114 research outputs found
Agent-Based Modeling and Simulation of Network Infrastructure Cyber-Attacks and Cooperative Defense Mechanisms
Graphical & digital media application
The Framework for Simulation of Bioinspired Security Mechanisms against Network Infrastructure Attacks
The paper outlines a bioinspired approach named βnetwork nervous system" and methods of simulation of infrastructure attacks and protection mechanisms based on this approach. The protection mechanisms based on this approach consist of distributed prosedures of information collection and processing, which coordinate the activities of the main devices of a computer network, identify attacks, and determine nessesary countermeasures. Attacks and protection mechanisms are specified as structural models using a set-theoretic approach. An environment for simulation of protection mechanisms based on the biological metaphor is considered; the experiments demonstrating the effectiveness of the protection mechanisms are described
DoS and DDoS Attacks: Defense, Detection and Traceback Mechanisms - A Survey
Denial of Service (DoS) or Distributed Denial of Service (DDoS) attacks are typically explicit attempts to exhaust victim2019;s bandwidth or disrupt legitimate users2019; access to services. Traditional architecture of internet is vulnerable to DDoS attacks and it provides an opportunity to an attacker to gain access to a large number of compromised computers by exploiting their vulnerabilities to set up attack networks or Botnets. Once attack network or Botnet has been set up, an attacker invokes a large-scale, coordinated attack against one or more targets. Asa result of the continuous evolution of new attacks and ever-increasing range of vulnerable hosts on the internet, many DDoS attack Detection, Prevention and Traceback mechanisms have been proposed, In this paper, we tend to surveyed different types of attacks and techniques of DDoS attacks and their countermeasures. The significance of this paper is that the coverage of many aspects of countering DDoS attacks including detection, defence and mitigation, traceback approaches, open issues and research challenges
ΠΠΌΠΈΡΠ°ΡΠΈΠΎΠ½Π½ΠΎΠ΅ ΠΌΠΎΠ΄Π΅Π»ΠΈΡΠΎΠ²Π°Π½ΠΈΠ΅ ΠΌΠ΅Ρ Π°Π½ΠΈΠ·ΠΌΠΎΠ² Π·Π°ΡΠΈΡΡ ΠΎΡ Π±ΠΎΡ-ΡΠ΅ΡΠ΅ΠΉ.
