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Mitigating Network-Based Denial-of-Service Attacks with Client PuzzlesMcNevin, Timothy John 04 May 2005 (has links)
Over the past few years, denial-of-service (DoS) and distributed denial-of-service (DDoS) attacks have become more of a threat than ever. These attacks are aimed at denying or degrading service for a legitimate user by any means necessary. The need to propose and research novel methods to mitigate them has become a critical research issue in network security. Recently, client puzzle protocols have received attention as a method for combating DoS and DDoS attacks. In a client puzzle protocol, the client is forced to solve a cryptographic puzzle before it can request any operation from a remote server or host. This thesis presents the framework and design of two different client puzzle protocols: Puzzle TCP and Chained Puzzles.
Puzzle TCP, or pTCP, is a modification to the Transmission Control Protocol (TCP) that supports the use of client puzzles at the transport layer and is designed to help combat various DoS attacks that target TCP. In this protocol, when a server is under attack, each client is required to solve a cryptographic puzzle before the connection can be established. This thesis presents the design and implementation of pTCP, which was embedded into the Linux kernel, and demonstrates how effective it can be at defending against specific attacks on the transport layer.
Chained Puzzles is an extension to the Internet Protocol (IP) that utilizes client puzzles to mitigate the crippling effects of a large-scale DDoS flooding attack by forcing each client to solve a cryptographic problem before allowing them to send packets into the network. This thesis also presents the design of Chained Puzzles and verifies its effectiveness with simulation results during large-scale DDoS flooding attacks. / Master of Science
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Anomaly detection via high-dimensional data analysis on web access data.January 2009 (has links)
Suen, Ho Yan. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2009. / Includes bibliographical references (leaves 99-104). / Abstract also in Chinese. / Abstract --- p.i / Acknowledgement --- p.iv / Chapter 1 --- Introduction --- p.1 / Chapter 1.1 --- Motivation --- p.1 / Chapter 1.2 --- Organization --- p.4 / Chapter 2 --- Literature Review --- p.6 / Chapter 2.1 --- Related Works --- p.6 / Chapter 2.2 --- Background Study --- p.7 / Chapter 2.2.1 --- World Wide Web --- p.7 / Chapter 2.2.2 --- Distributed Denial of Service Attack --- p.11 / Chapter 2.2.3 --- Tools for Dimension Reduction --- p.13 / Chapter 2.2.4 --- Tools for Anomaly Detection --- p.20 / Chapter 2.2.5 --- Receiver operating characteristics (ROC) Analysis --- p.22 / Chapter 3 --- System Design --- p.25 / Chapter 3.1 --- Methodology --- p.25 / Chapter 3.2 --- System Overview --- p.27 / Chapter 3.3 --- Reference Profile Construction --- p.31 / Chapter 3.4 --- Real-time Anomaly Detection and Response --- p.32 / Chapter 3.5 --- Chapter Summary --- p.34 / Chapter 4 --- Reference Profile Construction --- p.35 / Chapter 4.1 --- Web Access Logs Collection --- p.35 / Chapter 4.2 --- Data Preparation --- p.37 / Chapter 4.3 --- Feature Extraction and Embedding Engine (FEE Engine) --- p.40 / Chapter 4.3.1 --- Sub-Sequence Extraction --- p.42 / Chapter 4.3.2 --- Hash Function on Sub-sequences (optional) --- p.45 / Chapter 4.3.3 --- Feature Vector Construction --- p.46 / Chapter 4.3.4 --- Diffusion Wavelets Embedding --- p.47 / Chapter 4.3.5 --- Numerical Example of Feature Set Reduction --- p.49 / Chapter 4.3.6 --- Reference Profile and Further Use of FEE Engine --- p.50 / Chapter 4.4 --- Chapter Summary --- p.50 / Chapter 5 --- Real-time Anomaly Detection and Response --- p.52 / Chapter 5.1 --- Session Filtering and Data Preparation --- p.54 / Chapter 5.2 --- Feature Extraction and Embedding --- p.54 / Chapter 5.3 --- Distance-based Outlier Scores Calculation --- p.55 / Chapter 5.4 --- Anomaly Detection and Response --- p.56 / Chapter 5.4.1 --- Length-Based Anomaly Detection Modules --- p.56 / Chapter 5.4.2 --- Characteristics of Anomaly Detection Modules --- p.59 / Chapter 5.4.3 --- Dynamic Threshold Adaptation --- p.60 / Chapter 5.5 --- Chapter Summary --- p.63 / Chapter 6 --- Experimental Results --- p.65 / Chapter 6.1 --- Experiment Datasets --- p.65 / Chapter 6.1.1 --- Normal Web Access Logs --- p.66 / Chapter 6.1.2 --- Attack Data Generation --- p.68 / Chapter 6.2 --- ROC Curve Construction --- p.70 / Chapter 6.3 --- System Parameters Selection --- p.71 / Chapter 6.4 --- Performance of Anomaly Detection --- p.82 / Chapter 6.4.1 --- Performance Analysis --- p.85 / Chapter 6.4.2 --- Performance in defending DDoS attacks --- p.87 / Chapter 6.5 --- Computation Requirement --- p.91 / Chapter 6.6 --- Chapter Summary --- p.95 / Chapter 7 --- Conclusion and Future Work --- p.96 / Bibliography --- p.99
