844 research outputs found

    CALD : surviving various application-layer DDoS attacks that mimic flash crowd

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    Distributed denial of service (DDoS) attack is a continuous critical threat to the Internet. Derived from the low layers, new application-layer-based DDoS attacks utilizing legitimate HTTP requests to overwhelm victim resources are more undetectable. The case may be more serious when suchattacks mimic or occur during the flash crowd event of a popular Website. In this paper, we present the design and implementation of CALD, an architectural extension to protect Web servers against various DDoS attacks that masquerade as flash crowds. CALD provides real-time detection using mess tests but is different from other systems that use resembling methods. First, CALD uses a front-end sensor to monitor thetraffic that may contain various DDoS attacks or flash crowds. Intense pulse in the traffic means possible existence of anomalies because this is the basic property of DDoS attacks and flash crowds. Once abnormal traffic is identified, the sensor sends ATTENTION signal to activate the attack detection module. Second, CALD dynamically records the average frequency of each source IP and check the total mess extent. Theoretically, the mess extent of DDoS attacks is larger than the one of flash crowds. Thus, with some parameters from the attack detection module, the filter is capable of letting the legitimate requests through but the attack traffic stopped. Third, CALD may divide the security modules away from the Web servers. As a result, it keeps maximum performance on the kernel web services, regardless of the harassment from DDoS. In the experiments, the records from www.sina.com and www.taobao.com have proved the value of CALD

    FAIR: Forwarding Accountability for Internet Reputability

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    This paper presents FAIR, a forwarding accountability mechanism that incentivizes ISPs to apply stricter security policies to their customers. The Autonomous System (AS) of the receiver specifies a traffic profile that the sender AS must adhere to. Transit ASes on the path mark packets. In case of traffic profile violations, the marked packets are used as a proof of misbehavior. FAIR introduces low bandwidth overhead and requires no per-packet and no per-flow state for forwarding. We describe integration with IP and demonstrate a software switch running on commodity hardware that can switch packets at a line rate of 120 Gbps, and can forward 140M minimum-sized packets per second, limited by the hardware I/O subsystem. Moreover, this paper proposes a "suspicious bit" for packet headers - an application that builds on top of FAIR's proofs of misbehavior and flags packets to warn other entities in the network.Comment: 16 pages, 12 figure

    DDoS Never Dies? An IXP Perspective on DDoS Amplification Attacks

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    DDoS attacks remain a major security threat to the continuous operation of Internet edge infrastructures, web services, and cloud platforms. While a large body of research focuses on DDoS detection and protection, to date we ultimately failed to eradicate DDoS altogether. Yet, the landscape of DDoS attack mechanisms is even evolving, demanding an updated perspective on DDoS attacks in the wild. In this paper, we identify up to 2608 DDoS amplification attacks at a single day by analyzing multiple Tbps of traffic flows at a major IXP with a rich ecosystem of different networks. We observe the prevalence of well-known amplification attack protocols (e.g., NTP, CLDAP), which should no longer exist given the established mitigation strategies. Nevertheless, they pose the largest fraction on DDoS amplification attacks within our observation and we witness the emergence of DDoS attacks using recently discovered amplification protocols (e.g., OpenVPN, ARMS, Ubiquity Discovery Protocol). By analyzing the impact of DDoS on core Internet infrastructure, we show that DDoS can overload backbone-capacity and that filtering approaches in prior work omit 97% of the attack traffic.Comment: To appear at PAM 202

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

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    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

