Data Leakage Prevention Using Statistical Semantics Analysis
Sultan Alneyadi · Griffith Research Online · 2016
Protecting confidential data from being leaked into the public domain is a growing concern among organisations and individuals. Traditionally, data confidentiality has been preserved utilising common security procedures contained within ‘Information Security Policies’, which have limited scope to be individually tailored, along with conventional security mechanisms such as firewalls, virtual private networks and intrusion detection systems. Unfortunately, these standard mechanisms lack the necessary pro-activeness and dynamism required in today’s world of ever-changing technology and innovative threats to digital security. Another consideration is the constantly variable nature of how data are manifested through multitudes of leaking channels to be satisfactorily effective in preventing the serious and potentially disastrous consequences of such leaks. Therefore, there has been an industry-wide drive towards mitigating these drawbacks using more effective instruments. Recently, Data Leakage Prevention Systems (DLPSs) have been introduced as dedicated mechanisms to detect and prevent the leakage of confidential data that are in use, in transit and at rest. DLPSs use various techniques to analyse the context and content of confidential data to detect and prevent leakages. Contextual analysis usually studies the attributes surrounding confidential data such as senders, recipients, timing, data size and data format. In contrast, content analysis focuses on identifying confidential content using techniques such as regular expression, data fingerprinting and statistical analysis. Equipped with contextual analysis, content analysis or a combination of both, a wide range of DLPSs have been proposed in academia and industry, each with its own rewards and drawbacks.