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Denial of Service attacks: path reconstruction for IP traceback using Adjusted Probabilistic Packet MarkingDube, Raghav 17 February 2005 (has links)
The use of Internet has revolutionized the way information is exchanged, changed
business paradigms and put mission critical and sensitive systems online. Any dis-
ruption of this connectivity and the plethora of services provided results in significant
damages to everyone involved. Denial of Service (DoS) attacks are becoming increas-
ingly common and are the cause of lost time and revenue.
Flooding type DoS attacks use spoofed IP addresses to disguise the attackers.
This makes identification of the attackers extremely difficult. This work proposes a
new scheme that allows the victim of a DoS attack to identify the correct origin of the
malicious traffic. The suggested mechanism requires routers to mark packets using
adjusted probabilistic marking. This results in a lower number of packet-markings
required to identify the traffic source. Unlike many related works, we use the existing
IPv4 header structure to incorporate these markings. We simulate and test our
algorithms using real Internet trace data to show that our technique is fast, and
works successfully for a large number of distributed attackers.
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A simulation study of an application layer DDoS detection mechanismMekhitarian, Araxi, Rabiee, Amir January 2016 (has links)
Over the last couple of years the rise of application layer Distributed Denial of Service (DDoS) attacks has significantly increased. Because of this, many issues have been raised on how organizations and companies can protect themselves from intrusions and damages against their systems and services. The consequences from these attacks are many, ranging from revenue losses for companies to stolen personal data. As the technologies are evolving, application layer DDoS attacks are becoming more effective and there is not a concrete solution that entirely protects against them. This thesis focuses on the available defense mechanisms and presents a general overview of different types of application layer DDoS attacks and how they are constructed. Moreover this report provides a simulation based on one of the defense mechanisms mentioned, named CALD. The simulation tested two different application layer DDoS attacks and showed that CALD can detect and differentiate between the two attacks. This report can be used as a general information source for application layer DDoS attacks, how to detect them and how to defend against them. Furthermore the simulation can be used as a basis on how well a relatively small-scaled implementation of CALD can detect DDoS attacks on the application layer. / Under de senaste åren har ökningen av Distributed Denial of Service (DDoS) attacker på applikationslagret ökat markant. På grund av detta har många frågor uppkommit om hur organisationer och företag kan skydda sig mot intrång och skador mot sina system och tjänster. Konsekvenserna av dessa attacker är många, allt från intäktsförluster för företag till stulen personlig data. Eftersom tekniken utvecklas, har DDoS attacker på applikationslagret blivit mer effektiva och det finns inte en konkret lösning för att hindra dem. Denna rapport fokuserar på de tillgängliga försvarsmekanismer och presenterar en allmän översikt över olika typer av DDoS-attacker på applikationslagret och hur de är uppbyggda. Dessutom bidrar den här rapporten med en redovisning av en simulering baserad på en av de försvarsmekanismer som nämns i rapporten, CALD. Simuleringen testade två olika attacker på applikationslagret och visar att CALD kan upptäcka och skilja mellan de två attackerna. Denna rapport kan användas som en allmän informationskälla för DDoSattacker på applikationslagret och hur man försvarar sig mot och upptäcker dessa. Vidare kan simuleringen användas som utgångspunkt på hur väl en relativt småskalig implementering av CALD kan upptäcka DDoS-attacker på applikationslagret.
