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

Consistency of the Health of the Nation Outcome Scales (HoNOS) at inpatient-to-community transition

OBJECTIVES: The Health of the Nation Outcome Scales (HoNOS) are mandated outcome-measures in many mental-health jurisdictions. When HoNOS are used in different care settings, it is important to assess if setting specific bias exists. This article examines the consistency of HoNOS in a sample of psychiatric patients transitioned from acute inpatient care and community centres. SETTING: A regional mental health service with both acute and community facilities. PARTICIPANTS: 111 psychiatric patients were transferred from inpatient care to community care from 2012 to 2014. Their HoNOS scores were extracted from a clinical database; Each inpatient-discharge assessment was followed by a community-intake assessment, with the median period between assessments being 4 days (range 0-14). Assessor experience and professional background were recorded. PRIMARY AND SECONDARY OUTCOME MEASURES: The difference of HoNOS at inpatient-discharge and community-intake were assessed with Pearson correlation, Cohen\u27s κ and effect size. RESULTS: Inpatient-discharge HoNOS was on average lower than community-intake HoNOS. The average HoNOS was 8.05 at discharge (median 7, range 1-22), and 12.16 at intake (median 12, range 1-25), an average increase of 4.11 (SD 6.97). Pearson correlation between two total scores was 0.073 (95% CI -0.095 to 0.238) and Cohen\u27s κ was 0.02 (95% CI -0.02 to 0.06). Differences did not appear to depend on assessor experience or professional background. CONCLUSIONS: Systematic change in the HoNOS occurs at inpatient-to-community transition. Some caution should be exercised in making direct comparisons between inpatient HoNOS and community HoNOS scores

Involutivity of integrals for sine-Gordon, modified KdV and potential KdV maps

Closed form expressions in terms of multi-sums of products have been given in \cite{Tranclosedform, KRQ} of integrals of sine-Gordon, modified Korteweg-de Vries and potential Korteweg-de Vries maps obtained as so-called $(p,-1)$-traveling wave reductions of the corresponding partial difference equations. We prove the involutivity of these integrals with respect to recently found symplectic structures for those maps. The proof is based on explicit formulae for the Poisson brackets between multi-sums of products.Comment: 24 page

Development of a Real-Time, Simple and High-Accuracy Fall Detection System for Elderly Using 3-DOF Accelerometers

© 2018, King Fahd University of Petroleum & Minerals. Falls represent a major problem for the elderly people aged 60 or above. There are many monitoring systems which are currently available to detect the fall. However, there is a great need to propose a system which is of optimal effectiveness. In this paper, we propose to develop a low-cost fall detection system to precisely detect an event when an elderly person accidentally falls. The fall detection algorithm compares the acceleration with lower fall threshold and upper fall threshold values to accurately detect a fall event. The post-fall recognition module is the combination of posture recognition and vertical velocity estimation that has been added to our proposed method to enhance the performance and accuracy. In case of a fall, our device will transmit the location information to the contacts instantly via SMS and voice call. A smartphone application will ensure that the notifications are delivered to the elderly person’s relatives so that medical attention can be provided with minimal delay. The system was tested by volunteers and achieved 100% sensitivity and accuracy. This was confirmed by testing with public datasets and it also achieved the same percentage in sensitivity and accuracy as in our recorded datasets

Incommensurate antiferromagnetic order in weakly frustrated two-dimensional van der Waals insulator CrPSe3_3

Although the magnetic order is suppressed by a strong magnetic frustration, it is maintained but appears in complex order forms such as a cycloid or spin density wave in weakly frustrated systems. Herein, we report a weakly magnetic-frustrated two-dimensional van der Waals material CrPSe$_3$. Polycrystalline CrPSe$_3$ was synthesized at an optimized temperature of 700$^\circ$C to avoid the formation of any secondary phases (e.g., Cr$_2$Se$_3$). The antiferromagnetic transition appeared at $T_N\sim 126$ K with a large Curie-Weiss temperature $T_{\rm CW} \sim -371$ via magnetic susceptibility measurements, indicating weak frustration in CrPSe$_3$ with a frustration factor $f (|T_{\rm CW}|/T_N) \sim 3$. Evidently, the formation of long-range incommensurate spin-density wave antiferromagnetic order with the propagation vector $k = (0, 0.04, 0)$ was revealed by neutron diffraction measurements at low temperatures (below 120K). The monoclinic crystal structure of C2/m symmetry is preserved over the studied temperature range down to 20K, as confirmed by Raman spectroscopy measurements. Our findings on the spin density wave antiferromagnetic order in two-dimensional (2D) magnetic materials, not previously observed in the MPX$_3$ family, are expected to enrich the physics of magnetism at the 2D limit, thereby opening opportunities for their practical applications in spintronics and quantum devices.Comment: 23 pages, 4 figures, 2 table

