Ionic liquids via reaction of the zwitterionic 1,3-dimethylimidazolium-2-carboxylate with protic acids. Overcoming synthetic limitations and establishing new halide free protocols for the formation of ILs

The previously reported preparation of 1,3-dimethylimidazolium salts by the reaction of 1,3-dialkylimidazolium-2-carboxylate zwitterions with protic acids has been reinvestigated in detail, leading to the identification of two competing reactions: isomerisation and decarboxylation. The ability to control both pathways allows this methodology to be used as an effective, green, waste-free approach to readily prepare a wide range of ionic liquids in high yields. Additionally, this reaction protocol opens new possibilities in the formation of other imidazolium salts, whose syntheses were previously either very expensive (due to ion exchange protocols involving metals like Ag) or difficult to achieve (due to multiple extractions and large quantities of hard to remove inorganic by-products).

Classifying Network Protocols: A ‘2 Way’ Flow Approach

The identification and classification of network traffic and protocols is a vital step in many quality of service and security systems. Traffic classification strategies must evolve, alongside the protocols utilising the Internet, to overcome the use of ephemeral or masquerading port numbers and transport layer encryption. This research expands the concept of using machine learning on the initial statistics of flow of packets to determine its underlying protocol. Recognising the need for efficient training/retraining of a classifier and the requirement for fast classification, the authors investigate a new application of k-means clustering referred to as 'two-way' classification. The 'two-way' classification uniquely analyses a bidirectional flow as two unidirectional flows and is shown, through experiments on real network traffic, to improve classification accuracy by as much as 18% when measured against similar proposals. It achieves this accuracy while generating fewer clusters, that is, fewer comparisons are needed to classify a flow. A 'two-way' classification offers a new way to improve accuracy and efficiency of machine learning statistical classifiers while still maintaining the fast training times associated with the k-means.