The document discusses denial of service (DoS) attacks, specifically SYN flooding and DNS amplification attacks. It describes how these attacks work by exploiting vulnerabilities in TCP connections and DNS servers. The document outlines the development of attack clients to launch SYN flooding and DNS amplification attacks against a server. It also details how the server was designed to detect and mitigate SYN flooding attacks by tracking and flushing incomplete connection requests. The server was able to withstand SYN flooding attacks but could not defend against DNS amplification. Evaluation results show the server had near perfect detection rates for SYN flooding but occasional dropped legitimate connections under high volume attacks.
DDoS attacks target companies and institutions that provide online services. They work by overloading servers with traffic from multiple compromised systems known as "bots" or "zombies". Common DDoS attack types include SMURF, TCP SYN/ACK, UDP flood, DNS amplification, and attacks using peer-to-peer networks. Defenses include configuring routers and firewalls to filter unauthorized traffic, limiting response messages, and tracking malicious activity on peer-to-peer networks. As attack methods evolve, continued development of detection and mitigation techniques is needed.
Efficient ddos attacks security scheme using asvseSAT Journals
Abstract A distributed Denial of Service (DDoS) attack enables higher threats to the internet. There are so many scheme designed to identify the node which is to be attacker node. The real process is such as we want to trace the source of the attacker and enable security to our network. The protocol introduced here, called Adaptive Selective Verification with Stub (ASVS) is shown to use bandwidth efficiently and uses stub creation. The Stub procedure to reduce the server load at the time of emergency and congestion. Using this stub idea we can store the ASVS protocol procedure in the server and we can have the stub in the every client so that we can detect the hacker system by the client itself. We use omniscient protocol which enables to send information about the attacker to all the clients. Keywordss: Adaptive Selective Verification With Stub (ASVS), Distributive Denial Of Service Attacks (DDoS) Flooding, Performance Analysis.
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology.
Monitoring of traffic over the victim under tcp syn flood in a laneSAT Publishing House
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology
The document provides information about different types of DDoS attacks including DoS, DDoS, DNS reflection, SYN reflection, SMURF, UDP flood, SNMP, NTP, HTTP GET, and HTTP POST attacks. It describes how each attack works and overloads the target system with traffic. Mitigation techniques are also outlined, such as firewalls, rate limiting, authentication, and modifying server configurations.
The document discusses denial of service (DoS) attacks and methods of mitigation. It describes various types of DoS attacks including flooding attacks like TCP SYN floods and UDP floods that exhaust server bandwidth or resources. Other attacks discussed include HTTP floods, SSL handshake floods, and attacks that exploit vulnerabilities or misuse features like HTTP POST floods and SSL renegotiation attacks. State-of-the-art mitigation techniques mentioned include DoS mitigation software developed by the Society for Electronic Transactions & Security that use techniques like client puzzles to protect against various application layer attacks.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
The document discusses denial of service (DoS) and distributed denial of service (DDoS) attacks. It describes different types of DoS attacks such as sending malformed packets to exploit protocol or application flaws. It notes that DDoS attacks involve aggregating malicious traffic from many zombie machines to flood the victim with packets. Most defense methods focus on mitigating bandwidth consumption from packet flooding. However, attackers may also directly target applications to exhaust computational resources. The document proposes an acknowledgment-based port hopping protocol for secure communication between a sender and receiver that is resistant to such attacks.
DDoS attacks target companies and institutions that provide online services. They work by overloading servers with traffic from multiple compromised systems known as "bots" or "zombies". Common DDoS attack types include SMURF, TCP SYN/ACK, UDP flood, DNS amplification, and attacks using peer-to-peer networks. Defenses include configuring routers and firewalls to filter unauthorized traffic, limiting response messages, and tracking malicious activity on peer-to-peer networks. As attack methods evolve, continued development of detection and mitigation techniques is needed.
Efficient ddos attacks security scheme using asvseSAT Journals
Abstract A distributed Denial of Service (DDoS) attack enables higher threats to the internet. There are so many scheme designed to identify the node which is to be attacker node. The real process is such as we want to trace the source of the attacker and enable security to our network. The protocol introduced here, called Adaptive Selective Verification with Stub (ASVS) is shown to use bandwidth efficiently and uses stub creation. The Stub procedure to reduce the server load at the time of emergency and congestion. Using this stub idea we can store the ASVS protocol procedure in the server and we can have the stub in the every client so that we can detect the hacker system by the client itself. We use omniscient protocol which enables to send information about the attacker to all the clients. Keywordss: Adaptive Selective Verification With Stub (ASVS), Distributive Denial Of Service Attacks (DDoS) Flooding, Performance Analysis.
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology.
Monitoring of traffic over the victim under tcp syn flood in a laneSAT Publishing House
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology
The document provides information about different types of DDoS attacks including DoS, DDoS, DNS reflection, SYN reflection, SMURF, UDP flood, SNMP, NTP, HTTP GET, and HTTP POST attacks. It describes how each attack works and overloads the target system with traffic. Mitigation techniques are also outlined, such as firewalls, rate limiting, authentication, and modifying server configurations.
The document discusses denial of service (DoS) attacks and methods of mitigation. It describes various types of DoS attacks including flooding attacks like TCP SYN floods and UDP floods that exhaust server bandwidth or resources. Other attacks discussed include HTTP floods, SSL handshake floods, and attacks that exploit vulnerabilities or misuse features like HTTP POST floods and SSL renegotiation attacks. State-of-the-art mitigation techniques mentioned include DoS mitigation software developed by the Society for Electronic Transactions & Security that use techniques like client puzzles to protect against various application layer attacks.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
The document discusses denial of service (DoS) and distributed denial of service (DDoS) attacks. It describes different types of DoS attacks such as sending malformed packets to exploit protocol or application flaws. It notes that DDoS attacks involve aggregating malicious traffic from many zombie machines to flood the victim with packets. Most defense methods focus on mitigating bandwidth consumption from packet flooding. However, attackers may also directly target applications to exhaust computational resources. The document proposes an acknowledgment-based port hopping protocol for secure communication between a sender and receiver that is resistant to such attacks.
