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Software-defined networks (SDNs) offer robust network architectures for current and future Internet of Things (IoT) applications. At the same time, SDNs constitute an attractive target for cyber attackers due to their global network view and programmability. One of the major vulnerabilities of typical SDN architectures is their susceptibility to Distributed Denial of Service (DDoS) flooding attacks. DDoS flooding attacks can render SDN controllers unavailable to their underlying infrastructure, causing service disruption or a complete outage in many cases. In this paper, machine learning-based detection and classification of DDoS flooding attacks on SDNs is investigated using popular machine learning (ML) algorithms. The ML algorithms, classifiers and methods investigated are quadratic discriminant analysis (QDA), Gaussian Naïve Bayes (GNB), $k$ -nearest neighbor (k-NN), and classification and regression tree (CART). The general principle is illustrated through a case study, in which, experimental data (i.e. jitter, throughput, and response time metrics) from a representative SDN architecture suitable for typical mid-sized enterprise-wide networks is used to build classification models that accurately identify and classify DDoS flooding attacks. The SDN model used was emulated in Mininet and the DDoS flooding attacks (i.e. hypertext transfer protocol (HTTP), transmission control protocol (TCP), and user datagram protocol (UDP) attacks) have been launched on the SDN model using low orbit ion cannon (LOIC). Although all the ML methods investigated show very good efficacy in detecting and classifying DDoS flooding attacks, CART demonstrated the best performance on average in terms of prediction accuracy (98%), prediction speed ( $5.3\,\,{\times }\,\,10^{5}$ observations per second), training time (12.4 ms), and robustness.

In this study, specifically for the detection of ripe/unripe tomatoes with/without defects in the crop field, two distinct methods are described and compared from captured images by a camera mounted on a mobile robot. One is a machine learning approach, known as ‘Cascaded Object Detector’ (COD) and the other is a composition of traditional customised methods, individually known as ‘Colour Transformation’: ‘Colour Segmentation’ and ‘Circular Hough Transformation’. The (Viola‐Jones) COD generates ‘histogram of oriented gradient’ (HOG) features to detect tomatoes. For ripeness checking, the RGB mean is calculated with a set of rules. However, for traditional methods, colour thresholding is applied to detect tomatoes either from natural or solid background and RGB colour is adjusted to identify ripened tomatoes. This algorithm is shown to be optimally feasible for any micro‐controller based miniature electronic devices in terms of its run time complexity of O(n3) for a traditional method in best and average cases. Comparisons show that the accuracy of the machine learning method is 95%, better than that of the Colour Segmentation Method using MATLAB.

The infrastructure of large networks is broken down into areas that have a common security policy called a domain. Security within a domain is commonly implemented at all nodes. However this can have a negative effect on performance since it introduces a delay associated with packet filtering. When Access Control Lists (ACLs) are used within a router for this purpose then a significant overhead is introduced associated with this process. It is likely that identical checks are made at multiple points within a domain prior to a packet reaching its destination. Therefore by eliminating ACLs within a domain by modifying the ingress/egress points with equivalent functionality an improvement in the overall performance can be obtained. This paper considers the effect of the delays when using router operating systems offering different levels of functionality. It considers factors which contribute to the delay particularly due to ACLs and by using theoretical principles modified by practical calculation a model is created. Additionally this paper provides an example of an optimized solution which reduces the delay through network routers by distributing the security rules to the ingress/egress points of the domain without affecting the security policy.

It seems to be accepted that intelligence—artificial or otherwise—and ‘the singularity’ are inseparable concepts: ‘The singularity’ will apparently arise from AI reaching a, supposedly particular, but actually poorly-defined, level of sophistication; and an empowered combination of hardware and software will take it from there (and take over from us). However, such wisdom and debate are simplistic in a number of ways: firstly, this is a poor definition of the singularity; secondly, it muddles various notions of intelligence; thirdly, competing arguments are rarely based on shared axioms, so are frequently pointless; fourthly, our models for trying to discuss these concepts at all are often inconsistent; and finally, our attempts at describing any ‘post-singularity’ world are almost always limited by anthropomorphism. In all of these respects, professional ‘futurists’ often appear as confused as storytellers who, through freer licence, may conceivably have the clearer view: perhaps then, that becomes a reasonable place to start. There is no attempt in this paper to propose, or evaluate, any research hypothesis; rather simply to challenge conventions. Using examples from science fiction to illustrate various assumptions behind the AI/singularity debate, this essay seeks to encourage discussion on a number of possible futures based on different underlying metaphysical philosophies. Although properly grounded in science, it eventually looks beyond the technology for answers and, ultimately, beyond the Earth itself.

This paper provides an initial introduction to, and definition of, the ‘Dynamically Powered Relays for a Flexible Optical Network’ (DPR-FON) problem for opto-electro-optical (OEO) regenerators used in optical networks. In such networks, optical transmission parameters can be varied dynamically as traffic patterns change. This will provide different bandwidths, but also change the regeneration limits as a result. To support this flexibility, OEOs (‘relays’) may be switched on and off as required, thus saving power. DPR-FON is shown to be NP-complete; consequently, solving such a dynamic problem in real-time requires a fast heuristic capable of delivering an acceptable approximation to the optimal configuration with low complexity. In this paper, just such an algorithm is developed, implemented, and evaluated against more computationally-demanding alternatives for two known cases. A number of real-world extensions are considered as the paper develops, combining to produce the ‘Generalised Dynamically Powered Relays for a Flexible Optical Network’ (GDPR-FON) problem. This, too, is analysed and an associated fast heuristic proposed, along with an exploration of the further research that is required.