A Deep Learning Approach Combining Auto-encoder with One-class SVM for DDoS Attack Detection in SDNs

Software Defined Networking (SDN) provides us with the capability of collecting network traffic information and managing networks proactively. Therefore, SDN facilitates the promotion of more robust and secure networks. Recently, several Machine Learning (ML)/Deep Learning (DL) intrusion detection approaches have been proposed to secure SDN networks. Currently, most of the proposed ML/DL intrusion detection approaches are based on supervised learning approach that required labelled and well-balanced datasets for training. However, this is time intensive and require significant human expertise to curate these datasets. These approaches cannot deal well with imbalanced and unlabeled datasets. In this paper, we propose a hybrid unsupervised DL approach using the stack autoencoder and One-class Support Vector Machine (SAE-1SVM) for Distributed Denial of Service (DDoS) attack detection. The experimental results show that the proposed algorithm can achieve an average accuracy of 99.35 % with a small set of flow features. The SAE-1SVM shows that it can reduce the processing time significantly while maintaining a high detection rate. In summary, the SAE-1SVM can work well with imbalanced and unlabeled datasets and yield a high detection accuracy

DeepIDS: Deep Learning Approach for Intrusion Detection in Software Defined Networking

Software Defined Networking (SDN) is developing as a new solution for the development and innovation of the Internet. SDN is expected to be the ideal future for the Internet, since it can provide a controllable, dynamic, and cost-effective network. The emergence of SDN provides a unique opportunity to achieve network security in a more efficient and flexible manner. However, SDN also has original structural vulnerabilities, which are the centralized controller, the control-data interface and the control-application interface. These vulnerabilities can be exploited by intruders to conduct several types of attacks. In this paper, we propose a deep learning (DL) approach for a network intrusion detection system (DeepIDS) in the SDN architecture. Our models are trained and tested with the NSL-KDD dataset and achieved an accuracy of 80.7% and 90% for a Fully Connected Deep Neural Network (DNN) and a Gated Recurrent Neural Network (GRU-RNN), respectively. Through experiments, we confirm that the DL approach has the potential for flow-based anomaly detection in the SDN environment. We also evaluate the performance of our system in terms of throughput, latency, and resource utilization. Our test results show that DeepIDS does not affect the performance of the OpenFlow controller and so is a feasible approach

A Systematic Review of Data-Driven Attack Detection Trends in IoT

The Internet of Things is perhaps a concept that the world cannot be imagined without today, having become intertwined in our everyday lives in the domestic, corporate and industrial spheres. However, irrespective of the convenience, ease and connectivity provided by the Internet of Things, the security issues and attacks faced by this technological framework are equally alarming and undeniable. In order to address these various security issues, researchers race against evolving technology, trends and attacker expertise. Though much work has been carried out on network security to date, it is still seen to be lagging in the field of Internet of Things networks. This study surveys the latest trends used in security measures for threat detection, primarily focusing on the machine learning and deep learning techniques applied to Internet of Things datasets. It aims to provide an overview of the IoT datasets available today, trends in machine learning and deep learning usage, and the efficiencies of these algorithms on a variety of relevant datasets. The results of this comprehensive survey can serve as a guide and resource for identifying the various datasets, experiments carried out and future research directions in this field

Selective allocation of patients with vaginal apical prolapse to either mesh augmented open abdominal repair or vaginal sacrospinous colpopexy improve functional and anatomical outcomes

Background: To evaluate the functional and anatomical outcomes after allocation of patients with apical vaginal prolapse to either mesh augmented abdominal repair or vaginal sacrospinous-colpopexy based on proposed selection criteria.Methods: A non-randomized trial was conducted at Ain-Shams university maternity hospital on patients with apical vaginal prolapse stage ≥2 based on pelvic organ prolapse quantification system. Certain criteria were proposed for patient selection to either mesh augmented abdominal repair or vaginal sacrospinous-colpopexy. Seventy-eight patients were assigned for sacrospinous-colpopexy and 47-patients for abdominal repair. Primary outcomes were the functional outcome using urogenital distress inventory questionnaire and patient global impression of improvement (PGI-I). Both were measured at 1-year’s follow-up. Secondary outcomes involved the anatomical success (defined as no apical prolapse ≥POP-Q stage 2), perioperative data and long-term complications.Results: There was improvement in all UDI domains for sacrospinous-colpopexy and abdominal repair groups with genital prolapse domain of median (interquartile range) 0 (0-10), 0 (0-0) respectively. Eighty-nine percent of abdominal repair group and 85% of sacrospinous-colpopexy group reported scale of 1 or 2 on PGI-I scale at 1-year follow-up. PGI-I score and improvements in UDI domains were maintained till 5-year follow-up. The anatomic success rate at 1-year follow-up was 97.9% in abdominal repair group and 78.2% in the sacrospinous-colpopexy group. No long-term mesh complications were detected in mesh augmented abdominal repair over the whole follow-up periods.Conclusion: The resulting meritorious functional and anatomical outcomes favor adoption of our proposed selection criteria in the initiation of guidelines and recommendations for managing vaginal apical prolapse