Internets Manage Communication Procedure and Protection that Crash on ServersIRJET Journal
This document summarizes a research study that evaluated the performance of the Windows Server and Mac OS X Server operating systems when subjected to different types of ICMP-based denial-of-service attacks. The attacks tested were ping floods and Smurf attacks. Both servers were installed on the same Apple Mac Pro hardware platform to isolate the effects of the operating systems. The Windows Server was able to handle more legitimate HTTP connections than the Mac OS X Server under ping flood attacks, but crashed at a much lower bandwidth of Smurf attack traffic (150 Mbps) compared to the Mac OS X Server (500 Mbps). The study concludes that while Windows Server performed better against ping floods, its built-in protections were less effective against Smurf attacks
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
DRDoS is the latest in the series of Denial of Service attacks. An explanation of the history of this type of attack is in order to fully understand the ramifications of this new threat.http://servv89pn0aj.sn.sourcedns.com/~gbpprorg/2600/DRDoS-Spyrochaete.html
A Comparative Approach to Handle Ddos AttacksIOSR Journals
This document summarizes various types of distributed denial of service (DDoS) attacks and potential countermeasures. It discusses common DDoS attacks like SYN flooding, UDP flooding, ICMP flooding, teardrop attacks, land attacks, and Win Nuke attacks. For each type of attack, it provides details on how the attack works and recommends precautions like firewalls, ingress/egress filtering, updating systems, and monitoring logs files and network traffic. Recent DDoS attack statistics indicate attacks are growing in frequency and bandwidth. Experimental testbed platforms are also discussed that can be used to study DDoS attacks and countermeasures.
This document discusses a novel method called Early Detection of SYN Flooding Attack by Adaptive Thresholding (EDSAT) to detect SYN flooding attacks in mobile ad hoc networks. SYN flooding is a denial of service attack that exploits weaknesses in TCP by flooding a target with spoofed SYN requests, overwhelming its resources. EDSAT uses an optimized adaptive threshold algorithm that monitors the SYN arrival rate and raises an alarm if it increases above an adaptive threshold based on a moving average of past rates. This helps detect attacks early by accounting for normal variations in traffic. The paper aims to optimize tuning parameters to improve detection performance compared to standard adaptive thresholding methods.
This document summarizes a survey of distributed denial-of-service (DDoS) attacks based on vulnerabilities in the TCP/IP protocol stack. It begins by introducing DDoS attacks and their architecture, then classifies DDoS attacks according to the TCP/IP layer they target - application layer, transport layer, or internet layer. Specific attack types are described for each layer, including HTTP flooding, SYN flooding, Smurf attacks, and more. The document aims to provide understanding of existing DDoS attack tools, methods, and defense mechanisms.
This document proposes an Expedite Message Authentication Protocol (EMAP) for vehicular ad hoc networks (VANETs) that aims to significantly decrease message loss ratio due to message verification delay compared to conventional authentication methods using certificate revocation lists (CRLs). EMAP replaces the time-consuming CRL checking process with a more efficient revocation checking process using hash-based message authentication codes (HMACs) shared only between valid vehicles. It also uses a novel probabilistic key distribution method to securely share and update keys.
The document discusses defending against distributed denial-of-service (DDoS) attacks and proposes solutions. It describes types of DDoS attacks like SYN flooding and reflector attacks. It then analyzes solutions like route-based packet filtering and a distributed attack detection system using detection systems to identify attacks and install filters. The document concludes current defenses are inadequate and more effective detection-and-filtering approaches need to be developed.
This document is a dissertation analyzing the GandCrab ransomware through network traffic using Wireshark. It outlines the objectives to assess and analyze a ransomware sample in network traffic and the network behavior once ransomware enters a system. The research methodology section describes setting up a test bed environment with 4 systems to execute and monitor the ransomware's network activities by capturing PCAP files with Wireshark for analysis. Key features of the GandCrab PCAP file are then extracted for further research on GandCrab, which spread widely in 2018 as a ransomware-as-a-service.
This document describes five types of SNMP applications: command generator applications, command responder applications, notification originator applications, notification receiver applications, and proxy forwarder applications. It defines procedures for each type of application for generating and processing SNMP messages. It also defines MIB modules for specifying management targets, notification filtering, and proxy forwarding.
This document discusses several types of denial of service (DoS) attacks, including distributed denial of service (DDoS) attacks. It describes how a DDoS attack uses multiple compromised systems or "zombies" to launch a large-scale attack. It also explains specific DoS attack methods like Smurf attacks, which flood a target with ping replies by spoofing the target's IP address, and SYN flood attacks, which exploit the TCP three-way handshake process to overwhelm a server with half-open connections. The document provides technical details on how various DoS attacks work to crash systems or make networks and services unavailable.
A ROBUST MECHANISM FOR DEFENDING DISTRIBUTED DENIAL OF SERVICE ATTACKS ON WEB...IJNSA Journal
Distributed Denial of Service (DDoS) attacks have emerged as a popular means of causing mass targeted service disruptions, often for extended periods of time. The relative ease and low costs of launching such attacks, supplemented by the current inadequate sate of any viable defense mechanism, have made them one of the top threats to the Internet community today. Since the increasing popularity of web-based applications has led to several critical services being provided over the Internet, it is imperative to monitor the network traffic so as to prevent malicious attackers from depleting the resources of the network and denying services to legitimate users. This paper first presents a brief discussion on some of the important types of DDoS attacks that currently exist and some existing mechanisms to combat these attacks. It then points out the major drawbacks of the currently existing defense mechanisms and proposes a new mechanism for protecting a web-server against a DDoS attack. In the proposed mechanism, incoming traffic to the server is continuously monitored and any abnormal rise in the inbound traffic is immediately detected. The detection algorithm is based on a statistical analysis of the inbound traffic on the server and a robust hypothesis testing framework. While the detection process is on, the sessions from the legitimate sources are not disrupted and the load on the server is restored to the normal level by blocking the traffic from the attacking sources. To cater to different scenarios, the detection algorithm has various modules with varying level of computational and memory overheads for
their execution. While the approximate modules are fast in detection and involve less overhead, they provide lower level of detection accuracy. The accurate modules employ complex detection logic and hence involve more overhead for their execution. However, they have very high detection accuracy. Simulations carried out on the proposed mechanism have produced results that demonstrate effectiveness of the proposed defense mechanism against DDoS attacks.
DISTRIBUTED DENIAL OF SERVICE (DDOS) ATTACKS DETECTION MECHANISMijcseit
Pushback is a mechanism for defending against Distributed Denial-of-Service (DDoS) attacks. DDoS attacks are treated as a congestion-control problem, but because most such congestion is caused by malicious hosts not obeying traditional end-to-end congestion control, the problem must be handled by the routers. Functionality is added to each router to detect and preferentially drop packets that probably belong to an attack. Upstream routers are also notified to drop such packets in order that the router’s resources be used to route legitimate traffic hence term pushback. Client puzzles have been advocated as a
promising countermeasure to DoS attacks in the recent years. In order to identify the attackers, the victim server issues a puzzle to the client that sent the traffic. When the client is able to solve the puzzle, it is assumed to be authentic and the traffic from it is allowed into the server. If the victim suspects that the
puzzles are solved by most of the clients, it increases the complexity of the puzzles. This puzzle solving technique allows the traversal of the attack traffic throughout the intermediate routers before reaching the destination. In order to attain the advantages of both pushback and puzzle solving techniques, a hybrid scheme called Router based Pushback technique, which involves both the techniques to solve the problem of DDoS attacks is proposed. In this proposal, the puzzle solving mechanism is pushed back to the core routers rather than having at the victim. The router based client puzzle mechanism checks the host system whether it is legitimate or not by providing a puzzle to be solved by the suspected host.
