A Deep Learning Approach to Network Intrusion Detection

Software Defined Networking (SDN) has recently emerged to become one of the promising solutions for the future Internet. With the logical centralization of controllers and a global network overview, SDN brings us a chance to strengthen our network security. However, SDN also brings us a dangerous increase in potential threats. In this paper, we apply a deep learning approach for flow-based anomaly detection in an SDN environment. We build a Deep Neural Network (DNN) model for an intrusion detection system and train the model with the NSL-KDD Dataset. In this work, we just use six basic features (that can be easily obtained in an SDN environment) taken from the forty-one features of NSL-KDD Dataset. Through experiments, we confirm that the deep learning approach shows strong potential to be used for flow-based anomaly detection in SDN environments

Rail Internet of Things: An Architectural Platform and Assured Requirements Model

Given the plethora of individual preferences and requirements of public transport passengers for travel, seating, catering, etc., it becomes very challenging to tailor generic services to individuals’ requirements using the existing service platforms. As tens of thousands of sensors have been already deployed along roadsides and rail tracks, and on buses and trains in many countries, it is expected that the introduction of IP networking will revolutionise the functionality of public transport in general and rail services in particular. In this paper, we propose a new communication paradigm to improve rail services and address the requirement of rail service users: the Rail Internet of Things (RIoT). To the best of our knowledge, it is the first work to define the RIoT and design an architectural platform that includes its components and the data communication channels. Moreover, we develop an assured requirements model using the situation calculus modelling to represent the fundamental requirements for adjustable, decentralised feedback control mechanisms necessary for the RIoT-ready software systems. The developed formal model is applied to demonstrate the design of passenger assistance software that interacts with the RIoT ecosystem and provides passengers with real-time information that is tailored to their requirements with runtime adaptability. Keywords—Assistance; Assured model; Inclusive; IoT; Rail Internet of Things (RIoT); Situation Calculu

Development of a defined compost system for the study of plant-microbe interactions

Plant growth promoting rhizobacteria can improve plant health by providing enhanced nutrition, disease suppression and abiotic stress resistance, and have potential to contribute to sustainable agriculture. We have developed a sphagnum peat-based compost platform for investigating plant-microbe interactions. The chemical, physical and biological status of the system can be manipulated to understand the relative importance of these factors for plant health, demonstrated using three case studies: 1. Nutrient depleted compost retained its structure, but plants grown in this medium were severely stunted in growth due to removal of essential soluble nutrients - particularly, nitrogen, phosphorus and potassium. Compost nutrient status was replenished with the addition of selected soluble nutrients, validated by plant biomass; 2. When comparing milled and unmilled compost, we found nutrient status to be more important than matrix structure for plant growth; 3. In compost deficient in soluble P, supplemented with an insoluble inorganic form of P (Ca3(PO4)2), application of a phosphate solubilising Pseudomonas strain to plant roots provides a significant growth boost when compared with a Pseudomonas strain incapable of solubilising Ca3(PO4)2. Our findings show that the compost system can be manipulated to impose biotic and abiotic stresses for testing how microbial inoculants influence plant growth

Small-Group Teaching: Should It Be Recorded?

Background Recording large-group lectures is commonplace in higher education, allowing students to access content asynchronously and remotely. With the move towards online learning during the COVID-19 pandemic, recording of small-group teaching sessions has also become increasingly common; however, the educational value of this practice is unknown. Methods All medical students rotating through the Acute Medicine Department of a large teaching hospital were invited to enrol in the study. Consenting students were recorded for the second half of an online case-based learning (CBL) session. The recording was available for 6 months; viewing patterns were analysed. Students were sent a questionnaire after the session, asking them to reflect on the recorded and unrecorded halves of the session. Findings Thirty-three students underwent recording in 12 separate groups; 31 students (94%) completed the questionnaire. All 31 respondents (100%) described the session as “useful” or “very useful”. Twenty-four respondents (77%) recommended continuing to record small-group sessions and 17 (55%) reported being “likely” or “very likely” to watch the recording. Six respondents (19%) reported a negative impact of being recorded. During 6 months of follow-up, no students returned to view the recording for more than 1 minute. Conclusion Despite positive feedback for the session and high student demand for ongoing recording, no students viewed the recording for any significant duration. One-fifth of students reported a negative impact of being recorded. The findings from this study do not support routine recording of small-group CBL sessions, even where demand for this may exist

Call me Fei: Chinese-speaking students’ decision whether or not to use English names in classroom interaction

Unlike other groups of international students, Chinese speakers’ use of English names while studying in English is an established norm. Relatively little discussion of the practice has taken place within recent literature, and less attention still has been paid to the minority of Chinese-speaking students who do not adopt English names. The choice of name used during classroom interaction is, though, both significant and meaningful, symbolising the social and cultural membership a person would like to evoke and impacting on student-teacher relationships. This article reports on a survey into the use of English names by Chinese speakers, which was completed by 330 Chinese-speaking students at UK universities – 255 of whom had adopted English names, 75 of whom had not. Survey responses reveal why and how decisions to/not to adopt English names are made. Interview data is then presented from discussions with eight Chinese-speaking students based in the UK who do not use English names. They explain why and describe their experiences of being a minority among Chinese-speakers studying in English

