Research pick: Intruding on big data - "User information intrusion prediction method based on empirical mode decomposition and spectrum feature detection"

Plain text documents and databases are vulnerable to intrusion by malicious third parties in a way that encrypted, password-protected materials are not. However, there are computer overheads and costs to adding encryption and so documents are often held on servers in plaintext nevertheless. Writing in the International Journal of Information and Communication Technology, a team from China is developing an intrusion detection system that is not resource hungry but can protect plaintext materials.

The team points out that with so-called “big data” the resource costs of encryption can make such protection a non-negligible task. Processing big data files can become unfeasibly slow with the constant need to decrypt and re-encrypt materials as they are retrieved, edited, curated, and otherwise processed and saved. However, the storage of information in plain text is prone to information leaks.
The team has suggested that pattern recognition and information filtering methods could be used to recognise intrusion and allow plaintext attacks to be quickly blocked before significant amounts of data are leaked but without those massive encryption-decryption overheads. An additional benefit is that the information can be shared between legitimate users without the need for cumbersome password protocols and systems being in place.

The reports that their system has a “relatively high probability of intrusion detection and low false alarm probability at low signal-to-noise ratio, which improves the intrusion detection and interception capability.”

Ma, Z., Ma, Y., Huang, X., Zhang, M., Su, B. and Zhao, L. (2020) ‘User information intrusion prediction method based on empirical mode decomposition and spectrum feature detection’, Int. J. Information and Communication Technology, Vol. 16, No. 2, pp.99–111.