To create effective mechanisms of protection against botnets, it is necessary to investigate the behavior of botnets and their impact on the operation of computer networks, as well as methods for botnet detection and counteraction to them. The paper investigates protection mechanisms against botnets, which are proliferated by worm propagation techniques and carry out DDoS attacks. As a toolkit to study of botnets and protect mechanisms we developed the simulation environment. The paper considers the architecture of the simulation environment implemented and a multitude of experiments which show ample opportunities of the developed simulation environment for research of botnets and protection mechanisms.ΠΠ»Ρ ΡΠΎΠ·Π΄Π°Π½ΠΈΡ ΡΡΡΠ΅ΠΊΡΠΈΠ²Π½ΡΡ
ΠΌΠ΅Ρ
Π°Π½ΠΈΠ·ΠΌΠΎΠ² Π·Π°ΡΠΈΡΡ ΠΎΡ Π±ΠΎΡ-ΡΠ΅ΡΠ΅ΠΉ Π½Π΅ΠΎΠ±Ρ
ΠΎΠ΄ΠΈΠΌΠΎ ΠΈΡΡΠ»Π΅Π΄ΠΎΠ²Π°ΡΡ ΠΏΠΎΠ²Π΅Π΄Π΅Π½ΠΈΠ΅ Π±ΠΎΡ-ΡΠ΅ΡΠ΅ΠΉ, ΠΈΡ
Π²Π»ΠΈΡΠ½ΠΈΠ΅ Π½Π° ΡΠ°Π±ΠΎΡΡ ΠΊΠΎΠΌΠΏΡΡΡΠ΅ΡΠ½ΡΡ
ΡΠ΅ΡΠ΅ΠΉ, Π° ΡΠ°ΠΊΠΆΠ΅ ΠΌΠ΅ΡΠΎΠ΄Ρ Π΄Π΅ΡΠ΅ΠΊΡΠΈΡΠΎΠ²Π°Π½ΠΈΡ Π±ΠΎΡ-ΡΠ΅ΡΠ΅ΠΉ ΠΈ ΠΏΡΠΎΡΠΈΠ²ΠΎΠ΄Π΅ΠΉΡΡΠ²ΠΈΡ ΠΈΠΌ. Π Π΄Π°Π½Π½ΠΎΠΉ ΡΡΠ°ΡΡΠ΅ ΠΈΡΡΠ»Π΅Π΄ΡΡΡΡΡ ΠΌΠ΅Ρ
Π°Π½ΠΈΠ·ΠΌΡ Π·Π°ΡΠΈΡΡ ΠΎΡ Π±ΠΎΡ-ΡΠ΅ΡΠ΅ΠΉ, ΡΠ°ΡΠΏΡΠΎΡΡΡΠ°Π½ΡΡΡΠΈΡ
ΡΡ Ρ ΠΏΠΎΠΌΠΎΡΡΡ ΡΠ΅Ρ
Π½ΠΎΠ»ΠΎΠ³ΠΈΠΈ ΠΊΠΎΠΌΠΏΡΡΡΠ΅ΡΠ½ΡΡ
ΡΠ΅ΡΠ²Π΅ΠΉ ΠΈ Π²ΡΠΏΠΎΠ»Π½ΡΡΡΠΈΡ
ΡΠ°ΡΠΏΡΠ΅Π΄Π΅Π»Π΅Π½Π½ΡΠ΅ Π°ΡΠ°ΠΊΠΈ ΡΠΈΠΏΠ° Β«ΠΎΡΠΊΠ°Π· Π² ΠΎΠ±ΡΠ»ΡΠΆΠΈΠ²Π°Π½ΠΈΠΈΒ». Π ΠΊΠ°ΡΠ΅ΡΡΠ²Π΅ ΠΈΠ½ΡΡΡΡΠΌΠ΅Π½ΡΠ° Π΄Π»Ρ ΠΈΡΡΠ»Π΅Π΄ΠΎΠ²Π°Π½ΠΈΡ Π±ΠΎΡ-ΡΠ΅ΡΠ΅ΠΉ ΠΈ ΠΌΠ΅Ρ
Π°Π½ΠΈΠ·ΠΌΠΎΠ² Π·Π°ΡΠΈΡΡ ΠΏΡΠ΅Π΄Π»Π°Π³Π°Π΅ΡΡΡ ΠΏΡΠΎΠ³ΡΠ°ΠΌΠΌΠ½ΠΎ-ΠΈΠ½ΡΡΡΡΠΌΠ΅Π½ΡΠ°Π»ΡΠ½Π°Ρ ΡΡΠ΅Π΄Π° ΠΈΠΌΠΈΡΠ°ΡΠΈΠΎΠ½Π½ΠΎΠ³ΠΎ ΠΌΠΎΠ΄Π΅Π»ΠΈΡΠΎΠ²Π°Π½ΠΈΡ, ΡΠ°Π·ΡΠ°Π±ΠΎΡΠ°Π½Π½Π°Ρ Π°Π²ΡΠΎΡΠ°ΠΌΠΈ ΡΡΠ°ΡΡΠΈ. ΠΠΏΠΈΡΡΠ²Π°Π΅ΡΡΡ ΠΎΠ±ΡΠ°Ρ Π°ΡΡ
ΠΈΡΠ΅ΠΊΡΡΡΠ° ΡΡΠ΅Π΄Ρ ΠΌΠΎΠ΄Π΅Π»ΠΈΡΠΎΠ²Π°Π½ΠΈΡ ΠΈ ΠΏΡΠ΅Π΄ΡΡΠ°Π²Π»Π΅Π½Ρ ΡΠΊΡΠΏΠ΅ΡΠΈΠΌΠ΅Π½ΡΡ, ΠΊΠΎΡΠΎΡΡΠ΅ ΠΏΠΎΠΊΠ°Π·ΡΠ²Π°ΡΡ Π²ΠΎΠ·ΠΌΠΎΠΆΠ½ΠΎΡΡΠΈ ΡΠ°Π·ΡΠ°Π±ΠΎΡΠ°Π½Π½ΠΎΠΉ ΡΡΠ΅Π΄Ρ ΠΈΠΌΠΈΡΠ°ΡΠΈΠΎΠ½Π½ΠΎΠ³ΠΎ ΠΌΠΎΠ΄Π΅Π»ΠΈΡΠΎΠ²Π°Π½ΠΈΡ Π΄Π»Ρ ΠΈΡΡΠ»Π΅Π΄ΠΎΠ²Π°Π½ΠΈΡ Π±ΠΎΡ-ΡΠ΅ΡΠ΅ΠΉ ΠΈ ΠΌΠ΅Ρ
Π°Π½ΠΈΠ·ΠΌΠΎΠ² Π·Π°ΡΠΈΡΡ ΠΎΡ Π½ΠΈΡ
Adaptive Response System for Distributed Denial-of-Service Attacks
The continued prevalence and severe damaging effects of the Distributed Denial of Service (DDoS)
attacks in todayβs Internet raise growing security concerns and call for an immediate response to come
up with better solutions to tackle DDoS attacks. The current DDoS prevention mechanisms are usually
inflexible and determined attackers with knowledge of these mechanisms, could work around them.