DISTRIBUTED DENIAL OF SERVICE (DDOS) ATTACKS DETECTION MECHANISM ijcseit
Pushback is a mechanism for defending against Distributed Denial-of-Service (DDoS) attacks. DDoS
attacks are treated as a congestion-control problem, but because most such congestion is caused by
malicious hosts not obeying traditional end-to-end congestion control, the problem must be handled by the
routers. Functionality is added to each router to detect and preferentially drop packets that probably
belong to an attack. Upstream routers are also notified to drop such packets in order that the router’s
resources be used to route legitimate traffic hence term pushback. Client puzzles have been advocated as a
promising countermeasure to DoS attacks in the recent years. In order to identify the attackers, the victim
server issues a puzzle to the client that sent the traffic. When the client is able to solve the puzzle, it is
assumed to be authentic and the traffic from it is allowed into the server. If the victim suspects that the
puzzles are solved by most of the clients, it increases the complexity of the puzzles. This puzzle solving
technique allows the traversal of the attack traffic throughout the intermediate routers before reaching the
destination. In order to attain the advantages of both pushback and puzzle solving techniques, a hybrid
scheme called Router based Pushback technique, which involves both the techniques to solve the problem
of DDoS attacks is proposed. In this proposal, the puzzle solving mechanism is pushed back to the core
routers rather than having at the victim. The router based client puzzle mechanism checks the host system
whether it is legitimate or not by providing a puzzle to be solved by the suspected host.
The document provides best practices for improving the resiliency of applications on AWS against DDoS attacks, including using AWS Shield Standard, AWS Shield Advanced, Amazon CloudFront, Amazon Route 53, Elastic Load Balancing, Amazon API Gateway, Amazon VPC, and Amazon EC2 with Auto Scaling. It describes infrastructure layer attacks like UDP reflection attacks and SYN floods, and application layer attacks. It outlines mitigation techniques and a reference architecture using various AWS services.
Know All About Mitigate TCP Syn Flood Attacks. It is a cyberattack so you need to fix this issue to secure your device & data security using this method. To know more visit https://bit.ly/3czNIcM
Enhancing the impregnability of linux serversIJNSA Journal
Worldwide IT industry is experiencing a rapid shift towards Service Oriented Architecture (SOA). As a
response to the current trend, all the IT firms are adopting business models such as cloud based services
which rely on reliable and highly available server platforms. Linux servers are known to be highly
secure. Network security thus becomes a major concern to all IT organizations offering cloud based
services. The fundamental form of attack on network security is Denial of Service. This paper focuses on
fortifying the Linux server defence mechanisms resulting in an increase in reliability and availability of
services offered by the Linux server platforms. To meet this emerging scenario, most of the organizations
are adopting business models such as cloud computing that are dependant on reliable server platforms.
Linux servers are well ahead of other server platforms in terms of security. This brings network security
to the forefront of major concerns to an organization. The most common form of attacks is a Denial of
Service attack. This paper focuses on mechanisms to detect and immunize Linux servers from DoS .
ENHANCING THE IMPREGNABILITY OF LINUX SERVERSIJNSA Journal
Worldwide IT industry is experiencing a rapid shift towards Service Oriented Architecture (SOA). As a response to the current trend, all the IT firms are adopting business models such as cloud based services which rely on reliable and highly available server platforms. Linux servers are known to be highly secure. Network security thus becomes a major concern to all IT organizations offering cloud based services. The fundamental form of attack on network security is Denial of Service. This paper focuses on fortifying the Linux server defence mechanisms resulting in an increase in reliability and availability of services offered by the Linux server platforms. To meet this emerging scenario, most of the organizations are adopting business models such as cloud computing that are dependant on reliable server platforms. Linux servers are well ahead of other server platforms in terms of security. This brings network security to the forefront of major concerns to an organization. The most common form of attacks is a Denial of Service attack. This paper focuses on mechanisms to detect and immunize Linux servers from DoS .
The document provides an overview of common DDoS attack types including SYN floods, UDP floods, ICMP floods, and HTTP floods. It describes how these attacks work to overwhelm servers and networks with traffic to cause denial of service. The document also covers reflection DDoS attacks using protocols like DNS, NTP, and Memcached to amplify the traffic and discusses recommendations for mitigating these attacks.
Security issues have become a major issue in recent years due to the advancement of technology in networking and its use in a destructive way. A number of Defence strategies have been devised to overcome the flooding attack which is prominent in the networking industry due to which depletion of resources Takes place. But these mechanism are not designed in an optimally and effectively and some of the issues have been unresolved. Hence in this paper we suggest a Game theory based strategy to create a series of Defence mechanisms using puzzles. Here the concept of Nash equilibrium is used to handle sophisticated flooding attack to defend distributed attacks from unknown number of sources
This document discusses distributed denial of service (DDoS) attacks. It begins by defining a DDoS attack as a malicious attempt to disrupt normal traffic by overwhelming a target with a flood of traffic utilizing multiple compromised systems. The document then discusses the evolution of DDoS attacks over time in terms of size and complexity. It provides examples of different types of DDoS attacks including application layer attacks like HTTP floods, protocol attacks like SYN floods, and volumetric attacks like DNS amplification attacks. Finally, it discusses common techniques for mitigating DDoS attacks such as black hole routing, rate limiting, web application firewalls, and anycast network diffusion.
Internets Manage Communication Procedure and Protection that Crash on ServersIRJET Journal
This document summarizes a research study that evaluated the performance of the Windows Server and Mac OS X Server operating systems when subjected to different types of ICMP-based denial-of-service attacks. The attacks tested were ping floods and Smurf attacks. Both servers were installed on the same Apple Mac Pro hardware platform to isolate the effects of the operating systems. The Windows Server was able to handle more legitimate HTTP connections than the Mac OS X Server under ping flood attacks, but crashed at a much lower bandwidth of Smurf attack traffic (150 Mbps) compared to the Mac OS X Server (500 Mbps). The study concludes that while Windows Server performed better against ping floods, its built-in protections were less effective against Smurf attacks
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
DRDoS is the latest in the series of Denial of Service attacks. An explanation of the history of this type of attack is in order to fully understand the ramifications of this new threat.http://servv89pn0aj.sn.sourcedns.com/~gbpprorg/2600/DRDoS-Spyrochaete.html
A Comparative Approach to Handle Ddos AttacksIOSR Journals
This document summarizes various types of distributed denial of service (DDoS) attacks and potential countermeasures. It discusses common DDoS attacks like SYN flooding, UDP flooding, ICMP flooding, teardrop attacks, land attacks, and Win Nuke attacks. For each type of attack, it provides details on how the attack works and recommends precautions like firewalls, ingress/egress filtering, updating systems, and monitoring logs files and network traffic. Recent DDoS attack statistics indicate attacks are growing in frequency and bandwidth. Experimental testbed platforms are also discussed that can be used to study DDoS attacks and countermeasures.
