An approach in identifying and tracing back spoofed IP packets to their sources

With internet expanding in every aspect of businesses infrastructure, it becomes more and more important to make these businesses infrastructures safe and secure to the numerous attacks perpetrated on them conspicuously when it comes to denial of service (DoS) attacks. A Dos attack can be summarized as an effort carried out by either a person or a group of individual to suppress a particular outline service. This can hence be achieved by using and manipulating packets which are sent out using the IP protocol included into the IP address of the sending party. However, one of the major drawbacks is that the IP protocol is not able to verify the accuracy of the address and has got no method to validate the authenticity of the sender’s packet. Knowing how this works, an attacker can hence fabricate any source address to gain unauthorized access to critical information. In the event that attackers can manipulate this lacking for numerous targeted attacks, it would be wise and safe to determine whether the network traffic has got spoofed packets and how to traceback. IP traceback has been quite active specially with the DOS attacks therefore this paper will be focusing on the different types of attacks involving spoofed packets and also numerous methods that can help in identifying whether packet have spoofed source addresses based on both active and passive host based methods and on the router-based methods

Flooding attacks to internet threat monitors (ITM): Modeling and counter measures using botnet and honeypots

The Internet Threat Monitoring (ITM),is a globally scoped Internet monitoring system whose goal is to measure, detect, characterize, and track threats such as distribute denial of service(DDoS) attacks and worms. To block the monitoring system in the internet the attackers are targeted the ITM system. In this paper we address flooding attack against ITM system in which the attacker attempt to exhaust the network and ITM's resources, such as network bandwidth, computing power, or operating system data structures by sending the malicious traffic. We propose an information-theoretic frame work that models the flooding attacks using Botnet on ITM. Based on this model we generalize the flooding attacks and propose an effective attack detection using Honeypots

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 today’s 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 “leniency” 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 DARE’s 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

Deployable filtering architectures against large denial-of-service attacks

Denial-of-Service attacks continue to grow in size and frequency despite serious underreporting. While several research solutions have been proposed over the years, they have had important deployment hurdles that have prevented them from seeing any significant level of deployment on the Internet. Commercial solutions exist, but they are costly and generally are not meant to scale to Internet-wide levels. In this thesis we present three filtering architectures against large Denial-of-Service attacks. Their emphasis is in providing an effective solution against such attacks while using simple mechanisms in order to overcome the deployment hurdles faced by other solutions. While these are well-suited to being implemented in fast routing hardware, in the early stages of deployment this is unlikely to be the case. Because of this, we implemented them on low-cost off-the-shelf hardware and evaluated their performance on a network testbed. The results are very encouraging: this setup allows us to forward traffic on a single PC at rates of millions of packets per second even for minimum-sized packets, while at the same time processing as many as one million filters; this gives us confidence that the architecture as a whole could combat even the large botnets currently being reported. Better yet, we show that this single-PC performance scales well with the number of CPU cores and network interfaces, which is promising for our solutions if we consider the current trend in processor design. In addition to using simple mechanisms, we discuss how the architectures provide clear incentives for ISPs that adopt them early, both at the destination as well as at the sources of attacks. The hope is that these will be sufficient to achieve some level of initial deployment. The larger goal is to have an architectural solution against large DoS deployed in place before even more harmful attacks take place; this thesis is hopefully a step in that direction

IP TRACEBACK Scenarios

Internet Protocol (IP) trace back is the enabling technology to control Internet crime. In this paper, we present novel and practical IP traceback systems which provide a defense system with the ability to find out the real sources of attacking packets that traverse through the network. IP traceback is to find the origin of an IP packet on the Internet without relying on the source IP address field. Due to the trusting nature of the IP protocol, the source IP address of a packet is not authenticated. As a result, the source address in an IP packet can be falsified (IP address spoofing). Spoof IP packets can be used for different attacks. The problem of finding the source of a packet is called the IP traceback problem. IP Traceback is a critical ability for identifying sources of attacks and instituting protection measures for the Internet. Most existing approaches to this problem have been tailored toward DDoS attack detection

Protecting web services with service oriented traceback architecture

Service oriented architecture (SOA) is a way of reorganizing software infrastructure into a set of service abstracts. In the area of applying SOA to Web service security, there have been some well defined security dimensions. However, current Web security systems, like WS-Security are not efficient enough to handle distributed denial of service (DDoS) attacks. Our new approach, service oriented traceback architecture (SOTA), provides a framework to be able to identify the source of an attack. This is accomplished by deploying our defence system at distributed routers, in order to examine the incoming SOAP messages and place our own SOAP header. By this method, we can then use the new SOAP header information, to traceback through the network the source of the attack. According to our experimental performance evaluations, we find that SOTA is quite scaleable, simple and quite effective at identifying the source.<br /

Flow-oriented anomaly-based detection of denial of service attacks with flow-control-assisted mitigation

Flooding-based distributed denial-of-service (DDoS) attacks present a serious and major threat to the targeted enterprises and hosts. Current protection technologies are still largely inadequate in mitigating such attacks, especially if they are large-scale. In this doctoral dissertation, the Computer Network Management and Control System (CNMCS) is proposed and investigated; it consists of the Flow-based Network Intrusion Detection System (FNIDS), the Flow-based Congestion Control (FCC) System, and the Server Bandwidth Management System (SBMS). These components form a composite defense system intended to protect against DDoS flooding attacks. The system as a whole adopts a flow-oriented and anomaly-based approach to the detection of these attacks, as well as a control-theoretic approach to adjust the flow rate of every link to sustain the high priority flow-rates at their desired level. The results showed that the misclassification rates of FNIDS are low, less than 0.1%, for the investigated DDOS attacks, while the fine-grained service differentiation and resource isolation provided within the FCC comprise a novel and powerful built-in protection mechanism that helps mitigate DDoS attacks

TDDA- Traceback-based Defence against DDoS Attack

Look In today's fast growing of internet use, security of the data and information, resources and other useful files are more important viewpoints. Distributed Denial-of-Service (DDoS) attacks are responsible for making a machine or network resource unavailable to its appropriate users. Also a DDoS attack reduces the efficiency or capability of the server to doing its job. That’s why they are very challenging issues for us. The problem is rises when spoofed IP addresses are present in the attack packets. In order to solve this critical situation of problem, that’s why we proposed a new mechanism to efficiently reduce the impact or outcome caused by DDoS attacks. In some cases, even if the attacking traffic can be filtered by the victim side, here also the attacker may blocks the access of the victim by consuming the computing resources or by consuming a large amount portion of the bandwidth of the victim. This paper is proposes a Traceback-based Defense against DDoS Attacks (TDDA) approach to resolve this problem very goodly. In this paper, we present and design one technique that can be impressively filter out the majority of DDoS attack traffic. Our primary objective or intention for this work is to improving the overall throughput and performance of the appropriate traffic and also reduce the attack traffic to maintain the quality of service for that user