Stochastic resonance and finite resolution in a network of leaky integrate-and-fire neurons.

This thesis is a study of stochastic resonance (SR) in a discrete implementation of a leaky integrate-and-fire (LIF) neuron network. The aim was to determine if SR can be realised in limited precision discrete systems implemented on digital hardware. How neuronal modelling connects with SR is discussed. Analysis techniques for noisy spike trains are described, ranging from rate coding, statistical measures, and signal processing measures like power spectrum and signal-to-noise ratio (SNR). The main problem in computing spike train power spectra is how to get equi-spaced sample amplitudes given the short duration of spikes relative to their frequency. Three different methods of computing the SNR of a spike train given its power spectrum are described. The main problem is how to separate the power at the frequencies of interest from the noise power as the spike train encodes both noise and the signal of interest. Two models of the LIF neuron were developed, one continuous and one discrete, and the results compared. The discrete model allowed variation of the precision of the simulation values allowing investigation of the effect of precision limitation on SR. The main difference between the two models lies in the evolution of the membrane potential. When both models are allowed to decay from a high start value in the absence of input, the discrete model does not completely discharge while the continuous model discharges to almost zero. The results of simulating the discrete model on an FPGA and the continuous model on a PC showed that SR can be realised in discrete low resolution digital systems. SR was found to be sensitive to the precision of the values in the simulations. For a single neuron, we find that SR increases between 10 bits and 12 bits resolution after which it saturates. For a feed-forward network with multiple input neurons and one output neuron, SR is stronger with more than 6 input neurons and it saturates at a higher resolution. We conclude that stochastic resonance can manifest in discrete systems though to a lesser extent compared to continuous systems

An adversarial approach for intrusion detection systems using Jacobian Saliency Map Attacks (JSMA) Algorithm

In today’s digital world, the information systems are revolutionizing the way we connect. As the people are trying to adopt and integrate intelligent systems into daily lives, the risks around cyberattacks on user-specific information have significantly grown. To ensure safe communication, the Intrusion Detection Systems (IDS) were developed often by using machine learning (ML) algorithms that have the unique ability to detect malware against network security violations. Recently, it was reported that the IDS are prone to carefully crafted perturbations known as adversaries. With the aim to understand the impact of such attacks, in this paper, we have proposed a novel random neural network-based adversarial intrusion detection system (RNN-ADV). The NSL-KDD dataset is utilized for training. For adversarial attack crafting, the Jacobian Saliency Map Attack (JSMA) algorithm is used, which identifies the feature which can cause maximum change to the benign samples with minimum added perturbation. To check the effectiveness of the proposed adversarial scheme, the results are compared with a deep neural network which indicates that RNN-ADV performs better in terms of accuracy, precision, recall, F1 score and training epochs

RNN-ABC: A New Swarm Optimization Based Technique for Anomaly Detection

The exponential growth of internet communications and increasing dependency of users upon software-based systems for most essential, everyday applications has raised the importance of network security. As attacks are on the rise, cybersecurity should be considered as a prime concern while developing new networks. In the past, numerous solutions have been proposed for intrusion detection; however, many of them are computationally expensive and require high memory resources. In this paper, we propose a new intrusion detection system using a random neural network and an artificial bee colony algorithm (RNN-ABC). The model is trained and tested with the benchmark NSL-KDD data set. Accuracy and other metrics, such as the sensitivity and specificity of the proposed RNN-ABC, are compared with the traditional gradient descent algorithm-based RNN. While the overall accuracy remains at 95.02%, the performance is also estimated in terms of mean of the mean squared error (MMSE), standard deviation of MSE (SDMSE), best mean squared error (BMSE), and worst mean squared error (WMSE) parameters, which further confirms the superiority of the proposed scheme over the traditional method

Secure Software Development: Week 5 - Security Matters During The Development Cycle

Why Security Matters? It is much more expensive to fix once the software has been shipped Prevention is better than cure For every software company: Fixing an issue after release is 50 to 200 times more expensive than fixing it in the test cycle. Much more expensive to fix later on – Microsoft estimates that each security bulletin costs $100k Bad PR - Your company face on magazines and news paper headlines! People will shy away from your products once they know you have been breached Consultants look to build their reputation by “victimising” easy targets like you

Big Data Landascape

Big Data Tech Linux Mint V

Week 3 - Relational Data Modelling I:modelling processes and the language of sets.

In this week, we will cover the following topics: What modelling is, and specifically what data modelling is. What data modelling involves in real-world situations (modelling cycle) Some visual design conventions used in relational database design. The language of sets … and will result in the following learning outcomes: An understanding of the general modelling cycle and how database designers might liaise with business clients. An appreciation for the fact that good design needs an good appreciation of the data domain, and the data in the domain. An understanding that of the mathematical language of sets. An appreciation that understanding this language will help inform database queries

Week 11 - NoSQL

In this week, we will cover the following topics: Definitions of the NoSQL ‘approach’ to databases. Alternative database forms, not just relational. ACIDity and NoSQL. A closer look into an industrial-strength NoSQL database: Neo4j. … and will result in the following learning outcomes Appreciation of what NoSQL means. An understanding that not all data suit a relational solution. Appreciation that while many alternative databases exist, they can be classified into a relatively small number, based on the underlying data structure. Understanding of how graph data can be stored and queried in a graph database, using neo4j as an example technology