This document discusses a novel method called Early Detection of SYN Flooding Attack by Adaptive Thresholding (EDSAT) to detect SYN flooding attacks in mobile ad hoc networks. SYN flooding is a denial of service attack that exploits weaknesses in TCP by flooding a target with spoofed SYN requests, overwhelming its resources. EDSAT uses an optimized adaptive threshold algorithm that monitors the SYN arrival rate and raises an alarm if it increases above an adaptive threshold based on a moving average of past rates. This helps detect attacks early by accounting for normal variations in traffic. The paper aims to optimize tuning parameters to improve detection performance compared to standard adaptive thresholding methods.
This document summarizes a survey of distributed denial-of-service (DDoS) attacks based on vulnerabilities in the TCP/IP protocol stack. It begins by introducing DDoS attacks and their architecture, then classifies DDoS attacks according to the TCP/IP layer they target - application layer, transport layer, or internet layer. Specific attack types are described for each layer, including HTTP flooding, SYN flooding, Smurf attacks, and more. The document aims to provide understanding of existing DDoS attack tools, methods, and defense mechanisms.
This document proposes an Expedite Message Authentication Protocol (EMAP) for vehicular ad hoc networks (VANETs) that aims to significantly decrease message loss ratio due to message verification delay compared to conventional authentication methods using certificate revocation lists (CRLs). EMAP replaces the time-consuming CRL checking process with a more efficient revocation checking process using hash-based message authentication codes (HMACs) shared only between valid vehicles. It also uses a novel probabilistic key distribution method to securely share and update keys.
The document discusses defending against distributed denial-of-service (DDoS) attacks and proposes solutions. It describes types of DDoS attacks like SYN flooding and reflector attacks. It then analyzes solutions like route-based packet filtering and a distributed attack detection system using detection systems to identify attacks and install filters. The document concludes current defenses are inadequate and more effective detection-and-filtering approaches need to be developed.
This document is a dissertation analyzing the GandCrab ransomware through network traffic using Wireshark. It outlines the objectives to assess and analyze a ransomware sample in network traffic and the network behavior once ransomware enters a system. The research methodology section describes setting up a test bed environment with 4 systems to execute and monitor the ransomware's network activities by capturing PCAP files with Wireshark for analysis. Key features of the GandCrab PCAP file are then extracted for further research on GandCrab, which spread widely in 2018 as a ransomware-as-a-service.
This document describes five types of SNMP applications: command generator applications, command responder applications, notification originator applications, notification receiver applications, and proxy forwarder applications. It defines procedures for each type of application for generating and processing SNMP messages. It also defines MIB modules for specifying management targets, notification filtering, and proxy forwarding.
This document discusses several types of denial of service (DoS) attacks, including distributed denial of service (DDoS) attacks. It describes how a DDoS attack uses multiple compromised systems or "zombies" to launch a large-scale attack. It also explains specific DoS attack methods like Smurf attacks, which flood a target with ping replies by spoofing the target's IP address, and SYN flood attacks, which exploit the TCP three-way handshake process to overwhelm a server with half-open connections. The document provides technical details on how various DoS attacks work to crash systems or make networks and services unavailable.
A ROBUST MECHANISM FOR DEFENDING DISTRIBUTED DENIAL OF SERVICE ATTACKS ON WEB...IJNSA Journal
Distributed Denial of Service (DDoS) attacks have emerged as a popular means of causing mass targeted service disruptions, often for extended periods of time. The relative ease and low costs of launching such attacks, supplemented by the current inadequate sate of any viable defense mechanism, have made them one of the top threats to the Internet community today. Since the increasing popularity of web-based applications has led to several critical services being provided over the Internet, it is imperative to monitor the network traffic so as to prevent malicious attackers from depleting the resources of the network and denying services to legitimate users. This paper first presents a brief discussion on some of the important types of DDoS attacks that currently exist and some existing mechanisms to combat these attacks. It then points out the major drawbacks of the currently existing defense mechanisms and proposes a new mechanism for protecting a web-server against a DDoS attack. In the proposed mechanism, incoming traffic to the server is continuously monitored and any abnormal rise in the inbound traffic is immediately detected. The detection algorithm is based on a statistical analysis of the inbound traffic on the server and a robust hypothesis testing framework. While the detection process is on, the sessions from the legitimate sources are not disrupted and the load on the server is restored to the normal level by blocking the traffic from the attacking sources. To cater to different scenarios, the detection algorithm has various modules with varying level of computational and memory overheads for
their execution. While the approximate modules are fast in detection and involve less overhead, they provide lower level of detection accuracy. The accurate modules employ complex detection logic and hence involve more overhead for their execution. However, they have very high detection accuracy. Simulations carried out on the proposed mechanism have produced results that demonstrate effectiveness of the proposed defense mechanism against DDoS attacks.
DISTRIBUTED DENIAL OF SERVICE (DDOS) ATTACKS DETECTION MECHANISMijcseit
Pushback is a mechanism for defending against Distributed Denial-of-Service (DDoS) attacks. DDoS attacks are treated as a congestion-control problem, but because most such congestion is caused by malicious hosts not obeying traditional end-to-end congestion control, the problem must be handled by the routers. Functionality is added to each router to detect and preferentially drop packets that probably belong to an attack. Upstream routers are also notified to drop such packets in order that the router’s resources be used to route legitimate traffic hence term pushback. Client puzzles have been advocated as a
promising countermeasure to DoS attacks in the recent years. In order to identify the attackers, the victim server issues a puzzle to the client that sent the traffic. When the client is able to solve the puzzle, it is assumed to be authentic and the traffic from it is allowed into the server. If the victim suspects that the
puzzles are solved by most of the clients, it increases the complexity of the puzzles. This puzzle solving technique allows the traversal of the attack traffic throughout the intermediate routers before reaching the destination. In order to attain the advantages of both pushback and puzzle solving techniques, a hybrid scheme called Router based Pushback technique, which involves both the techniques to solve the problem of DDoS attacks is proposed. In this proposal, the puzzle solving mechanism is pushed back to the core routers rather than having at the victim. The router based client puzzle mechanism checks the host system whether it is legitimate or not by providing a puzzle to be solved by the suspected host.
