Anti-Forensics

Anti-Forensics

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Anti-Forensics

Industrial Control Systems incorporate mechanisms to operate distribution, programming, and production of different items. Security of such networks is therefore more important than normal network security because they contain soft targets whose vulnerability to disruption is detrimental to the survival of large-scale plants thereby incurring huge losses (Weiss, 2010). They also have multiple pathways thereby comprising of interlinked networks hence single compromises affect the entire operation. As such, there is a severity matrix developed for their risk assessment using supervisory control and centralized data acquisition. For instance, they are linked to It networks for lesser isolation thus enhancing monitoring to detect anomalies (Zhang, 2010). Normal network security is different from ICS security because the latter employs Mean Failure Cost. It evaluates the computational basis for estimates regarding the availability of a system based on the loss that every stakeholder will sustain resulting from violations/breakdowns.

Anti-forensics are measures designed to negatively affect the availability, quality/amount of evidence of any wrongdoing within a system (Grimble, 2006). In normal network security environments, this technique involves data encryption to prevent hacking incidences. In Industrial Control Systems, anti-forensics is layered with network traffic protocols and customized operating system kernels that reverse the ability to access certain ports within the system. It is therefore laden with constrained devices with anti-logging capabilities, which make it difficult for conducting checks, or tampering with its operational cause (Grimble, 2001) The dynamic nature of the global industrial complex does not demand upgrading of current severity matrix to reduce anti-forensic effectiveness because that would be beneficial to the firms. This is due to the uncertainty surrounding the safety of the systems especially since technology keeps evolving. Preventing the compromise of such systems would therefore be a prudent move (Macaulay & Singer, 2012).

 

References

Grimble, M. J. (2001). Industrial Control Systems Design. Chichester [England: Wiley.

Grimble, M. J. (2006). Robust Industrial Control Systems: Optimal Design Approach for Polynomial Systems. Chichester: Wiley.

Macaulay, T., & Singer, B. (2012). Cybersecurity for Industrial Control Systems: SCADA, DCS, PLC, HMI, and SIS. Boca Raton, FL: CRC Press.

Weiss, J. (2010). Protecting Industrial Control Systems from Electronic Threats. New York: Momentum Press.

Zhang, P. (2010). Advanced Industrial Control Technology. Amsterdam: William Andrew/Elsevier.