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Transparently Improving Quality of Service of Modern ApplicationsYang, Yudong January 2019 (has links)
Improving end-to-end Quality of Service (QoS) in existing network systems is a fundamental problem, as it can be affected by many factors, including congestion, packet scheduling, attacks, and air-time allocation. This dissertation addresses QoS in two critical environments: home WiFi and cloud networks.
In home networks, we focus on improving QoS over WiFi networks, the dominant means for home Internet access. Three major reasons for end-to-end QoS efforts fail in WiFi networks are its: 1) inherent wireless channel characteristics, 2) approach to access control of the shared broadcast channel, and 3) impact on transport layer protocols, such as TCP, that operate end-to-end, and over-react to the loss or delay caused by the single WiFi link. We present our cross-layer design, Virtual Wire, leveraging the philosophy of centralization in modern networking to address the problem at the point of entry/egress into the WiFi network. Based on network conditions measured from buffer sizes, airtime, and throughput, flows are scheduled to the optimal utility. Unlike most existing WiFi QoS approaches, our design only relies on transparent modifications, requiring no changes to the network (including link layer) protocols, applications, or user intervention. Through extensive experimental investigation, we show that our design significantly enhances the reliability and predictability of WiFi performance, providing a ``virtual wire''-like link to the targeted application.
In cloud networks, we explore mechanisms to improve availability during DDoS attacks. The availability of cloud servers is impacted when excessive loads induced by DDoS attacks cause the servers to crash or respond too slowly to legitimate session requests. We model and analyze the effectiveness of a shuffling mechanism: the periodic, randomized re-assignment of users to servers. This shuffling mechanism not only complicates malicious users’ abilities to target specific servers but also, over time, allows a system to identify who the malicious users are. We design and evaluate improved classifiers which can, with statistical accuracy and well-defined levels of confidence, identify malicious users. We also propose and explore the effectiveness of a two-tiered system in which servers are partitioned in two, where one partition serves only ”filtered” users who have demonstrated non-malicious behavior. Our results show how shuffling with these novel classifiers can improve the QoS of the system, which is evaluated by the survival probability, the probability of a legitimate session not being affected by attacks.
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Προστασία συστημάτων από κατανεμημένες επιθέσεις στο Διαδίκτυο / Protecting systems from distributed attacks on the InternetΣτεφανίδης, Κυριάκος 17 March 2014 (has links)
Η παρούσα διατριβή πραγματεύεται το θέμα των κατανεμημένων επιθέσεων άρνησης υπηρεσιών στο Διαδίκτυο. Αναλύει τα υπάρχοντα συστήματα αντιμετώπισης και τα εργαλεία που χρησιμοποιούνται για την εξαπόλυση τέτοιου είδους επιθέσεων. Μελετά τον τρόπο που οργανώνονται οι επιθέσεις και παρουσιάζει την αρχιτεκτονική και την υλοποίηση ενός πρωτότυπου συστήματος ανίχνευσης των πηγών μιας κατανεμημένης επίθεσης άρνησης υπηρεσιών, καθώς και αντιμετώπισης των επιθέσεων αυτών. Τέλος, ασχολείται με το θέμα της ανεπιθύμητης αλληλογραφίας ως μιας διαφορετικού είδους επίθεση άρνησης υπηρεσιών και προτείνει ένα πρωτότυπο τρόπο αντιμετώπισής της. / In our thesis we deal with the issue of Distributed Denial of Service attacks on the Internet. We analyze the current defense methodologies and the tools that are used to unleash this type of attacks. We study the way that those attacks are constructed and organized and present a novel architecture, and its implementation details, of a system that is able to trace back to the true sources of such an attack as well as effectively filter such attacks in real time. Lastly we deal with the issue of spam e-mail as a different form of a distributed denial of service attack and propose a novel methodology that deals with the problem.
