Deep Learning Approach for Intrusion Detection System (IDS) in the Internet of Things (IoT) Network using Gated Recurrent Neural Networks (GRU)

The Internet of Things (IoT) is a complex paradigm where billions of devices are connected to a network. These connected devices form an intelligent system of systems that share the data without human-to-computer or human-to-human interaction. These systems extract meaningful data that can transform human lives, businesses, and the world in significant ways. However, the reality of IoT is prone to countless cyber-attacks in the extremely hostile environment like the internet. The recent hack of 2014 Jeep Cherokee, iStan pacemaker, and a German steel plant are a few notable security breaches. To secure an IoT system, the traditional high-end security solutions are not suitable, as IoT devices are of low storage capacity and less processing power. Moreover, the IoT devices are connected for longer time periods without human intervention. This raises a need to develop smart security solutions which are light-weight, distributed and have a high longevity of service. Rather than per-device security for numerous IoT devices, it is more feasible to implement security solutions for network data. The artificial intelligence theories like Machine Learning and Deep Learning have already proven their significance when dealing with heterogeneous data of various sizes. To substantiate this, in this research, we have applied concepts of Deep Learning and Transmission Control Protocol/Internet Protocol (TCP/IP) to build a light-weight distributed security solution with high durability for IoT network security. First, we have examined the ways of improving IoT architecture and proposed a light-weight and multi-layered design for an IoT network. Second, we have analyzed the existingapplications of Machine Learning and Deep Learning to the IoT and Cyber-Security. Third, we have evaluated deep learning\u27s Gated Recurrent Neural Networks (LSTM and GRU) on the DARPA/KDD Cup \u2799 intrusion detection data set for each layer in the designed architecture. Finally, from the evaluated metrics, we have proposed the best neural network design suitable for the IoT Intrusion Detection System. With an accuracy of 98.91% and False Alarm Rate of 0.76 %, this unique research outperformed the performance results of existing methods over the KDD Cup \u2799 dataset. For this first time in the IoT research, the concepts of Gated Recurrent Neural Networks are applied for the IoT security

Platinum group element mineralization at Musongati (Burundi) : concentration and Pd-Rh distribution in pentlandite

The mafic-ultramafic intrusions of the Karagwe-Ankole belt in Burundi are considered as a new potential source for platinum group elements (PGE). The intrusions have mainly been studied for their PGE potential with regard to PGE concentration, but the mineralogical distribution of PGE has not been examined to the same level. This study focuses on the Pd and Rh distribution in pentlandite of ultramafic rocks of the Musongati layered intrusion. The results are based on whole rock and pentlandite analyses which were incorporated into a mass balance. Palladium proportions in pentlandite vary between 4 and 69%. Rhodium is present in proportions ranging from 1-39% in pentlandite. Other PGE distributions could not be determined in pentlandite due to concentrations below detection limits. The results from this study demonstrate that Pd and Rh are hosted by sulfides since sulfur saturation of the magma occurred early on, perhaps before or simultaneously with the precipitation of silicate minerals. Based on these findings, a preliminary model for the mineralization of PGE in the Musongati intrusion is proposed

Étude numérique de l'interaction des ondes de surface avec les cavités souterraines