    Addressing practical challenges for anomaly detection in backbone networks

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    Network monitoring has always been a topic of foremost importance for both network operators and researchers for multiple reasons ranging from anomaly detection to tra c classi cation or capacity planning. Nowadays, as networks become more and more complex, tra c increases and security threats reproduce, achieving a deeper understanding of what is happening in the network has become an essential necessity. In particular, due to the considerable growth of cybercrime, research on the eld of anomaly detection has drawn signi cant attention in recent years and tons of proposals have been made. All the same, when it comes to deploying solutions in real environments, some of them fail to meet some crucial requirements. Taking this into account, this thesis focuses on lling this gap between the research and the non-research world. Prior to the start of this work, we identify several problems. First, there is a clear lack of detailed and updated information on the most common anomalies and their characteristics. Second, unawareness of sampled data is still common although the performance of anomaly detection algorithms is severely a ected. Third, operators currently need to invest many work-hours to manually inspect and also classify detected anomalies to act accordingly and take the appropriate mitigation measures. This is further exacerbated due to the high number of false positives and false negatives and because anomaly detection systems are often perceived as extremely complex black boxes. Analysing an issue is essential to fully comprehend the problem space and to be able to tackle it properly. Accordingly, the rst block of this thesis seeks to obtain detailed and updated real-world information on the most frequent anomalies occurring in backbone networks. It rst reports on the performance of di erent commercial systems for anomaly detection and analyses the types of network nomalies detected. Afterwards, it focuses on further investigating the characteristics of the anomalies found in a backbone network using one of the tools for more than half a year. Among other results, this block con rms the need of applying sampling in an operational environment as well as the unacceptably high number of false positives and false negatives still reported by current commercial tools. On the whole, the presence of ampling in large networks for monitoring purposes has become almost mandatory and, therefore, all anomaly detection algorithms that do not take that into account might report incorrect results. In the second block of this thesis, the dramatic impact of sampling on the performance of well-known anomaly detection techniques is analysed and con rmed. However, we show that the results change signi cantly depending on the sampling technique used and also on the common metric selected to perform the comparison. In particular, we show that, Packet Sampling outperforms Flow Sampling unlike previously reported. Furthermore, we observe that Selective Sampling (SES), a sampling technique that focuses on small ows, obtains much better results than traditional sampling techniques for scan detection. Consequently, we propose Online Selective Sampling, a sampling technique that obtains the same good performance for scan detection than SES but works on a per-packet basis instead of keeping all ows in memory. We validate and evaluate our proposal and show that it can operate online and uses much less resources than SES. Although the literature is plenty of techniques for detecting anomalous events, research on anomaly classi cation and extraction (e.g., to further investigate what happened or to share evidence with third parties involved) is rather marginal. This makes it harder for network operators to analise reported anomalies because they depend solely on their experience to do the job. Furthermore, this task is an extremely time-consuming and error-prone process. The third block of this thesis targets this issue and brings it together with the knowledge acquired in the previous blocks. In particular, it presents a system for automatic anomaly detection, extraction and classi cation with high accuracy and very low false positives. We deploy the system in an operational environment and show its usefulness in practice. The fourth and last block of this thesis presents a generalisation of our system that focuses on analysing all the tra c, not only network anomalies. This new system seeks to further help network operators by summarising the most signi cant tra c patterns in their network. In particular, we generalise our system to deal with big network tra c data. In particular, it deals with src/dst IPs, src/dst ports, protocol, src/dst Autonomous Systems, layer 7 application and src/dst geolocation. We rst deploy a prototype in the European backbone network of G EANT and show that it can process large amounts of data quickly and build highly informative and compact reports that are very useful to help comprehending what is happening in the network. Second, we deploy it in a completely di erent scenario and show how it can also be successfully used in a real-world use case where we analyse the behaviour of highly distributed devices related with a critical infrastructure sector.La monitoritzaci o de xarxa sempre ha estat un tema de gran import ancia per operadors de xarxa i investigadors per m ultiples raons que van des de la detecci o d'anomalies fins a la classi caci o d'aplicacions. Avui en dia, a mesura que les xarxes es tornen m es i m es complexes, augmenta el tr ansit de dades i les amenaces de seguretat segueixen creixent, aconseguir una comprensi