DISTRIBUTED DENIAL OF SERVICE (DDOS) ATTACKS DETECTION MECHANISM ijcseit
Pushback is a mechanism for defending against Distributed Denial-of-Service (DDoS) attacks. DDoS
attacks are treated as a congestion-control problem, but because most such congestion is caused by
malicious hosts not obeying traditional end-to-end congestion control, the problem must be handled by the
routers. Functionality is added to each router to detect and preferentially drop packets that probably
belong to an attack. Upstream routers are also notified to drop such packets in order that the router’s
resources be used to route legitimate traffic hence term pushback. Client puzzles have been advocated as a
promising countermeasure to DoS attacks in the recent years. In order to identify the attackers, the victim
server issues a puzzle to the client that sent the traffic. When the client is able to solve the puzzle, it is
assumed to be authentic and the traffic from it is allowed into the server. If the victim suspects that the
puzzles are solved by most of the clients, it increases the complexity of the puzzles. This puzzle solving
technique allows the traversal of the attack traffic throughout the intermediate routers before reaching the
destination. In order to attain the advantages of both pushback and puzzle solving techniques, a hybrid
scheme called Router based Pushback technique, which involves both the techniques to solve the problem
of DDoS attacks is proposed. In this proposal, the puzzle solving mechanism is pushed back to the core
routers rather than having at the victim. The router based client puzzle mechanism checks the host system
whether it is legitimate or not by providing a puzzle to be solved by the suspected host.
The document provides best practices for improving the resiliency of applications on AWS against DDoS attacks, including using AWS Shield Standard, AWS Shield Advanced, Amazon CloudFront, Amazon Route 53, Elastic Load Balancing, Amazon API Gateway, Amazon VPC, and Amazon EC2 with Auto Scaling. It describes infrastructure layer attacks like UDP reflection attacks and SYN floods, and application layer attacks. It outlines mitigation techniques and a reference architecture using various AWS services.
Know All About Mitigate TCP Syn Flood Attacks. It is a cyberattack so you need to fix this issue to secure your device & data security using this method. To know more visit https://bit.ly/3czNIcM
Enhancing the impregnability of linux serversIJNSA Journal
Worldwide IT industry is experiencing a rapid shift towards Service Oriented Architecture (SOA). As a
response to the current trend, all the IT firms are adopting business models such as cloud based services
which rely on reliable and highly available server platforms. Linux servers are known to be highly
secure. Network security thus becomes a major concern to all IT organizations offering cloud based
services. The fundamental form of attack on network security is Denial of Service. This paper focuses on
fortifying the Linux server defence mechanisms resulting in an increase in reliability and availability of
services offered by the Linux server platforms. To meet this emerging scenario, most of the organizations
are adopting business models such as cloud computing that are dependant on reliable server platforms.
Linux servers are well ahead of other server platforms in terms of security. This brings network security
to the forefront of major concerns to an organization. The most common form of attacks is a Denial of
Service attack. This paper focuses on mechanisms to detect and immunize Linux servers from DoS .
ENHANCING THE IMPREGNABILITY OF LINUX SERVERSIJNSA Journal
Worldwide IT industry is experiencing a rapid shift towards Service Oriented Architecture (SOA). As a response to the current trend, all the IT firms are adopting business models such as cloud based services which rely on reliable and highly available server platforms. Linux servers are known to be highly secure. Network security thus becomes a major concern to all IT organizations offering cloud based services. The fundamental form of attack on network security is Denial of Service. This paper focuses on fortifying the Linux server defence mechanisms resulting in an increase in reliability and availability of services offered by the Linux server platforms. To meet this emerging scenario, most of the organizations are adopting business models such as cloud computing that are dependant on reliable server platforms. Linux servers are well ahead of other server platforms in terms of security. This brings network security to the forefront of major concerns to an organization. The most common form of attacks is a Denial of Service attack. This paper focuses on mechanisms to detect and immunize Linux servers from DoS .
The document provides an overview of common DDoS attack types including SYN floods, UDP floods, ICMP floods, and HTTP floods. It describes how these attacks work to overwhelm servers and networks with traffic to cause denial of service. The document also covers reflection DDoS attacks using protocols like DNS, NTP, and Memcached to amplify the traffic and discusses recommendations for mitigating these attacks.
Security issues have become a major issue in recent years due to the advancement of technology in networking and its use in a destructive way. A number of Defence strategies have been devised to overcome the flooding attack which is prominent in the networking industry due to which depletion of resources Takes place. But these mechanism are not designed in an optimally and effectively and some of the issues have been unresolved. Hence in this paper we suggest a Game theory based strategy to create a series of Defence mechanisms using puzzles. Here the concept of Nash equilibrium is used to handle sophisticated flooding attack to defend distributed attacks from unknown number of sources
This document discusses distributed denial of service (DDoS) attacks. It begins by defining a DDoS attack as a malicious attempt to disrupt normal traffic by overwhelming a target with a flood of traffic utilizing multiple compromised systems. The document then discusses the evolution of DDoS attacks over time in terms of size and complexity. It provides examples of different types of DDoS attacks including application layer attacks like HTTP floods, protocol attacks like SYN floods, and volumetric attacks like DNS amplification attacks. Finally, it discusses common techniques for mitigating DDoS attacks such as black hole routing, rate limiting, web application firewalls, and anycast network diffusion.
This document summarizes a research paper that proposes a method to protect servers from SYN flood attacks. The method uses an algorithm with a continuous self-detecting process to identify and update information about genuine clients, even in the presence of spoofed packets. It builds a repository of genuine client information that can then be used by security systems like intrusion detection systems and packet filtering to further protect the server. The performance of the SYN flood attack protection could also be improved by implementing the algorithm in hardware using an FPGA.
This is a presentation i made about Denial of Service or a Distributed Denial of Service (DoS / DDoS) and the latest methods used to crash anything online and the future of such attacks which can disrupt the whole internet . Such attacks which are in TB's and can be launched from just single computer. And, there is not much that can be done to prevent them.
A Denial-of-Service (DoS) attack shuts down a machine or a network to make it inaccessible to its intended users. This PPT sheds light upon this kind of a cyberattack and its types, to increase awareness related to the threat that it poses to web servers and applications.
