Channel-forming activity of syringopeptin 25 A in mercury-supported phospholipid monolayers and negatively charged bilayers

Interactions of the cationic lipodepsipeptide syringopeptin 25 A (SP25A) with mercury-supported dioleoylphosphatidylcholine (DOPC), dioleoylphosphatidylserine (DOPS) and dioeleoylphosphatidic acid (DOPA) self-assembled monolayers (SAMs) were investigated by AC voltammetry in 0.1 M KCl at pH 3, 5.4 and 6.8. SP25A targets and penetrates the DOPS SAM much more effectively than the other SAMs not only at pH 6.8, where the DOPS SAM is negatively charged, but also at pH 3, where it is positively charged just as SP25A. Similar investigations at tethered bilayer lipid membranes (tBLMs) consisting of a thiolipid called DPTL anchored to mercury, with a DOPS, DOPA or DOPC distal monolayer on top of it, showed that, at physiological transmembrane potentials, SP25A forms ion channels spanning the tBLM only if DOPS is the distal monolayer. The distinguishing chemical feature of the DOPS SAM is the ionic interaction between the protonated amino group of a DOPS molecule and the carboxylate group of an adjacent phospholipid molecule. Under the reasonable assumption that SP25A preferentially interacts with this ion pair, the selective lipodepsipeptide antimicrobial activity against Gram-positive bacteria may be tentatively explained by its affinity for similar protonated amino-carboxylate pairs, which are expected to be present in the peptide moieties of peptidoglycan strands.

A survey of intrusion detection system technologies

This paper provides an overview of IDS types and how they work as well as configuration considerations and issues that affect them. Advanced methods of increasing the performance of an IDS are explored such as specification based IDS for protecting Supervisory Control And Data Acquisition (SCADA) and Cloud networks. Also by providing a review of varied studies ranging from issues in configuration and specific problems to custom techniques and cutting edge studies a reference can be provided to others interested in learning about and developing IDS solutions. Intrusion Detection is an area of much required study to provide solutions to satisfy evolving services and networks and systems that support them. This paper aims to be a reference for IDS technologies other researchers and developers interested in the field of intrusion detection.

Threat analysis of IoT networks using artificial neural network intrusion detection system

The Internet of things (IoT) is still in its infancy and has attracted much interest in many industrial sectors including medical fields, logistics tracking, smart cities and automobiles. However, as a paradigm, it is susceptible to a range of significant intrusion threats. This paper presents a threat analysis of the IoT and uses an Artificial Neural Network (ANN) to combat these threats. A multi-level perceptron, a type of supervised ANN, is trained using internet packet traces, then is assessed on its ability to thwart Distributed Denial of Service (DDoS/DoS) attacks. This paper focuses on the classification of normal and threat patterns on an IoT Network. The ANN procedure is validated against a simulated IoT network. The experimental results demonstrate 99.4% accuracy and can successfully detect various DDoS/DoS attacks.