Abstract
This report provides an overview of the current implementation of SIS in the field of cybersecurity. It also identifies the positive and negative aspects of using SIS in cybersecurity, including ethical issues which could arise while using SIS in this area. One company working in the industry of telecommunications (Company A) is analysed in this report. Further specific ethical issues that arise when using SIS technologies in Company A are critically evaluated. Finally, conclusions are drawn on the case study and areas for improvement are suggested.
Highlights
This report provides an overview of the current implementation of SIS in the field of cybersecurity
Our goal is to compare them with the interview that has been conducted in a major telecommunications software and hardware company, Company A, in order to give an overview on the ethical issues in cybersecurity
Acquiring informed consent is an important activity for cybersecurity, and one that has been at the heart of research ethics and practice for decades (Johnson et al, 2012; Miller and Wertheimer, 2009).Consent is variously valued as the respect for autonomy (Beauchamp, 2009) or the minimization of harm (Manson and O’Neill, 2007).The justification for informed consent is a considerable challenge for data analytics, where anonymised data may be used without explicit consent of the person from whom it originates
Summary
The introduction of big data and artificial intelligence (Smart Information Systems, or SIS) in cybersecurity is still in its early phase. There is comparatively little work carried out on cybersecurity using SIS for several reasons These include the remarkable diversity of cyber attacks (e.g. different approaches to hacking systems and introducing malware), the danger of false positives and false negatives, and the relatively low intelligence of existing SIS. The motivation of the attack can range from state security and intelligence gathering (e.g.US Intelligence spying on Chinese military installations), to financial incentives through blackmail (e.g. encrypting a company’s files and agreeing to decrypt them only when the company has paid the hacker a certain sum of money) This diversity means that it is extremely difficulty to develop a SIS that will effectively recognize an attack for what it is. When coupled with a human operator to scan any alerts and so determine whether to take action, the combined human-machine security system can prove to be effective, albeit still facing the above problems of automation bias and excessive false positives (Macnish, 2012)
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