DDoS attacks make headlines everyday, but how do they work and how can you defend against them? DDoS attacks can be high volume UDP traffic floods, SYN floods, DNS amplification, or Layer 7 HTTP attacks. Understanding how to protect yourself from DDoS is critical to doing business on the internet today. Suzanne Aldrich, a lead Solutions Engineer at Cloudflare, will cover how these attacks work, what is being targeted by the attackers, and how you can protect against the different attack types. She will cap the session with the rise in IoT attacks, and expectations for the future of web security.
https://2017.badcamp.net/session/devops-performance-security-privacy/beginner/anatomy-ddos-attack
This document provides an overview of distributed denial of service (DDoS) attacks including:
- Common types of DDoS attacks like UDP floods, SYN floods, DNS floods and HTTP floods and how they work to overwhelm servers.
- How DDoS attacks are evolving to larger sizes and more complex botnets.
- Methods for mitigating DDoS attacks including black hole routing, rate limiting, web application firewalls, anycast networks and cloud-based DDoS protection services.
- A real example of mitigating a massive 400Gbps DDoS attack and the largest attacks seen to date.
Using the Web or another research tool, search for alternative means.pdffms12345
Using namespace std;
The expansion of a steel bridge as it heated to a final Celsius temperature, Tf, from an initial
temperature T0 , can be approximated using the following formula:
Increase in length= a* L*(Tf-T0). Where a is the coefficient of expansion that is for steel is
11.7e-6, L is the length of bridge at temperature T0.
Using this formula, write a C++ program that displays a table of expansion length for a steel
bridge that’s 7365 meters long at 0 degrees Celsius, as the temperature increases to 40 degrees in
5 degree increments.
Solution
#include
using namespace std;
float Length(int Tf)
{
const float a = 11.7E-6;
const float L = 7365;
const float To=0;
return a*L*(Tf-To);
}
int main(int argc, char const *argv[])
{
cout<<\"Intitial Temperature\\tFinal Temperature\\tIncreased Length\ \";
for (int i=1;i<=8;i++)
cout<<0<<\" degrees\\t\\t\"<.
IRJET- DDOS Detection System using C4.5 Decision Tree AlgorithmIRJET Journal
This document proposes a machine learning model using the C4.5 decision tree algorithm to detect DDOS attacks. It trains the model on DDOS attack samples from the CICIDS2017 dataset, dividing the samples into training and test data. The Weka data mining tool is used to build the model with attribute filtering and 10-fold cross-validation. The trained model is then validated on the test data to accurately differentiate between benign and DDOS flooding traffic. This combined signature-based and anomaly-based detection approach can effectively detect complex DDOS attacks.
IRJET- DDOS Detection System using C4.5 Decision Tree Algorithm
DoS Attacks
1. DoS Attacks, Detection, and Mitigation
Josiah Konrad (100847435)
Vladamir Menshikov (100840927)
Reilly Moore (100945421)
Xiao Zhu (100757147)
December 8, 2015
1 Introduction
1.1 Context
Many online services use a “Client-Server” model. In this model a service is provided
through a dedicated server where the individual users (the clients) connect to the server over
the internet at will (Fig 1-1).
One common method for bringing down servers is a Denial of Service Attack (DoS) known
as SYN Flooding (Where SYN is a Synchronization request). SYN Flooding is a relatively safe and
efficient way to bring down a server by overloading its resources through a spam of SYN requests
with a Spoofed Internet Protocol (IP) Address where the return address in the packet is forged to
point the server to a different location. The attack exploits a vulnerability in Transmission Control
Protocol (TCP) connections by interrupting the “Three-way Handshake” between a client and a
server that opens a connection. When interrupted, a server will continue to wait for an
acknowledgement (ACK) tying up resources while it waits. The attack floods the server with SYN
2. requests as the server continues to allocate resources to these connections that will never
complete until the server has used all of its resources and starts refusing the connections of
legitimate users.
Another common DoS attack is the DNS amplification attack. DNS amplification attacks are
still very common on the internet and can be very powerful against an unprepared server. They
work by requesting records from a Domain Name System (DNS) server with your victim’s IP
address as the source address in the IP header. Certain domains contain very large records, so
when the DNS server sends the response, it may be many times larger than the original request.
This achieves an amplification effect, as the victim will receive far larger amounts of data than
the attacker has originally sent. This attack is effective when you are able to flood the victim with
more information than their bandwidth allows.
1.2 Problem Statement
Servers are occasionally brought down by these DoS attacks without warning or record.
Often leaving server owners in the dark about what exactly happened and what they can do
about it in the future. Our goal was to create a set of DoS attack clients that will attack a server
of our creation that can detect an attack, record the event in its logs and possibly even defend
against it.
1.3 Result
One of our attack clients can launch a SYN Flood attack on a TCP server, overloading its
resources causing future connections to fail. Our Server can successfully detect when it is being
attacked by a SYN Flood Attack. It will record the event in the server’s logs and the server will
mitigate the attack based on the algorithm described in section 3. Our second attack client
launches a DNS amplification attack flooding a server with DNS requests effectively multiplying
the attack volume of the client for use against a server. Our server however, can only defend
against SYN flooding, not DNS amplification. The reasons for this are described in section 4.
3. 1.4 Outline
The rest of the report is structured as follows. Section 2 presents the relevant background
information for a TCP connection, a SYN Flood Attack and a DNS Amplification Attack. Section 3
details the qualitative results of our objective relating to the attack clients and the mitigation
efforts of the server. Section 4 details the quantitative results with numerical breakdowns of the
effectiveness of our attack clients and defence server. Finally, section 5 is the conclusion of the
report followed by the references used.
2 Background Information
Recently, DoS and DDoS attacks are becoming more and more common every day (Fig 1-0).
Since they conform to the same specifications as the designed ‘legal’ way to communicate with
a system, the victim cannot avoid or defend against them directly. Because of this, the DoS attack
becomes an effective method for denying a web service (DoS). Often, if an attacker cannot
breach a server’s security they will instead launch a DoS attack to disable the service.
DoS and DDoS attacks can cause serious damage to the victim’s server, not only in hours of
downtime but also in data and subscriber loss. Occasionally an attack can even cause physical
damage to a server. The cost associated with defending against future attacks and recovering
from previous attacks can often be prohibitively high.
Fig 2-0: A news report of a recent DoS attack [1]
4. In light of this reality, it is important to pre-emptively mitigate these attacks to minimize the
potential damage.
IP Spoofing is required for both types of attacks. IP Spoofing is the forgery of the source IP
in a packet. It involves manually editing the header of the packet to change the source IP to a
random value effectively concealing the identity of the spoofer. The attacker can then send
potentially thousands of packets that appear to be from thousands of different users. This
concealment allows the attacker to continuously operate without detection.