Most existing detection and response mechanisms are standalone systems which do not rely on
adaptive updates to mitigate attacks. As different responses vary in their βleniencyβ in treating
detected attack traffic, there is a need for an Adaptive Response System.
We designed and implemented our DDoS Adaptive ResponsE (DARE) System, which is a
distributed DDoS mitigation system capable of executing appropriate detection and mitigation
responses automatically and adaptively according to the attacks. It supports easy integrations for both
signature-based and anomaly-based detection modules. Additionally, the design of DAREβs individual
components takes into consideration the strengths and weaknesses of existing defence mechanisms,
and the characteristics and possible future mutations of DDoS attacks. These components consist of an
Enhanced TCP SYN Attack Detector and Bloom-based Filter, a DDoS Flooding Attack Detector and
Flow Identifier, and a Non Intrusive IP Traceback mechanism. The components work together
interactively to adapt the detections and responses in accordance to the attack types. Experiments
conducted on DARE show that the attack detection and mitigation are successfully completed within
seconds, with about 60% to 86% of the attack traffic being dropped, while availability for legitimate
and new legitimate requests is maintained. DARE is able to detect and trigger appropriate responses in
accordance to the attacks being launched with high accuracy, effectiveness and efficiency.
We also designed and implemented a Traffic Redirection Attack Protection System (TRAPS), a
stand-alone DDoS attack detection and mitigation system for IPv6 networks. In TRAPS, the victim
under attack verifies the authenticity of the source by performing virtual relocations to differentiate the
legitimate traffic from the attack traffic. TRAPS requires minimal deployment effort and does not
require modifications to the Internet infrastructure due to its incorporation of the Mobile IPv6
protocol. Experiments to test the feasibility of TRAPS were carried out in a testbed environment to
verify that it would work with the existing Mobile IPv6 implementation. It was observed that the
operations of each module were functioning correctly and TRAPS was able to successfully mitigate an
attack launched with spoofed source IP addresses
Denial of Service in Web-Domains: Building Defenses Against Next-Generation Attack Behavior
The existing state-of-the-art in the field of application layer Distributed Denial of Service (DDoS) protection is generally designed, and thus effective, only for static web domains. To the best of our knowledge, our work is the first that studies the problem of application layer DDoS defense in web domains of dynamic content and organization, and for next-generation bot behaviour. In the first part of this thesis, we focus on the following research tasks: 1) we identify the main weaknesses of the existing application-layer anti-DDoS solutions as proposed in research literature and in the industry, 2) we obtain a comprehensive picture of the current-day as well as the next-generation application-layer attack behaviour and 3) we propose novel techniques, based on a multidisciplinary approach that combines offline machine learning algorithms and statistical analysis, for detection of suspicious web visitors in static web domains. Then, in the second part of the thesis, we propose and evaluate a novel anti-DDoS system that detects a broad range of application-layer DDoS attacks, both in static and dynamic web domains, through the use of advanced techniques of data mining. The key advantage of our system relative to other systems that resort to the use of challenge-response tests (such as CAPTCHAs) in combating malicious bots is that our system minimizes the number of these tests that are presented to valid human visitors while succeeding in preventing most malicious attackers from accessing the web site. The results of the experimental evaluation of the proposed system demonstrate effective detection of current and future variants of application layer DDoS attacks
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