L’effondrement des remblais routiers causé par le développement de cavités souterraines autour des ponceaux pose un risque majeur pour la sécurité des usagers et les installations à proximité. La détection de vides peu profonds est devenue l'une des missions récurrentes difficiles en génie civil à cause de la complexité de la réponse sismique d’un remblai routier en présence d’un ponceau et d’éventuelles cavités. Bien que les méthodes non intrusives basées sur les ondes de surface permettent d’estimer efficacement la vitesse des ondes de cisaillement des dépôts de sol, de nombreux défis sont rencontrés lorsqu'il s'agit de juger de la présence d'une inhomogénéité latérale locale en raison de la résolution limitée des approches géophysiques appliquées. Par conséquent, une étude numérique a été entreprise pour étudier la sensibilité des deux composantes des ondes de Rayleigh (la composante horizontale et la composante verticale désignées X et Z respectivement dans cette étude) et la seule composante des ondes de Love (désignée Y dans cette étude) à un contraste de rigidité (vide) dans différents contextes géologiques. Les accélérations des trois composants sont simulées au moyen du programme de modélisation numérique par éléments finis FLAC3D (Fast Lagrangian Analysis of Continua in 3 Dimensions) pour différentes configurations de modèles en présence et en absence de cavité. Les données sismiques sont traitées avec la transformée de Stockwell généralisée (GST) dans le domaine temps-fréquence. Les résultats sont présentés sous forme des tomographies des courbes de dispersion des vitesses de groupe et de phase pour évaluer l'effet de la cavité et la localiser par rapport à la source. La signature de la cavité a également été étudiée à deux différentes profondeurs à partir du modèle parfaitement homogène. Les distributions de vitesse des trois composants ont révélé des changements négligeables après la création de la plus profonde cavité. Les observations numériques ont démontré que les vitesses de phase sont plus sensibles que la vitesse de groupe aux variations latérales de densité. De plus, on peut conclure que les trois composants ont révélé des distributions de vitesse de phase perceptibles et distinctes en présence d’un vide. La composante X s'est également avérée plus efficace pour localiser la cavité. Les résultats de cette étude numérique suggèrent l’acquisition des trois composantes lors des relevés sismiques sur terrain et d’intégrer simultanément ses trois composantes lors de l’analyse pour une plus grande fiabilité.Abstract : A road collapse caused by the development of near-surface cavities surrounding buried culverts poses a major hazard to road users’ safety and nearby facilities. The complexity of the road embankment seismic response has made it a challenging recurring mission in civil engineering to detect shallow voids. Although non-intrusive surface wave methods afford reliable shear wave velocity estimates of the subsurface materials, many challenges are encountered when judging the presence of a local lateral heterogeneity due to the limited resolution of the applied geophysical approaches. Therefore, a numerical study was conducted to investigate the sensitivity of the two Rayleigh waves components (the horizontal and vertical components, designed as X-component and Z-components, respectively in this study) and the only Love waves component (designed as Y-component in this study) to a contrast of rigidity (void) in different geological contexts. The accelerations of the three components are computed using a finite element commercial code FLAC3D (Fast Lagrangian Analysis of Continua in 3 Dimensions) for different model configurations both with and without a cavity. The seismic data are processed using the Generalized Stockwell transform (GST) in the time-frequency domain. To evaluate the effect of the cavity and locate it with respect to the source offset, the results are presented in the form of tomography maps and the group and phase velocity dispersion curve variations along the inspected array. The cavity signature was also studied at two depths relying on a perfectly homogeneous model. The velocity distribution change of the three components revealed minor changes after creating the deeper cavity. Moreover, the numerical observations demonstrated that the phase velocity is considerably more susceptible to lateral density variations than the group velocity. It was concluded that the three components revealed perceptible and distinct phase velocity changes in the presence of the void. The X-component was also found to be more effective in localizing the near and far boundaries of the cavity. The results of this numerical study suggest acquiring the three components during field seismic surveys and integrating the three components simultaneously during the analysis procedure for better efficiency

Discriminative Dictionary Learning with Motion Weber Local Descriptor for Violence Detection

© 1991-2012 IEEE. Automatic violence detection from video is a hot topic for many video surveillance applications. However, there has been little success in developing an algorithm that can detect violence in surveillance videos with high performance. In this paper, following our recently proposed idea of motion Weber local descriptor (WLD), we make two major improvements and propose a more effective and efficient algorithm for detecting violence from motion images. First, we propose an improved WLD (IWLD) to better depict low-level image appearance information, and then extend the spatial descriptor IWLD by adding a temporal component to capture local motion information and hence form the motion IWLD. Second, we propose a modified sparse-representation-based classification model to both control the reconstruction error of coding coefficients and minimize the classification error. Based on the proposed sparse model, a class-specific dictionary containing dictionary atoms corresponding to the class labels is learned using class labels of training samples. With this learned dictionary, not only the representation residual but also the representation coefficients become discriminative. A classification scheme integrating the modified sparse model is developed to exploit such discriminative information. The experimental results on three benchmark data sets have demonstrated the superior performance of the proposed approach over the state of the arts