SYN Flooding is a DoS attack that exploits the “Three-way Handshake” in a TCP connection
through IP Spoofing. A typical client server TCP connection starts when a client makes a SYN
request to a server, the server sends a SYN-ACK to the client and finally the client sends a final
ACK to complete the connection (Fig 2-1) [2][3].
The SYN Flood Attack interrupts this handshake by changing the return IP in the sent
packet to a different address (IP Spoofing). This causes the server to sent the SYN-ACK to the
spoofed address instead of the attacker’s and the spoofed address will never send back an ACK
because it knows it never sent a SYN request (Fig 2-2) [4].
5. This redirection has the consequence of wasting server resources waiting for the spoofed IP
to respond. The attacker will send the server many seemingly legitimate SYN requests that are
indistinguishable from actual users [5]. The server is then flooded with SYN requests causing it to
allocate resources to each new connection. Eventually the attacker will have flooded the server
with so many connection requests that will never complete the server will run out of resources
to allocate to new connections either crashing the server or simply refusing connection to any
new clients effectively denying them the service [6].
DNS Amplification works by requesting records from a Domain Name System (DNS) server
with your victim’s IP address as the source address [9]. When the DNS server sends a response,
it is often many times larger than the original request [10]. This achieves the desired amplification
effect, as the victim will receive far larger amounts of data than the attacker has originally sent
(Fig 2-3) [11].
6. 3 Result
We have developed an attack client that is designed to launch a SYN Flood Attack on a
TCP server. The client is capable of overloading a server with TCP SYN requests that have a
spoofed IP protecting the client’s identity. The spoofed IP is randomized with every packet
making it difficult for a server to detect and block the attack client. Ideally a SYN flood attack
would use multiple instances of the attack client, however a single client can often flood a
server with enough spoofed requests to effectively disable it.
The server protects against SYN flood attacks through design. It does this by keeping a
large master list of active SYN requests in a fixed size array with a counter denoting the position
(effective size) in the array. Every SYN request has its IP logged in the array as the server waits
for an ACK. When an ACK is received the server records that client on a separate list of
legitimate clients (Fig 3-1). The first array of SYN requests has a maximum arbitrary size of
1000. Every time the position counter of the array reaches the maximum size (1000) the server
resets the counters value to 0, effectively flushing the buffer of SYN requests. This
implementation works because it has a fixed amount of resources allocated to active SYN
requests and every time the resources are expended it flushes the buffer and starts from 0.
This system can still drop legitimate users attempting to connect if they fail to connect before
the buffer is flushed but a sufficiently large buffer makes this scenario relatively unlikely. The
7. end result of this system is that the server can now handle a large amount of spoofed SYN
requests by discarding incomplete SYN requests when the buffer is full.
The server can withstand an average sized attack indefinitely where a similarly sized
server without any defence could be brought down in seconds. The rate of detection is
essentially perfect, however at large attack volumes legitimate clients attempting to establish
a new connection may be dropped.
The implementation of our SYN Flooding mitigation makes all but the largest of SYN Flood
Attacks a non-issue. This implementation is likely unsuited for commercial use as its design still
leaves it very susceptible to other types of DoS attacks. Since many DoS attacks are multi-
factored the usefulness of this implementation is likely minimal. One of the most common
methods for defending against SYN Flood attacks is “SYN Cookies” where the only major
weakness is that it discards all TCP options. Our implementation fixes this issue by completing
a standard TCP connection but this comes at a cost of occasionally dropping users that are
Fig 3-1: The SYN Flood Mitigation Algorithm
8. attempting to connect while the server is under attack. The likelihood of being dropped is
directly proportional to the volume of the attack. This is described in detail in section 4.
Our second client is designed to launch a DNS amplification attack, however there is no
server software counterpart for this attack. The client has a very straightforward interface for
basic use: it is simply run from a command line argument taking only your victim’s IP address
and port (and optionally a DNS server to use). From here it simply runs in an infinite loop
sending packets until receiving the interrupt signal.
For more advanced use, which DNS servers are queried as well as which records are
requested may be easily changed, however this requires recompilation. This implementation
is also straightforward from a programming point of view, with respect to modifications to the
frequency of attacks, as well as distributing across multiple DNS servers. The DNS attack code
was separated into its own portable file.
In order to implement the attack, we constructed a datagram and sent it over a raw
socket. The datagram is constructed first with the headers (IP, UDP, DNS) and then with the
DNS query body. As to not illicit suspicion, most of the header fields and query fields are set to
the typical values for making a DNS requests. The only significant differences are in the IP
header and the DNS query. The source address in the IP header is spoofed to be the target’s
address,
header->saddr = inet_addr(sender_ip);
and the DNS query type is set to 255, the value to request ‘ANY’ from the DNS server, which
returns all record types.
q->qtype = htons(0x00ff);
Lastly we calculate the UDP and IP header checksums since we are using raw sockets and
the kernel’s networking stack will leave it as is. If the checksum fields are invalid, the attacker’s
local router will most likely discard the packet entirely. After all of this the attack is amplified
many times allowing a single connection to bring down a larger server.
9. 4 Evaluation
Our implementation of a SYN Flood attack was used for all tests relating to the
evaluation of the effectiveness of our server’s defence.
Our SYN Flood attack client was able to consistently bring down our basic undefended
test server in approximately ~1 second. As the basic server had no defence its resources were
filled to the point that the server would crash. It is worth noting however that an average
commercial server has options to deal with a sudden spike in network traffic allowing it to
withstand a SYN Flood attack much longer before eventually running out of resources.
Our defence client was designed to withstand a SYN Flood attack indefinitely as it does
not bind any resources to a connection until it has been verified and it is able to do so with
varying effectiveness depending on the volume of the attack. There is a theoretical maximum
limit of SYN requests our server may take per second where the server cannot process the
requests as fast as it receives them but we were not able to reach this limit with our
implementation.
Our detection rate for if the server is under attack was 100% in all tests when the attack
was sustained for more than 100s (Fig 4-0). In volumes of 1000 requests per second or more
the server detected the attack nearly instantaneously. The detection rate for this
implementation is somewhat irrelevant however because unlike other defences that must be
activated upon detection our defence is through design and cannot be activated or deactivated.
Therefore, the detection rate in the server’s log is just for the benefit of the server owner.
0
50
100
150
10 100 1000 10000
Detection Speed (s) vs. Attack
Volume (requests/s)
Requests per Second
Fig 4-0: Detection speed (s) of a SYN
Flood Attack with respect to the
volume of an attack.
Seconds
10. The biggest weakness of our defence algorithm is false positives (Fig 4-1). If the server is
not under attack only 1 in 1000 legitimate requests will be flushed from the buffer (effectively a
false positive). Note that these rates for dropped clients only pertain to clients attempting to
connect. Once a client has successfully established a connection it should never be dropped.