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

The fate of acetic acid during glucose co-metabolism by the spoilage yeast Zygosaccharomyces bailii

Zygosaccharomyces bailii is one of the most widely represented spoilage yeast species, being able to metabolise acetic acid in the presence of glucose. To clarify whether simultaneous utilisation of the two substrates affects growth efficiency, we examined growth in single- and mixed-substrate cultures with glucose and acetic acid. Our findings indicate that the biomass yield in the first phase of growth is the result of the weighted sum of the respective biomass yields on single-substrate medium, supporting the conclusion that biomass yield on each substrate is not affected by the presence of the other at pH 3.0 and 5.0, at least for the substrate concentrations examined. In vivo(13)C-NMR spectroscopy studies showed that the gluconeogenic pathway is not operational and that [2-(13)C]acetate is metabolised via the Krebs cycle leading to the production of glutamate labelled on C(2), C(3) and C(4). The incorporation of [U-(14)C]acetate in the cellular constituents resulted mainly in the labelling of the protein and lipid pools 51.5% and 31.5%, respectively. Overall, our data establish that glucose is metabolised primarily through the glycolytic pathway, and acetic acid is used as an additional source of acetyl-CoA both for lipid synthesis and the Krebs cycle. This study provides useful clues for the design of new strategies aimed at overcoming yeast spoilage in acidic, sugar-containing food environments. Moreover, the elucidation of the molecular basis underlying the resistance phenotype of Z. bailii to acetic acid will have a potential impact on the improvement of the performance of S. cerevisiae industrial strains often exposed to acetic acid stress conditions, such as in wine and bioethanol production.This work was supported by Fundacao para a Ciencia e Tecnologia (FCT), Portugal Grant PTDC/AGR-ALI/102608/2008 and by project FCOMP-01-0124-FEDER- 007047 and by FEDER through POFC - COMPETE and national funds from FCT - project PEst-C/BIA/UI4050/2011. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript

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

Ecohealth trainer manual

This training manual, and the Field Building Leadership Initiative (FBLI) of which it is one component, is part of a global initiative to build capacity in ecosystem approaches to health. Although several books and journals provide materials for learners about Ecohealth, the FBLI Ecohealth Trainer Manual is intended primarily for lecturers, teachers, and trainers. The focus here is on how to teach Ecohealth, providing teachers and trainers with a starting point from which to explore, improvise, adapt, and develop diverse educational Ecohealth learning experiences for and with their participants

Combination Antifungal Therapy for Cryptococcal Meningitis

Background Combination antifungal therapy (amphotericin B deoxycholate and flucytosine) is the recommended treatment for cryptococcal meningitis but has not been shown to reduce mortality, as compared with amphotericin B alone. We performed a randomized, controlled trial to determine whether combining flucytosine or high-dose fluconazole with high-dose amphotericin B improved survival at 14 and 70 days. Methods We conducted a randomized, three-group, open-label trial of induction therapy for cryptococcal meningitis in patients with human immunodeficiency virus infection. All patients received amphotericin B at a dose of 1 mg per kilogram of body weight per day; patients in group 1 were treated for 4 weeks, and those in groups 2 and 3 for 2 weeks. Patients in group 2 concurrently received flucytosine at a dose of 100 mg per kilogram per day for 2 weeks, and those in group 3 concurrently received fluconazole at a dose of 400 mg twice daily for 2 weeks. Results A total of 299 patients were enrolled. Fewer deaths occurred by days 14 and 70 among patients receiving amphotericin B and flucytosine than among those receiving amphotericin B alone (15 vs. 25 deaths by day 14; hazard ratio, 0.57; 95% confidence interval [CI], 0.30 to 1.08; unadjusted P=0.08; and 30 vs. 44 deaths by day 70; hazard ratio, 0.61; 95% CI, 0.39 to 0.97; unadjusted P=0.04). Combination therapy with fluconazole had no significant effect on survival, as compared with monotherapy (hazard ratio for death by 14 days, 0.78; 95% CI, 0.44 to 1.41; P=0.42; hazard ratio for death by 70 days, 0.71; 95% CI, 0.45 to 1.11; P=0.13). Amphotericin B plus flucytosine was associated with significantly increased rates of yeast clearance from cerebrospinal fluid (−0.42 log10 colony-forming units [CFU] per milliliter per day vs. −0.31 and −0.32 log10 CFU per milliliter per day in groups 1 and 3, respectively; P<0.001 for both comparisons). Rates of adverse events were similar in all groups, although neutropenia was more frequent in patients receiving a combination therapy. Conclusions Amphotericin B plus flucytosine, as compared with amphotericin B alone, is associated with improved survival among patients with cryptococcal meningitis. A survival benefit of amphotericin B plus fluconazole was not found