o m es profunda del que passa a la xarxa s'ha convertit en una necessitat essencial. Concretament, degut al considerable increment del ciberactivisme, la investigaci o en el camp de la detecci o d'anomalies ha crescut i en els darrers anys s'han fet moltes i diverses propostes. Tot i aix o, quan s'intenten desplegar aquestes solucions en entorns reals, algunes d'elles no compleixen alguns requisits fonamentals. Tenint aix o en compte, aquesta tesi se centra a omplir aquest buit entre la recerca i el m on real. Abans d'iniciar aquest treball es van identi car diversos problemes. En primer lloc, hi ha una clara manca d'informaci o detallada i actualitzada sobre les anomalies m es comuns i les seves caracter stiques. En segona inst ancia, no tenir en compte la possibilitat de treballar amb nom es part de les dades (mostreig de tr ansit) continua sent bastant est es tot i el sever efecte en el rendiment dels algorismes de detecci o d'anomalies. En tercer lloc, els operadors de xarxa actualment han d'invertir moltes hores de feina per classi car i inspeccionar manualment les anomalies detectades per actuar en conseqüencia i prendre les mesures apropiades de mitigaci o. Aquesta situaci o es veu agreujada per l'alt nombre de falsos positius i falsos negatius i perqu e els sistemes de detecci o d'anomalies s on sovint percebuts com caixes negres extremadament complexes. Analitzar un tema es essencial per comprendre plenament l'espai del problema i per poder-hi fer front de forma adequada. Per tant, el primer bloc d'aquesta tesi pret en proporcionar informaci o detallada i actualitzada del m on real sobre les anomalies m es freqüents en una xarxa troncal. Primer es comparen tres eines comercials per a la detecci o d'anomalies i se n'estudien els seus punts forts i febles, aix com els tipus d'anomalies de xarxa detectats. Posteriorment, s'investiguen les caracter stiques de les anomalies que es troben en la mateixa xarxa troncal utilitzant una de les eines durant m es de mig any. Entre d'altres resultats, aquest bloc con rma la necessitat de l'aplicaci o de mostreig de tr ansit en un entorn operacional, aix com el nombre inacceptablement elevat de falsos positius i falsos negatius en eines comercials actuals. En general, el mostreig de tr ansit de dades de xarxa ( es a dir, treballar nom es amb una part de les dades) en grans xarxes troncals s'ha convertit en gaireb e obligatori i, per tant, tots els algorismes de detecci o d'anomalies que no ho tenen en compte poden veure seriosament afectats els seus resultats. El segon bloc d'aquesta tesi analitza i confi rma el dram atic impacte de mostreig en el rendiment de t ecniques de detecci o d'anomalies plenament acceptades a l'estat de l'art. No obstant, es mostra que els resultats canvien signi cativament depenent de la t ecnica de mostreig utilitzada i tamb e en funci o de la m etrica usada per a fer la comparativa. Contr ariament als resultats reportats en estudis previs, es mostra que Packet Sampling supera Flow Sampling. A m es, a m es, s'observa que Selective Sampling (SES), una t ecnica de mostreig que se centra en mostrejar fluxes petits, obt e resultats molt millors per a la detecci o d'escanejos que no pas les t ecniques tradicionals de mostreig. En conseqü encia, proposem Online Selective Sampling, una t ecnica de mostreig que obt e el mateix bon rendiment per a la detecci o d'escanejos que SES, per o treballa paquet per paquet enlloc de mantenir tots els fluxes a mem oria. Despr es de validar i evaluar la nostra proposta, demostrem que es capa c de treballar online i utilitza molts menys recursos que SES. Tot i la gran quantitat de tècniques proposades a la literatura per a la detecci o d'esdeveniments an omals, la investigaci o per a la seva posterior classi caci o i extracci o (p.ex., per investigar m es a fons el que va passar o per compartir l'evid encia amb tercers involucrats) es m es aviat marginal. Aix o fa que sigui m es dif cil per als operadors de xarxa analalitzar les anomalies reportades, ja que depenen unicament de la seva experi encia per fer la feina. A m es a m es, aquesta tasca es un proc es extremadament lent i propens a errors. El tercer bloc d'aquesta tesi se centra en aquest tema tenint tamb e en compte els coneixements adquirits en els blocs anteriors. Concretament, presentem un sistema per a la detecci o extracci o i classi caci o autom atica d'anomalies amb una alta precisi o i molt pocs falsos positius. Adicionalment, despleguem el sistema en un entorn operatiu i demostrem la seva utilitat pr actica. El quart i ultim bloc d'aquesta tesi presenta una generalitzaci o del nostre sistema que se centra en l'an alisi de tot el tr ansit, no nom es en les anomalies. Aquest nou sistema pret en ajudar m es als operadors ja que resumeix els patrons de tr ansit m es importants de la seva xarxa. En particular, es generalitza el sistema per fer front al "big data" (una gran quantitat de dades). En particular, el sistema tracta IPs origen i dest i, ports origen i destí , protocol, Sistemes Aut onoms origen i dest , aplicaci o que ha generat el tr ansit i fi nalment, dades de geolocalitzaci o (tamb e per origen i dest ). Primer, despleguem un prototip a la xarxa europea per a la recerca i la investigaci o (G EANT) i demostrem que el sistema pot processar grans quantitats de dades r apidament aix com crear informes altament informatius i compactes que s on de gran utilitat per ajudar a comprendre el que est a succeint a la xarxa. En segon lloc, despleguem la nostra eina en un escenari completament diferent i mostrem com tamb e pot ser utilitzat amb exit en un cas d' us en el m on real en el qual s'analitza el comportament de dispositius altament distribuïts
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