Detection and Counter Measure of AL-DDoS Attacks in Web Traffic

Distributed Denial-of-Service (DDoS) assaults are a developing danger crosswise over Internet, disturbing access to Information and administrations. Presently days, these assaults are focusing on the application layer. Aggressors are utilizing systems that are exceptionally hard to recognize and relieve. In this task propose another technique to recognize AL-DDoS assaults. This work separates itself from past techniques by considering AL-DDoS assault location in overwhelming spine activity. In addition, the identification of AL-DDoS assaults is effectively deceived by glimmer group movement. By analyzing the entropy of AL-DDoS assaults and glimmer swarms, these model output be utilized to perceive the genuine AL-DDoS assaults. With a quick AL-DDoS identification speed, the channel is equipped for letting the real demands through yet the assault movement is halted

Intelligent feature selection for detecting http/2 denial of service attacks

Intrusion-detection systems employ machine learning techniques to classify traffic into attack and legitimate. Network flooding attacks can leverage the new web communications protocol (HTTP/2) to bypass intrusion-detection systems. This creates an urgent demand to understand HTTP/2 characteristics and to devise customised cyber-attack detection schemes. This paper proposes Step Sister; a technique to generate an optimum network traffic feature set for network intrusion detection. The proposed technique demonstrates that a consistent set of features are selected for a given HTTP/2 dataset. This allows intrusion-detection systems to classify previously unseen network traffic samples with fewer false alarm than when techniques used in literature were employed. The results show that the proposed technique yields a set of features that, when used for network traffic classification, yields low numbers of false alarms

Taking Back the Internet: Defeating DDoS and Adverse Network Conditions via Reactive BGP Routing

In this work, we present Nyx, a system for mitigating Distributed Denial of Service (DDoS) attacks by routing critical traffic from known benign networks around links under attack from a massively distributed botnet. Nyx alters how Autonomous Systems (ASes) handle route selection and advertisement in the Border Gateway Protocol (BGP) in order to achieve isolation of critical traffic away from congested links onto alternative, less congested paths. Our system controls outbound paths through the normal process of BGP path selection, while return paths from critical ASes are controlled through the use of existing traffic engineering techniques. To prevent alternative paths from including attacked network links, Nyx employs strategic lying in a manner that is functional in the presence of RPKI. Our system only exposes the alternate path to the networks needed for forwarding and those networks\u27 customer cones, thus strategically reducing the number of ASes outside of the critical AS that receive the alternative path. By leaving the path taken by malicious traffic unchanged and limiting the amount of added traffic load placed on the alternate path, our system causes less than 10 ASes on average to be disturbed by our inbound traffic migration.Nyx is the first system that scalably and effectively mitigates transit-link DDoS attacks that cannot be handled by existing and costly traffic filtering or prioritization techniques. Unlike the prior state of the art, Nyx is highly deployable, requiring only minor changes to router policies at the deployer, and requires no assistance from external networks. Using our own Internet-scale simulator, we find that in more than 98% of cases our system can successfully migrate critical traffic off of the network segments under transit-link DDoS. In over 98% of cases, the alternate path provides some degree of relief over the original path. Finally, in over 70% of cases where Nyx can migrate critical traffic off attacked segments, the new path has sufficient capacity to handle the entire traffic load without congestion

Contribuciones para la Detección de Ataques Distribuidos de Denegación de Servicio (DDoS) en la Capa de Aplicación

Se analizaron seis aspectos sobre la detección de ataques DDoS: técnicas, variables, herramientas, ubicación de implementación, punto en el tiempo y precisión de detección. Este análisis permitió realizar una contribución útil al diseño de una estrategia adecuada para neutralizar estos ataques. En los últimos años, estos ataques se han dirigido hacia la capa de aplicación. Este fenómeno se debe principalmente a la gran cantidad de herramientas para la generación de este tipo de ataque. Por ello, además, en este trabajo se propone una alternativa de detección basada en el dinamismo del usuario web. Para esto, se evaluaron las características del dinamismo del usuario extraídas de las funciones del mouse y del teclado. Finalmente, el presente trabajo propone un enfoque de detección de bajo costo que consta de dos pasos: primero, las características del usuario se extraen en tiempo real mientras se navega por la aplicación web; en segundo lugar, cada característica extraída es utilizada por un algoritmo de orden (O1) para diferenciar a un usuario real de un ataque DDoS. Los resultados de las pruebas con las herramientas de ataque LOIC, OWASP y GoldenEye muestran que el método propuesto tiene una eficacia de detección del 100% y que las características del dinamismo del usuario de la web permiten diferenciar entre un usuario real y un robot