Deep Learning Combined with De - noising Data for Network Intrusion Detection

Anomaly-based Network Intrusion Detection Systems (NIDSs) are a common security defense for modern networks. The success of their operation depends upon vast quantities of training data. However, one major limitation is the inability of NIDS to be reliably trained using imbalanced datasets. Network observations are naturally imbalanced, yet without substantial data pre-processing, NIDS accuracy can be significantly reduced. With the diversity and dynamicity of modern network traffic, there are concerns that the current reliance upon un-natural balanced datasets cannot remain feasible in modern networks. This paper details our de-noising method, which when combined with deep learning techniques can address these concerns and offer accuracy improvements of between 1.5% and 4.5%. Promising results have been obtained from our model thus far, demonstrating improvements over existing approaches and the strong potential for use in modern NIDSs

Adult cardiac stem cells are multipotent and robustly myogenic: c-kit expression is necessary but not sufficient for their identification

Multipotent adult resident cardiac stem cells (CSCs) were first identified by the expression of c-kit, the stem cell factor receptor. However, in the adult myocardium c-kit alone cannot distinguish CSCs from other c-kit-expressing (c-kitpos) cells. The adult heart indeed contains a heterogeneous mixture of c-kitpos cells, mainly composed of mast and endothelial/progenitor cells. This heterogeneity of cardiac c-kitpos cells has generated confusion and controversy about the existence and role of CSCs in the adult heart. Here, to unravel CSC identity within the heterogeneous c-kit-expressing cardiac cell population, c-kitpos cardiac cells were separated through CD45-positive or -negative sorting followed by c-kitpos sorting. The blood/endothelial lineage-committed (Lineagepos) CD45posc-kitpos cardiac cells were compared to CD45neg(Lineageneg/Linneg) c-kitpos cardiac cells for stemness and myogenic properties in vitro and in vivo. The majority (~90%) of the resident c-kitpos cardiac cells are blood/endothelial lineage-committed CD45posCD31posc-kitpos cells. In contrast, the LinnegCD45negc-kitpos cardiac cell cohort, which represents 10% of the total c-kitpos cells, contain all the cardiac cells with the properties of adult multipotent CSCs. These characteristics are absent from the c-kitneg and the blood/endothelial lineage-committed c-kitpos cardiac cells. Single Linnegc-kitpos cell-derived clones, which represent only 1–2% of total c-kitpos myocardial cells, when stimulated with TGF-β/Wnt molecules, acquire full transcriptome and protein expression, sarcomere organisation, spontaneous contraction and electrophysiological properties of differentiated cardiomyocytes (CMs). Genetically tagged cloned progeny of one Linnegc-kitpos cell when injected into the infarcted myocardium, results in significant regeneration of new CMs, arterioles and capillaries, derived from the injected cells. The CSC’s myogenic regenerative capacity is dependent on commitment to the CM lineage through activation of the SMAD2 pathway. Such regeneration was not apparent when blood/endothelial lineage-committed c-kitpos cardiac cells were injected. Thus, among the cardiac c-kitpos cell cohort only a very small fraction has the phenotype and the differentiation/regenerative potential characteristics of true multipotent CSCs

Behaviour-aware Malware Classification: Dynamic Feature Selection

Despite the continued advancements in security research, malware persists as being a major threat in this digital age. Malware detection is a primary defence strategy for most networks but the identification of malware strains is becoming increasingly difficult. Reliable identification is based upon characteristic features being detectable within an object. However, the limitations and expense of current malware feature extraction methods is significantly hindering this process. In this paper, we present a new method for identifying malware based on behavioural feature extraction. Our proposed method has been evaluated using seven classification methods whilst analysing 2,068 malware samples from eight different families. The results achieved thus far have demonstrated promising improvements over existing approaches

Symbols in engineering drawings (SiED): an imbalanced dataset benchmarked by convolutional neural networks.

Engineering drawings are common across different domains such as Oil & Gas, construction, mechanical and other domains. Automatic processing and analysis of these drawings is a challenging task. This is partly due to the complexity of these documents and also due to the lack of dataset availability in the public domain that can help push the research in this area. In this paper, we present a multiclass imbalanced dataset for the research community made of 2432 instances of engineering symbols. These symbols were extracted from a collection of complex engineering drawings known as Piping and Instrumentation Diagram (P&ID). By providing such dataset to the research community, we anticipate that this will help attract more attention to an important, yet overlooked industrial problem, and will also advance the research in such important and timely topics. We discuss the datasets characteristics in details, and we also show how Convolutional Neural Networks (CNNs) perform on such extremely imbalanced datasets. Finally, conclusions and future directions are discussed