When the server is under attack with a mild volume (10-100 requests per second) the rates are
about the same at only 1 in 1000 false positives. The rate of legitimate user attempted
connections being dumped only increases when the attack volume is higher than 1000 per
second. At this point it is estimated that roughly 10% of all legitimate requests would be
dropped depending greatly on the connection speed between the users and the server. Our
tests were base on an approximate time to connect of about ~200ms. Finally, at 10,000 and a
theoretical 100,000 requests per second the server would be flushing the buffer faster than the
legitimate clients could connect and we estimate roughly 50% and 98% respectively would be
dumped as false positives. This again however only effects users attempting to establish a
connection. Users already connected should remain unaffected. At very high volumes
upgrading the server’s hardware would be the best solution to mitigate an attack.
Fig 4-1: Rate of false positives for mitigation of a SYN Flood attack.
*Theoretical
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
10 100 1000 10000 100000*
Rate of False Positives for SYN Flood Defence
Requests Per Second
11. The DNS amplification attack was successful in terms of function, but not entirely in
terms of result. When run, it is capable of sending out 20,000+ packets per second, although
that rate is largely dependant on the CPU. These packets were verified to be valid DNS request
by capturing them with Wireshark.
As an example of our amplification effect, we used the ‘dig’ tool available in the
‘dnsutils’ package from the official Arch Linux repositories (this may be available by default on
other distributions, or from the respective distributions repositories). This tool makes a DNS
query for the domain and query or your choice. The tool was used to measure request and
response sizes, since the requests made are the same as our attack client.
For example, we ran dig ANY tf2pug.me, and caught the packets in Wireshark to view
the size on wire. For this domain, the requests were 80 bytes, and the response was 436 bytes.
Achieving a small amplification of 5.45 times.
At this point, things get more complicated. DNS servers have a wide range of policies in
terms of their response. The first major complication occurs when requesting very large
responses. For example, requesting ‘ANY’ from “ietf.org” will give you a TCP response because
DNS servers will only send UDP responses up to 512 bytes, or 4096 bytes if you indicate you can
handle it in your request. The response for ietf.org is 4935 bytes, and as such is useless to us
(since the victim has not initiated the TCP connection). In the case of having a domain which
gives a response just under 4096 bytes, we would achieve an amplification of just over 50
times. Below is an example of achieved amplification off of common domains (Fig 4-2).
0
100
200
300
400
500
600
700
800
google.com amazon.com wikipedia.org ebay.com
Request/Response Sizes for DNS Records
Request Response
5.89x
8.12x
3.36x
9.01x
Bytes
Fig 4-2: Typical amplification
from common domain records
12. The next complication caused by DNS server behaviour is in the case of receiving mass
requests from the same IP address. The majority of DNS servers (and almost all commonly used
servers) implement rate limits of some sort to prevent their servers from being used in a DNS
amplification attack. There are servers with no restrictions, however they are not always easily
findable and are typically from countries such as China, Afghanistan, or other similar countries
with less strict technological regulation.
Most large networks operators also have methods in place to prevent an attack from being
launched on their network. This can be very easily achieved by simply filtering out all packets
with invalid IP headers attempting to leave the network. We have determined through testing
that Carleton University likely has this, or something similar in place that prevents us from
launching an attack from the computer labs. We were however able to successfully launch
attacks from our respective home networks.
5 Conclusion
The DNS Amplification attack client can successfully amplify the client’s attack volumes
allowing it to launch a DoS attack even if the server has a larger bandwidth than the client, taking
into account the pitfalls described above. The SYN Flood attack client can successfully flood a
server with TCP SYN requests using a spoofed IP allowing it bring down servers that use TCP and
do not have adequate DoS protection. The server can successfully detect, log and mitigate a SYN
Flood attack, allowing legitimate users to continue to connect to the server and use its services.
Contributions of Team Members
Josiah Konrad: Developed and evaluated the SYN Flood Mitigation Algorithm, contributed to
the Anti-SYN Flood Server. Final editor for the report.
Vladamir Menshikov: Worked primarily on the Anti-SYN Flood Server, contributed to the SYN
Flood Attack Client and SYN Flood Mitigation Algorithm.
Reilly Moore: Developed and evaluated the DNS Attack Client, contributed to the Anti-SYN
Flood Server.
Xiao Zhu: Developed the SYN Flood Attack Client and implemented IP Spoofing for both
clients, contributed to Anti-SYN Flood Server.
13. References
[1] Newsweek, “Report: Canadian Government Websites inaccessible Following Denial-of-Service
Attack” http://www.newsweek.com/canadian-government-websites-inaccessible-following-denial-
service-attack-344002. Accessed: November 27, 2015
[2] Rhys Haden, “TCP” http://www.rhyshaden.com/tcp.htm. Accessed: November 21, 2015
[3] OmniSecu.com, “TCP Three-way Handshake” http://www.omnisecu.com/tcpip/tcp-three-way-
handshake.php. Accessed: November 21, 2015
[4] JUNIPER NETWORKS, “Understanding SYN Flood Attacks”
http://www.juniper.net/documentation/en_US/junos12.1/topics/concept/denial-of-service-network-
syn-flood-attack-understanding.html. Accessed: November 23, 2015
[5] Wesley M. Eddy, “Defenses Against TCP SYN Flooding Attacks”
http://www.cisco.com/web/about/ac123/ac147/archived_issues/ipj_9-4/syn_flooding_attacks.html.
Accessed: November 25, 2015
[6] Thanglalson Gangte, “SYN Flood Attacks-How to protect?-article” https://hakin9.org/syn-flood-
attacks-how-to-protect-article/. Accessed: November 23, 2015
[7] Mariusz Burdach, “Hardening the TCP/IP stack to SYN attacks”
http://www.symantec.com/connect/articles/hardening-tcpip-stack-syn-attacks. Accessed: November
23, 2015
[8] Rik Farrow, “TCP SYN Flooding Attacks and Remedies”
http://www.networkcomputing.com/unixworld/security/004/004.txt.html. Accessed: November 23,
2015
[9] Maxim Blagov, “DDoS Attack Glossary” https://www.incapsula.com/ddos/attack-glossary/dns-
amplification.html. Accessed: November 17, 2015
[10] DAVID CORNELL, “DNS AMPLIFICATION ATTACKS” https://labs.opendns.com/2014/03/17/dns-
amplification-attacks/. Accessed: November 17, 2015
[11] Matthew Prince, “Deep Inside a DNS Amplification DDoS Attack” https://blog.cloudflare.com/deep-
inside-a-dns-amplification-ddos-attack/. Accessed: November 21, 2015
Note: References [7] and [8] were general research sources and not referenced directly in this report.