Denial-of-service attack modelling and detection for HTTP/2 services

Businesses and society alike have been heavily dependent on Internet-based services, albeit with experiences of constant and annoying disruptions caused by the adversary class. A malicious attack that can prevent establishment of Internet connections to web servers, initiated from legitimate client machines, is termed as a Denial of Service (DoS) attack; volume and intensity of which is rapidly growing thanks to the readily available attack tools and the ever-increasing network bandwidths. A majority of contemporary web servers are built on the HTTP/1.1 communication protocol. As a consequence, all literature found on DoS attack modelling and appertaining detection techniques, addresses only HTTP/1.x network traffic. This thesis presents a model of DoS attack traffic against servers employing the new communication protocol, namely HTTP/2. The HTTP/2 protocol significantly differs from its predecessor and introduces new messaging formats and data exchange mechanisms. This creates an urgent need to understand how malicious attacks including Denial of Service, can be launched against HTTP/2 services. Moreover, the ability of attackers to vary the network traffic models to stealthy affects web services, thereby requires extensive research and modelling. This research work not only provides a novel model for DoS attacks against HTTP/2 services, but also provides a model of stealthy variants of such attacks, that can disrupt routine web services. Specifically, HTTP/2 traffic patterns that consume computing resources of a server, such as CPU utilisation and memory consumption, were thoroughly explored and examined. The study presents four HTTP/2 attack models. The first being a flooding-based attack model, the second being a distributed model, the third and fourth are variant DoS attack models. The attack traffic analysis conducted in this study employed four machine learning techniques, namely Naïve Bayes, Decision Tree, JRip and Support Vector Machines. The HTTP/2 normal traffic model portrays online activities of human users. The model thus formulated was employed to also generate flash-crowd traffic, i.e. a large volume of normal traffic that incapacitates a web server, similar in fashion to a DoS attack, albeit with non-malicious intent. Flash-crowd traffic generated based on the defined model was used to populate the dataset of legitimate network traffic, to fuzz the machine learning-based attack detection process. The two variants of DoS attack traffic differed in terms of the traffic intensities and the inter-packet arrival delays introduced to better analyse the type and quality of DoS attacks that can be launched against HTTP/2 services. A detailed analysis of HTTP/2 features is also presented to rank relevant network traffic features for all four traffic models presented. These features were ranked based on legitimate as well as attack traffic observations conducted in this study. The study shows that machine learning-based analysis yields better classification performance, i.e. lower percentage of incorrectly classified instances, when the proposed HTTP/2 features are employed compared to when HTTP/1.1 features alone are used. The study shows how HTTP/2 DoS attack can be modelled, and how future work can extend the proposed model to create variant attack traffic models that can bypass intrusion-detection systems. Likewise, as the Internet traffic and the heterogeneity of Internet-connected devices are projected to increase significantly, legitimate traffic can yield varying traffic patterns, demanding further analysis. The significance of having current legitimate traffic datasets, together with the scope to extend the DoS attack models presented herewith, suggest that research in the DoS attack analysis and detection area will benefit from the work presented in this thesis

Detection and defense of application-layer DDoS attacks in backbone web traffic

Web servers are usually located in a well-organized data center where these servers connect with the outside Internet directly through backbones. Meanwhile, the application-layer distributed denials of service (AL-DDoS) attacks are critical threats to the Internet, particularly to those business web servers. Currently, there are some methods designed to handle the AL-DDoS attacks, but most of them cannot be used in heavy backbones. In this paper, we propose a new method to detect AL-DDoS attacks. Our work distinguishes itself from previous methods by considering AL-DDoS attack detection in heavy backbone traffic. Besides, the detection of AL-DDoS attacks is easily misled by flash crowd traffic. In order to overcome this problem, our proposed method constructs a Real-time Frequency Vector (RFV) and real-timely characterizes the traffic as a set of models. By examining the entropy of AL-DDoS attacks and flash crowds, these models can be used to recognize the real AL-DDoS attacks. We integrate the above detection principles into a modularized defense architecture, which consists of a head-end sensor, a detection module and a traffic filter. With a swift AL-DDoS detection speed, the filter is capable of letting the legitimate requests through but the attack traffic is stopped. In the experiment, we adopt certain episodes of real traffic from Sina and Taobao to evaluate our AL-DDoS detection method and architecture. Compared with previous methods, the results show that our approach is very effective in defending AL-DDoS attacks at backbones. © 2013 Elsevier B.V. All rights reserved

Leveraging Conventional Internet Routing Protocol Behavior to Defeat DDoS and Adverse Networking Conditions

