Advances in Data Mining Knowledge Discovery and Applications

Advances in Data Mining Knowledge Discovery and Applications aims to help data miners, researchers, scholars, and PhD students who wish to apply data mining techniques. The primary contribution of this book is highlighting frontier fields and implementations of the knowledge discovery and data mining. It seems to be same things are repeated again. But in general, same approach and techniques may help us in different fields and expertise areas. This book presents knowledge discovery and data mining applications in two different sections. As known that, data mining covers areas of statistics, machine learning, data management and databases, pattern recognition, artificial intelligence, and other areas. In this book, most of the areas are covered with different data mining applications. The eighteen chapters have been classified in two parts: Knowledge Discovery and Data Mining Applications

Secure identity management in structured peer-to-peer (P2P) networks

Structured Peer-to-Peer (P2P) networks were proposed to solve routing problems of big distributed infrastructures. But the research community has been questioning their security for years. Most prior work in security services was focused on secure routing, reputation systems, anonymity, etc. However, the proper management of identities is an important prerequisite to provide most of these security services. The existence of anonymous nodes and the lack of a centralized authority capable of monitoring (and/or punishing) nodes make these systems more vulnerable against selfish or malicious behaviors. Moreover, these improper usages cannot be faced only with data confidentiality, nodes authentication, non-repudiation, etc. In particular, structured P2P networks should follow the following secure routing primitives: (1) secure maintenance of routing tables, (2) secure routing of messages, and (3) secure identity assignment to nodes. But the first two problems depend in some way on the third one. If nodes’ identifiers can be chosen by users without any control, these networks can have security and operational problems. Therefore, like any other network or service, structured P2P networks require a robust access control to prevent potential attackers joining the network and a robust identity assignment system to guarantee their proper operation. In this thesis, firstly, we analyze the operation of the current structured P2P networks when managing identities in order to identify what security problems are related to the nodes’ identifiers within the overlay, and propose a series of requirements to be accomplished by any generated node ID to provide more security to a DHT-based structured P2P network. Secondly, we propose the use of implicit certificates to provide more security and to exploit the improvement in bandwidth, storage and performance that these certificates present compared to explicit certificates, design three protocols to assign nodes’ identifiers avoiding the identified problems, while maintaining user anonymity and allowing users’ traceability. Finally, we analyze the operation of the most used mechanisms to distribute revocation data in the Internet, with special focus on the proposed systems to work in P2P networks, and design a new mechanism to distribute revocation data more efficiently in a structured P2P network.Las redes P2P estructuradas fueron propuestas para solventar problemas de enrutamiento en infraestructuras de grandes dimensiones pero su nivel de seguridad lleva años siendo cuestionado por la comunidad investigadora. La mayor parte de los trabajos que intentan mejorar la seguridad de estas redes se han centrado en proporcionar encaminamiento seguro, sistemas de reputación, anonimato de los usuarios, etc. Sin embargo, la adecuada gestión de las identidades es un requisito sumamente importante para proporcionar los servicios mencionados anteriormente. La existencia de nodos anónimos y la falta de una autoridad centralizada capaz de monitorizar (y/o penalizar) a los nodos hace que estos sistemas sean más vulnerables que otros a comportamientos maliciosos por parte de los usuarios. Además, esos comportamientos inadecuados no pueden ser detectados proporcionando únicamente confidencialidad de los datos, autenticación de los nodos, no repudio, etc. Las redes P2P estructuradas deberían seguir las siguientes primitivas de enrutamiento seguro: (1) mantenimiento seguro de las tablas de enrutamiento, (2) enrutamiento seguro de los mensajes, and (3) asignación segura de las identidades. Pero la primera de los dos primitivas depende de alguna forma de la tercera. Si las identidades de los nodos pueden ser elegidas por sus usuarios sin ningún tipo de control, muy probablemente aparecerán muchos problemas de funcionamiento y seguridad. Por lo tanto, de la misma forma que otras redes y servicios, las redes P2P estructuradas requieren de un control de acceso robusto para prevenir la presencia de atacantes potenciales, y un sistema robusto de asignación de identidades para garantizar su adecuado funcionamiento. En esta tesis, primero de todo analizamos el funcionamiento de las redes P2P estructuradas basadas en el uso de DHTs (Tablas de Hash Distribuidas), cómo gestionan las identidades de sus nodos, identificamos qué problemas de seguridad están relacionados con la identificación de los nodos y proponemos una serie de requisitos para generar identificadores de forma segura. Más adelante proponemos el uso de certificados implícitos para proporcionar más seguridad y explotar las mejoras en consumo de ancho de banda, almacenamiento y rendimiento que proporcionan estos certificados en comparación con los certificados explícitos. También hemos diseñado tres protocolos de asignación segura de identidades, los cuales evitan la mayor parte de los problemas identificados mientras mantienen el anonimato de los usuarios y la trazabilidad. Finalmente hemos analizado el funcionamiento de la mayoría de los mecanismos utilizados para distribuir datos de revocación en Internet, con especial interés en los sistemas propuestos para operar en redes P2P, y hemos diseñado un nuevo mecanismo para distribuir datos de revocación de forma más eficiente en redes P2P estructuradas.Postprint (published version

Deep Learning for Network Traffic Monitoring and Analysis (NTMA): A Survey

Modern communication systems and networks, e.g., Internet of Things (IoT) and cellular networks, generate a massive and heterogeneous amount of traffic data. In such networks, the traditional network management techniques for monitoring and data analytics face some challenges and issues, e.g., accuracy, and effective processing of big data in a real-time fashion. Moreover, the pattern of network traffic, especially in cellular networks, shows very complex behavior because of various factors, such as device mobility and network heterogeneity. Deep learning has been efficiently employed to facilitate analytics and knowledge discovery in big data systems to recognize hidden and complex patterns. Motivated by these successes, researchers in the field of networking apply deep learning models for Network Traffic Monitoring and Analysis (NTMA) applications, e.g., traffic classification and prediction. This paper provides a comprehensive review on applications of deep learning in NTMA. We first provide fundamental background relevant to our review. Then, we give an insight into the confluence of deep learning and NTMA, and review deep learning techniques proposed for NTMA applications. Finally, we discuss key challenges, open issues, and future research directions for using deep learning in NTMA applications.publishedVersio

Big data for digital forensics

Digital Forensics and its sub-branch Network Forensics are important and relevant topics which have gained further attention with the DDoS attacks delivered by botnets. This work focuses on a novel IDS solution called: SLIPS. This is a free software that uses Machine Learning to detect malicious behaviors in a network with the use of Markov Chain based detection and previously trained models. A major limitation of SLIPS lies on its performance, and this work also touches on the topic of Big Data, and more specifically MapReduce, in order to aid SLIPS with a better resource utilization. With the redistribution of SLIPS tasks across workers, adding a pre-processing of data, the proposed solution using MapReduce presented performance improvements of up to 433 times with the datasets tested

Robustness of Image-Based Malware Analysis

In previous work, “gist descriptor” features extracted from images have been used in malware classification problems and have shown promising results. In this research, we determine whether gist descriptors are robust with respect to malware obfuscation techniques, as compared to Convolutional Neural Networks (CNN) trained directly on malware images. Using the Python Image Library (PIL), we create images from malware executables and from malware that we obfuscate. We conduct experiments to compare classifying these images with a CNN as opposed to extracting the gist descriptor features from these images to use in classification. For the gist descriptors, we consider a variety of classification algorithms including k-nearest neighbors, random forest, support vector machine, and multi-layer perceptron. We find that gist descriptors are more robust than CNNs, with respect to the obfuscation techniques that we consider