Identification of Biometric-Based Continuous user Authentication and Intrusion Detection System for Cluster Based Manet

Mobile ad hoc is an infrastructure less dynamic network used in many applications; it has been targets of various attacks and makes security problems. This work aims to provide an enhanced level of security by using the prevention based and detection based approaches such as authentication and intrusion detection. The multi-model biometric technology is used for continuous authentication and intrusion detection in high security cluster based MANET. In this paper, an attempt has been made to combine continuous authentication and intrusion detection. In this proposed scheme, Dempster-Shafer theory is used for data fusion because more than one device needs to be chosen and their observation can be used to increase observation accuracy

One-Class Classification for Intrusion Detection on Vehicular Networks

Controller Area Network bus systems within vehicular networks are not equipped with the tools necessary to ward off and protect themselves from modern cyber-security threats. Work has been done on using machine learning methods to detect and report these attacks, but common methods are not robust towards unknown attacks. These methods usually rely on there being a sufficient representation of attack data, which may not be available due to there either not being enough data present to adequately represent its distribution or the distribution itself is too diverse in nature for there to be a sufficient representation of it. With the use of one-class classification methods, this issue can be mitigated as only normal data is required to train a model for the detection of anomalous instances. Research has been done on the efficacy of these methods, most notably One-Class Support Vector Machine and Support Vector Data Description, but many new extensions of these works have been proposed and have yet to be tested for injection attacks in vehicular networks. In this paper, we investigate the performance of various state-of-the-art one-class classification methods for detecting injection attacks on Controller Area Network bus traffic. We investigate the effectiveness of these techniques on attacks launched on Controller Area Network buses from two different vehicles during normal operation and while being attacked. We observe that the Subspace Support Vector Data Description method outperformed all other tested methods with a Gmean of about 85%.Comment: 7 pages, 2 figures, 4 tables. Accepted at IEEE Symposium Series on Computational Intelligence 202

Management And Security Of Multi-Cloud Applications

Single cloud management platform technology has reached maturity and is quite successful in information technology applications. Enterprises and application service providers are increasingly adopting a multi-cloud strategy to reduce the risk of cloud service provider lock-in and cloud blackouts and, at the same time, get the benefits like competitive pricing, the flexibility of resource provisioning and better points of presence. Another class of applications that are getting cloud service providers increasingly interested in is the carriers\u27 virtualized network services. However, virtualized carrier services require high levels of availability and performance and impose stringent requirements on cloud services. They necessitate the use of multi-cloud management and innovative techniques for placement and performance management. We consider two classes of distributed applications – the virtual network services and the next generation of healthcare – that would benefit immensely from deployment over multiple clouds. This thesis deals with the design and development of new processes and algorithms to enable these classes of applications. We have evolved a method for optimization of multi-cloud platforms that will pave the way for obtaining optimized placement for both classes of services. The approach that we have followed for placement itself is predictive cost optimized latency controlled virtual resource placement for both types of applications. To improve the availability of virtual network services, we have made innovative use of the machine and deep learning for developing a framework for fault detection and localization. Finally, to secure patient data flowing through the wide expanse of sensors, cloud hierarchy, virtualized network, and visualization domain, we have evolved hierarchical autoencoder models for data in motion between the IoT domain and the multi-cloud domain and within the multi-cloud hierarchy

Hydrothermal circulation from very high to low temperatures in the lower oceanic crust : evidence from layered gabbros from the Oman ophiolite and from partial melting experiments on oceanic gabbros

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