The Internet is a cornerstone of modern society. Yet increasingly devastating attacks against the Internet threaten to undermine the Internet\u27s success at connecting the unconnected. Of all the adversarial campaigns waged against the Internet and the organizations that rely on it, distributed denial of service, or DDoS, tops the list of the most volatile attacks. In recent years, DDoS attacks have been responsible for large swaths of the Internet blacking out, while other attacks have completely overwhelmed key Internet services and websites. Core to the Internet\u27s functionality is the way in which traffic on the Internet gets from one destination to another. The set of rules, or protocol, that defines the way traffic travels the Internet is known as the Border Gateway Protocol, or BGP, the de facto routing protocol on the Internet. Advanced adversaries often target the most used portions of the Internet by flooding the routes benign traffic takes with malicious traffic designed to cause widespread traffic loss to targeted end users and regions. This dissertation focuses on examining the following thesis statement. Rather than seek to redefine the way the Internet works to combat advanced DDoS attacks, we can leverage conventional Internet routing behavior to mitigate modern distributed denial of service attacks. The research in this work breaks down into a single arc with three independent, but connected thrusts, which demonstrate that the aforementioned thesis is possible, practical, and useful. The first thrust demonstrates that this thesis is possible by building and evaluating Nyx, a system that can protect Internet networks from DDoS using BGP, without an Internet redesign and without cooperation from other networks. This work reveals that Nyx is effective in simulation for protecting Internet networks and end users from the impact of devastating DDoS. The second thrust examines the real-world practicality of Nyx, as well as other systems which rely on real-world BGP behavior. Through a comprehensive set of real-world Internet routing experiments, this second thrust confirms that Nyx works effectively in practice beyond simulation as well as revealing novel insights about the effectiveness of other Internet security defensive and offensive systems. We then follow these experiments by re-evaluating Nyx under the real-world routing constraints we discovered. The third thrust explores the usefulness of Nyx for mitigating DDoS against a crucial industry sector, power generation, by exposing the latent vulnerability of the U.S. power grid to DDoS and how a system such as Nyx can protect electric power utilities. This final thrust finds that the current set of exposed U.S. power facilities are widely vulnerable to DDoS that could induce blackouts, and that Nyx can be leveraged to reduce the impact of these targeted DDoS attacks

The Proceedings of 15th Australian Information Security Management Conference, 5-6 December, 2017, Edith Cowan University, Perth, Australia

Conference Foreword The annual Security Congress, run by the Security Research Institute at Edith Cowan University, includes the Australian Information Security and Management Conference. Now in its fifteenth year, the conference remains popular for its diverse content and mixture of technical research and discussion papers. The area of information security and management continues to be varied, as is reflected by the wide variety of subject matter covered by the papers this year. The papers cover topics from vulnerabilities in “Internet of Things” protocols through to improvements in biometric identification algorithms and surveillance camera weaknesses. The conference has drawn interest and papers from within Australia and internationally. All submitted papers were subject to a double blind peer review process. Twenty two papers were submitted from Australia and overseas, of which eighteen were accepted for final presentation and publication. We wish to thank the reviewers for kindly volunteering their time and expertise in support of this event. We would also like to thank the conference committee who have organised yet another successful congress. Events such as this are impossible without the tireless efforts of such people in reviewing and editing the conference papers, and assisting with the planning, organisation and execution of the conference. To our sponsors, also a vote of thanks for both the financial and moral support provided to the conference. Finally, thank you to the administrative and technical staff, and students of the ECU Security Research Institute for their contributions to the running of the conference

Estudio del marketing estratégico para el emprendimiento. El caso de estudio de la zona rural de Manta en Ecuador

La presente investigación se enfoca en un estudio de marketing para favorecer el emprendimiento rural en el Ecuador, desde el enfoque del marketing estratégico que desarrolla actividades asociadas con las necesidades del mercado, los segmentos, la competencia interna y externa, la innovación y la mejora continua, y desde el entorno de las competencias emprendedoras, direccionadas a la formación y el uso de ambientes digitales para la generación de emprendimiento productivo y sostenible en el territorio rural. Se ha determinado que Ecuador es uno de los países latinoamericanos con la Tasa de Actividad Emprendedora Temprana más alta (36,2%), tan solo superado por Chile. De la misma forma, es notable que el 42,4% de los emprendimientos se generen en las zonas rurales del país. Por lo tanto, el objetivo de la presente tesis doctoral consiste en establecer un análisis propositivo del emprendimiento rural, desde el enfoque del marketing estratégico, que fomente el desarrollo sostenible de los emprendedores de las zonas rurales de Manta en Ecuador. En este sentido, la investigación se desarrolló mediante una metodología con enfoque mixto, que consistió en la aplicación de entrevistas dirigidas a los emprendedores rurales de la ciudad de Manta (Oferta). Asimismo, se realizaron encuestas dirigidas a los consumidores para analizar su comportamiento (Demanda) y tendencias respecto a los productos ofertados por los emprendimientos rurales. A partir del estudio metodológico realizado, se pudo generar una propuesta de planificación, desarrollo, aplicación y evaluación de un programa de marketing estratégico digital para el crecimiento sostenible del emprendimiento rural en el Ecuador