable autonomic detection and responseto cyberattacks should be employed synergistically with prevention techniquesin order to achieve effective defense-in-depth strategies and robust cybersecuritysystems. This is especially true for the critical systems belonging to Industry4.0 systems. In this chapter, we describe how we have integrated cyberattack detectionand response mechanisms into our Software-Defined Cloud Manufacturingarchitecture. The cyberattack detection algorithm described in this chapter is basedon ensemble intelligence with neural networks whose outputs are fed into a neuroevoledneural network oracle. The oracle produces an optimized classification outputthat is used to provide feedback to active attack response mechanisms within oursoftware-defined cloud manufacturing system. The underlying goal of this chapteris to show how computational intelligence approaches can be used to defend criticalIndustry 4.0 systems as well as other Internet-driven systems.
|Title of host publication||Cybersecurity for Industry 4.0|
|Subtitle of host publication||Analysis for Design and Manufacturing|
|Editors||Lane Thames, Dirk Schaefer|
|Publication status||Published - 6 Apr 2017|
|Name||Springer Series in Advanced Manufacturing|