Tumeur Germinale De L\'espace Para Pharynge : A Propos D\'un Cas

Les tumeurs germinales à localisation cervico-faciale sont rares. Nous rapportons l\'observation d\'une une fillette de 7 ans porteuse d\'une tumeur maligne à cellules germinales de l\'espace para-pharyngé droit traité par chimiothérapie. Les particularités étiopathogéniques, thérapeutiques, et pronostiques de cette tumeur sont rappelées après une revue des données de la littérature.Extragonadal germ cell tumors of the head and neck are very rare. We report the case of a 7-year-old girl with malignant germ cell tumor of the right parapharyngeal space treated by chemotherapy. Etiopathogenic, therapeutic, and prognostic characteristics of this tumour are recalled after a review of the literature data. Keywords: Extragonadal germ cell tumors, parapharyngeal tumors. Journal Tunisien d\'ORL et de chirurgie cervico-faciale Vol. 18 2007: pp. 61-6

Adenome pleomorphe a localisation extra-parotidienne

Objectives : Pleormorphic adenoma is a benign tumor of salivary gland. It mainly occurs in the parotid gland. The submandibular and minor salivary glands are rarely sites of occurrence. We describe the features of pleomorphic adenoma occurring at these sites.Material and methods: Between 2000 and 2009, 15 cases of pleomorphic adenoma occurring externly to the parotid have been collected.Results: Tumors were seen in the submandibular gland in 40 % of cases, in the hard palate in 33 % of cases, in the upper lip in 20 % of cases and in the parapharyngeal space in 7 % of cases. The mean age of patients was 48 years. The majority of cases were female. All patients were operated. We didn't report recurrence or malignants tumors after one year follow-up.Conclusion: After the parotid gland, the most common site of a pleomorphic adenoma is the submandibular gland followedby minor salivary gland of palate and lips. Each localisation has his clinical and therapeutic particularities.Key words : pleomorphic adenoma, submandibular gland, minor salivary glan

Spatial heterogeneity of leaf wetness duration in winter wheat canopy and its influence on plant disease epidemiology

Leaf wetness duration (LWD) is an important factor influencing the occurrence of plant disease epidemiology. Despite considerable efforts to determine LWD, little attention has been given to study its variability within the canopy. The objective of this study was to evaluate its spatiotemporal variability in wheat fields in a heterogeneous landscape. The spatiotemporal variability of LWD was evaluated in a site close to Arlon (Belgium) during the period May to July 2006 and 2007. LWD measurements were made using a set of flat plate sensors deployed at five different distances from a 18 m high hedge (5, 10, 20, 50, 100 m). Each set of two sensors was placed horizontally close the flag leaf. In addition, we collected the amount of dew water that deposited on rigid epoxy plates placed next to each sensors. Experimental results showed that LWD measurements revealed substantial heterogeneity among sensor positions. LWD is longer for sensors closer to the hedge mainly because of its shadowing effect. 3 to 4 hours of difference was observed between sensors located at 5 m and those located at 100 m, and besides, a significant quantitative difference (p < 0.0001) of dew deposit was observed between area beside hedge and those placed at 100 m. In summary, this study provides new information on how wetness is distributed on wheat leaves according to the distance from a hedge. This leads to local microclimate conditions that will contribute to the disease spatial heterogeneity

Microscopic modelling of doped manganites

Colossal magneto-resistance manganites are characterised by a complex interplay of charge, spin, orbital and lattice degrees of freedom. Formulating microscopic models for these compounds aims at meeting to conflicting objectives: sufficient simplification without excessive restrictions on the phase space. We give a detailed introduction to the electronic structure of manganites and derive a microscopic model for their low energy physics. Focussing on short range electron-lattice and spin-orbital correlations we supplement the modelling with numerical simulations.Comment: 20 pages, 10 figs, accepted for publ. in New J. Phys., Focus issue on Orbital Physic

All-sky Search for High-Energy Neutrinos from Gravitational Wave Event GW170104 with the ANTARES Neutrino Telescope

Advanced LIGO detected a significant gravitational wave signal (GW170104) originating from the coalescence of two black holes during the second observation run on January 4$^{\textrm{th}}$, 2017. An all-sky high-energy neutrino follow-up search has been made using data from the ANTARES neutrino telescope, including both upgoing and downgoing events in two separate analyses. No neutrino candidates were found within $\pm500$ s around the GW event time nor any time clustering of events over an extended time window of $\pm3$ months. The non-detection is used to constrain isotropic-equivalent high-energy neutrino emission from GW170104 to less than $\sim4\times 10^{54}$ erg for a $E^{-2}$ spectrum

The ANTARES Collaboration: Contributions to ICRC 2017 Part I: Neutrino astronomy (diffuse fluxes and point sources)

Papers on neutrino astronomy (diffuse fluxes and point sources, prepared for the 35th International Cosmic Ray Conference (ICRC 2017, Busan, South Korea) by the ANTARES Collaboratio