Intelligent Detection for Cyber Phishing Attacks using Fuzzy rule-Based Systems

Cyber phishing attacks are increasing rapidly, causing the world economy monetary losses. Although various phishing detections have been proposed to prevent phishing, there is still a lack of accuracy such as false positives and false negatives causing inadequacy in online transactions. This study constructs a fuzzy rule model utilizing combined features based on a fuzzy inference system to tackle the foreseen inaccuracy in online transactions. The importance of the intelligent detection of cyber phishing is to discriminate emerging phishing websites with a higher accuracy. The experimental results achieved an excellent accuracy compared to the reported results in the field, which demonstrates the effectiveness of the fuzzy rule model and the feature-set. The findings indicate that the new approach can be used to discriminate between phishing and legitimate websites. This paper contributes by constructing a fuzzy rule model using a combined effective feature-set that has shown an excellent performance. Phishing deceptions evolve rapidly and should therefore be updated regularly to keep ahead with the changes

Intelligent Security for Phishing Online using Adaptive Neuro Fuzzy Systems

Anti-phishing detection solutions employed in industry use blacklist-based approaches to achieve low false-positive rates, but blacklist approaches utilizes website URLs only. This study analyses and combines phishing emails and phishing web-forms in a single framework, which allows feature extraction and feature model construction. The outcome should classify between phishing, suspicious, legitimate and detect emerging phishing attacks accurately. The intelligent phishing security for online approach is based on machine learning techniques, using Adaptive Neuro-Fuzzy Inference System and a combination sources from which features are extracted. An experiment was performed using two-fold cross validation method to measure the system’s accuracy. The intelligent phishing security approach achieved a higher accuracy. The finding indicates that the feature model from combined sources can detect phishing websites with a higher accuracy. This paper contributes to phishing field a combined feature which sources in a single framework. The implication is that phishing attacks evolve rapidly; therefore, regular updates and being ahead of phishing strategy is the way forward

Abnormal Infant Movements Classification With Deep Learning on Pose-Based Features

The pursuit of early diagnosis of cerebral palsy has been an active research area with some very promising results using tools such as the General Movements Assessment (GMA). In our previous work, we explored the feasibility of extracting pose-based features from video sequences to automatically classify infant body movement into two categories, normal and abnormal. The classification was based upon the GMA, which was carried out on the video data by an independent expert reviewer. In this paper we extend our previous work by extracting the normalised pose-based feature sets, Histograms of Joint Orientation 2D (HOJO2D) and Histograms of Joint Displacement 2D (HOJD2D), for use in new deep learning architectures. We explore the viability of using these pose-based feature sets for automated classification within a deep learning framework by carrying out extensive experiments on five new deep learning architectures. Experimental results show that the proposed fully connected neural network FCNet performed robustly across different feature sets. Furthermore, the proposed convolutional neural network architectures demonstrated excellent performance in handling features in higher dimensionality. We make the code, extracted features and associated GMA labels publicly available

Live video transmission over data distribution service with existing low-power platforms

This paper investigates video transmission over a middleware layer based on the Object Management Group’s Data-Distribution Service (DDS) standard, with a focus on low power platforms. Low power platforms are being widely utilised to implement IoT devices. One important type of IoT application is live video sharing which requires higher bandwidth than current typical applications. However, only limited research has been carried out on quality of services of data distribution utilising low end platforms. This paper discusses the development of prototypes that consist of both a Raspberry Pi 2 and an Android smartphone with client applications using Prismtech’s Vortex line of DDS middleware. Experiments have yielded interesting performance results: DDS middleware implementations that run on low power hardware with native code can provide sufficient performance. They are efficient enough to consistently handle high bandwidth live video with the network performance proving to be the bottleneck rather than the processing power of the devices. However, virtual machine implementations on an Android device did not achieve similar performance levels. These research findings will provide recommendations on adopting low power devices for sharing live video distribution in IoT over DDS middleware

Intrusion Detection System by Fuzzy Interpolation

Network intrusion detection systems identify malicious connections and thus help protect networks from attacks. Various data-driven approaches have been used in the development of network intrusion detection systems, which usually lead to either very complex systems or poor generalization ability due to the complexity of this challenge. This paper proposes a data-driven network intrusion detection system using fuzzy interpolation in an effort to address the aforementioned limitations. In particular, the developed system equipped with a sparse rule base not only guarantees the online performance of intrusion detection, but also allows the generation of security alerts from situations which are not directly covered by the existing knowledge base. The proposed system has been applied to a well-known data set for system validation and evaluation with competitive results generated

