Иерархическая гибридизация бинарных классификаторов для выявления аномальных сетевых соединений

The paper considers a generalized hybrid approach for constructing a set of classification rules through the example of detection of anomalous network connections. There are five stages in the proposed technique. The first stage involves the setting of adaptive classifiers. At the second stage the signature analysis, creation of network connections and formation of network parameters are performed. The third stage is preprocessing of network parameters. At the fourth stage bypassing of a classifier tree in width is performed together with training or testing. The fifth stage is a detection of anomalous network connections. The distinctive features of the proposed technique are the possibility to set an arbitrary nesting of classifiers in each other and a lazy involvement of classifiers due to descending cascade learning of a general classifier fusion. The results of the experiments with the use of an open data set for calculating the performance rates of detection and classification of network anomalies are provided.В статье предлагается обобщенный гибридный подход к построению коллектива классификационных правил на примере решения задачи выявления аномальных сетевых соединений. Выделяется пять этапов в рассматриваемой методике. Первый этап включает в себя настройку адаптивных классификаторов. На втором этапе выполняется сигнатурный анализ, сборка сетевых соединений и формирование сетевых параметров. Третий этап заключается в предобработке сетевых параметров. На четвертом этапе осуществляется обход в ширину дерева классификаторов совместно с их обучением или тестированием. На пятом этапе выявляются аномальные сетевые соединения. Особенностями предлагаемой методики являются возможность задания произвольной вложенности классификаторов друг в друга и ленивое подключение классификаторов благодаря нисходящему каскадному обучению общего коллектива классификационных правил. Приводятся результаты экспериментов с использованием открытого набора данных для вычисления показателей эффективности обнаружения и классификации сетевых аномалий

The use of computational intelligence for security in named data networking

Information-Centric Networking (ICN) has recently been considered as a promising paradigm for the next-generation Internet, shifting from the sender-driven end-to-end communication paradigma to a receiver-driven content retrieval paradigm. In ICN, content -rather than hosts, like in IP-based design- plays the central role in the communications. This change from host-centric to content-centric has several significant advantages such as network load reduction, low dissemination latency, scalability, etc. One of the main design requirements for the ICN architectures -since the beginning of their design- has been strong security. Named Data Networking (NDN) (also referred to as Content-Centric Networking (CCN) or Data-Centric Networking (DCN)) is one of these architectures that are the focus of an ongoing research effort that aims to become the way Internet will operate in the future. Existing research into security of NDN is at an early stage and many designs are still incomplete. To make NDN a fully working system at Internet scale, there are still many missing pieces to be filled in. In this dissertation, we study the four most important security issues in NDN in order to defense against new forms of -potentially unknown- attacks, ensure privacy, achieve high availability, and block malicious network traffics belonging to attackers or at least limit their effectiveness, i.e., anomaly detection, DoS/DDoS attacks, congestion control, and cache pollution attacks. In order to protect NDN infrastructure, we need flexible, adaptable and robust defense systems which can make intelligent -and real-time- decisions to enable network entities to behave in an adaptive and intelligent manner. In this context, the characteristics of Computational Intelligence (CI) methods such as adaption, fault tolerance, high computational speed and error resilient against noisy information, make them suitable to be applied to the problem of NDN security, which can highlight promising new research directions. Hence, we suggest new hybrid CI-based methods to make NDN a more reliable and viable architecture for the future Internet.Information-Centric Networking (ICN) ha sido recientemente considerado como un paradigma prometedor parala nueva generación de Internet, pasando del paradigma de la comunicación de extremo a extremo impulsada por el emisora un paradigma de obtención de contenidos impulsada por el receptor. En ICN, el contenido (más que los nodos, como sucede en redes IPactuales) juega el papel central en las comunicaciones. Este cambio de "host-centric" a "content-centric" tiene varias ventajas importantes como la reducción de la carga de red, la baja latencia, escalabilidad, etc. Uno de los principales requisitos de diseño para las arquitecturas ICN (ya desde el principiode su diseño) ha sido una fuerte seguridad. Named Data Networking (NDN) (también conocida como Content-Centric Networking (CCN) o Data-Centric Networking (DCN)) es una de estas arquitecturas que son objetode investigación y que tiene como objetivo convertirse en la forma en que Internet funcionará en el futuro. Laseguridad de NDN está aún en una etapa inicial. Para hacer NDN un sistema totalmente funcional a escala de Internet, todavía hay muchas piezas que faltan por diseñar. Enesta tesis, estudiamos los cuatro problemas de seguridad más importantes de NDN, para defendersecontra nuevas formas de ataques (incluyendo los potencialmente desconocidos), asegurar la privacidad, lograr una alta disponibilidad, y bloquear los tráficos de red maliciosos o al menos limitar su eficacia. Estos cuatro problemas son: detección de anomalías, ataques DoS / DDoS, control de congestión y ataques de contaminación caché. Para solventar tales problemas necesitamos sistemas de defensa flexibles, adaptables y robustos que puedantomar decisiones inteligentes en tiempo real para permitir a las entidades de red que se comporten de manera rápida e inteligente. Es por ello que utilizamos Inteligencia Computacional (IC), ya que sus características (la adaptación, la tolerancia a fallos, alta velocidad de cálculo y funcionamiento adecuado con información con altos niveles de ruido), la hace adecuada para ser aplicada al problema de la seguridad ND