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The Current State of DDoS DefenseNilsson, Sebastian January 2014 (has links)
A DDoS attack is an attempt to bring down a machine connected to the Internet. This is done by having multiple computers repeatedly sending requests to tie up a server making it unable to answer legitimate requests. DDoS attacks are currently one of the biggest security threats on the internet according to security experts. We used a qualitative interview with experts in IT security to gather data to our research. We found that most companies are lacking both in knowledge and in their protection against DDoS attacks. The best way to minimize this threat would be to build a system with redundancy, do a risk analysis and revise security policies. Most of the technologies reviewed were found ineffective because of the massive amount of data amplification attacks can generate. Ingress filtering showed promising results in preventing DDoS attacks by blocking packages with spoofed IP addresses thus preventing amplification attacks.
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Security related self-protected networks: Autonomous threat detection and response (ATDR)Havenga, Wessel Johannes Jacobus January 2021 (has links)
>Magister Scientiae - MSc / Cybersecurity defense tools, techniques and methodologies are constantly faced with increasing
challenges including the evolution of highly intelligent and powerful new-generation threats. The
main challenges posed by these modern digital multi-vector attacks is their ability to adapt with
machine learning. Research shows that many existing defense systems fail to provide adequate
protection against these latest threats. Hence, there is an ever-growing need for self-learning technologies
that can autonomously adjust according to the behaviour and patterns of the offensive
actors and systems. The accuracy and effectiveness of existing methods are dependent on decision
making and manual input by human experts. This dependence causes 1) administration
overhead, 2) variable and potentially limited accuracy and 3) delayed response time.
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PERFORMANCE EVALUATION OF A TTL-BASED DYNAMIC MARKING SCHEME IN IP TRACEBACKDevasundaram, Shanmuga Sundaram January 2006 (has links)
No description available.
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Anomaly diagnosis based on regression and classification analysis of statistical traffic featuresLiu, Lei, Jin, X.L., Min, Geyong, Xu, L. 30 September 2013 (has links)
No / Traffic anomalies caused by Distributed Denial-of-Service (DDoS) attacks are major threats to both network service providers and legitimate customers. The DDoS attacks regularly consume and exhaust the resources of victims and hence result in abnormal bursty traffic through end-user systems. Additionally, malicious traffic aggregated into normal traffic often show dramatic changes in the traffic nature and statistical features. This study focuses on early detection of traffic anomalies caused by DDoS attacks in light of analyzing the network traffic behavior. Key statistical features including variance, autocorrelation, and self-similarity are employed to characterize the network traffic. Further, artificial neural network and support vector machine subject to the performance metrics are employed to predict and classify the abnormal traffic. The proposed diagnosis mechanism is validated through experiments where the datasets consist of two groups. The first group is the Massachusetts Institute of Technology Lincoln Laboratory dataset containing labeled DoS attack. The second group collected from DDoS attack simulation experiments covers three representative traffic shapes resulting from the dynamic attack rate configuration, namely, constant intensity, ramp-up behavior, and pulsing behavior. The experimental results demonstrate that the developed mechanism can effectively and precisely alert the abnormal traffic within short response period.
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