Integrating employability resources to strengthen student – personal tutor partnerships

The Teaching Excellence Framework has resulted in an increased focus on personalised learning, as part of the wider learning environment, and on graduate employment, as an indicator of student outcomes and learning gain. One element of the academic role which is fundamental to both these considerations is that of the personal tutor. The benefits of an effective personal tutoring system include promoting the development of transferable skills, improving student retention and progression, and strengthening career awareness and employability (McFarlane, 2016). Inconsistencies in the implementation of this important element of individualised student support may be partly due to staff uncertainties about the scope of their responsibilities, concerns about the boundaries between pastoral and academic elements of the role, and the lack of readily available resources and tools for the personal tutor. (Race 2010; Luck 2010; Gardner and Lane 2010; Barlow and Antoniou 2007; Levy et al. 2009). Moreover, students can have variable experiences of personal tutoring (Thomas 2006a; Hixenbaugh, Pearson and Williams, 2006), which result in them viewing it as poorly organised and perhaps unnecessary. This presentation will explore the challenges involved in the creation of a tool designed to help personal tutors facilitate student reflection on their progress and to signpost them to a range of employability resources and opportunities, and the strategies used to overcome these. There were four contributory elements to this development. An exploration of staff and students’ experiences of personal tutoring included focus group discussions and semi-structured interviews with students and staff across five different Departments which included sciences, geography and engineering. The findings of an institutional survey into student readiness for employability highlighted the proportion of students who did not appear to be considering the importance of in-course preparation for future graduate employment. Two examples of initiatives in which personal tutoring had been successfully embedded into curricula were then considered, from geography and health programmes. One of these had involved embedding personal tutoring within a core module and the other incorporated several mandatory individual meetings scheduled between students and personal tutors throughout an undergraduate programme. Lessons learned from all these elements were discussed with personal tutor coordinators, and the Director of Placements and Employability who advised about local, national and regional resources and opportunities, both at Faculty and University level. Building on these elements, a tool for personal tutors was developed and distributed to colleagues. The aim of the tool was to provide them with easily accessible resources which could be used at meetings with their personal tutees. It included elements of the work and toolkit developed by Winston, Nash, Parker and Rowntree (2017), created to encourage students to optimise the use of their assessment feedback, and reflect on their personal development. Alongside this, it highlighted the whole range of employability opportunities which might enable students to better consider their future following graduation. Feedback from colleagues was very positive and it was disseminated across the institution. Comments from colleagues who were new to Higher Education indicated that some were quite daunted as it highlighted the scope of their role as personal tutor, but that the tool was helpful in adapting to this responsibility

Directing Placements

Northumbria University is undergoing a transformation to achieve Vision 2025. Part of this change has been to create a one university structure where the opportunity for placements and learning in practice is available to all students. This session will give an overview of how this is organised with regards to placements at university and faculty level, how it is working and the challenges we have faced and are facing along with the benefits of working together. Within the session, we will share good practice from the four faculties around student engagement, curriculum development, streamlining processes and IT solutions

Cell-Based Reporter Release Assay to Determine the Potency of Proteolytic Bacterial Neurotoxins

Despite the implementation of cell-based replacement methods, the mouse lethality assay is still frequently used to determine the activity of botulinum toxin (BoNT) for medical use. One explanation is that due to the use of neoepitope-specific antibodies to detect the cleaved BoNT substrate, the currently devised assays can detect only one specific serotype of the toxin. Recently, we developed a cell-based functional assay, in which BoNT activity is determined by inhibiting the release of a reporter enzyme that is liberated concomitantly with the neurotransmitter from neurosecretory vesicles. In theory, this assay should be suitable to detect the activity of any BoNT serotype. Consistent with this assumption, the current study shows that the stimulus-dependent release of a luciferase from a differentiated human neuroblastoma-based reporter cell line (SIMA-hPOMC1-26-GLuc cells) was inhibited by BoNT-A and-C. Furthermore, this was also inhibited by BoNT-B and tetanus toxin to a lesser extent and at higher concentrations. In order to provide support for the suitability of this technique in practical applications, a dose–response curve obtained with a pharmaceutical preparation of BoNT-A closely mirrored the activity determined in the mouse lethality assay. In summary, the newly established cell-based assay may represent a versatile and specific alternative to the mouse lethality assay and other currently established cell-based assays