Denial Of Service

Though the Tor authors considered the selective denial of service attack in their threat model, they did not analyze its impact. Next, we analyze the effect of a selective DoS on Tor. We assume that dishonest routers will perform DoS on any tunnel they cannot compromise. This attack is easy to implement: if the adversary acts as a first or last router on a tunnel, the tunnel is observed for a brief period of time and matched against all other tunnels where a colluding router is the last or first router, respectively. If there is a match, the tunnel is compromised; otherwise, the adversary kills the tunnel by no longer forwarding traffic on it. The adversary also kills all tunnels where it is the middle node, unless both the previous and the next hop are also colluding. Alternately, Bauer et al. present an algorithm for linking tunnels even before traffic has been sent over them [1].

Launching a denial of service (DoS) attack is trivial, but detec- tion and response is a painfully slow and often a manual process. Automatic classification of attacks as single- or multi-source can help focus a response, but current packet-header-based approaches are susceptible to spoofing. This paper introduces a framework for classifying DoS attacks based on header content, transient ramp-up behavior and novel techniques such as spectral analysis. Although headers are easily forged, we show that characteristics of attack ramp-up and attack spectrum are more difficult to spoof. To eval- uate our framework we monitored access links of a regional ISP detecting 80 live attacks. Header analysis identified the number of attackers in 67 attacks, while the remaining 13 attacks were clas- sified based on ramp-up and spectral analysis. We validate our re- sults through monitoring at a second site, controlled experiments, and simulation. We use experiments and simulation to understand the underlying reasons for the characteristics observed. In addition to helping understand attack dynamics, classification mechanisms such as ours are important for the development of realistic models of DoS traffic, can be packaged as an automated tool to aid in rapid response to attacks, and can also be used to estimate the level of DoS activity on the Internet.

— Perform aggressive potential duplicate packet suppression to ensure that packets duplicated either in the network or by the denial-of-service attack victim (for example, multiple SYN ACKs to a single SYN) do not artificially magnify the scope of an attack [Moore et al. 2004]. In this study, we remove any packet with the same flow tuple < source IP address, destination IP ad- dress, protocol, source port, destination port > as another packet seen in the last five minutes. For ICMP error messages, we extract the IP addresses, protocol and ports from the original packet within the ICMP messages. Note that a high-rate packet stream with constant IP addresses, protocol, and ports in the raw data would, at most, result in one packet per five minutes in the extracted backscatter dataset. This approach provides very aggressive suppression, removing packets that may not be true packet-level duplicates, thus leading to underestimation of packet counts and rates.

In February of 2000, a series of massive denial-of-service (DoS) attacks incapacitated several high-visibility In- ternet e-commerce sites, including Yahoo, Ebay, and E*trade. Next, in January of 2001, Microsoft’s name server infrastructure was disabled by a similar assault. Despite attacks on high-profile sites, the majority of attacks are not well publicized. Many other domes- tic and foreign sites have also been victims, ranging from smaller commercial sites, to educational institu- tions, public chat servers and government organizations. While it is clear from these anecdotal reports that denial-of-service attacks continue to be a problem, there is currently not much quantitative data about the preva- lence of these attacks nor any representative character- ization of their behavior. Unfortunately, there are mul-

In this paper, we propose a new approach to preventing and constraining denial-of-service (DoS) attacks. Instead of be- ing able to send anything to anyone at any time, in our architecture, nodes must first obtain “permission to send” from the destination; a receiver provides tokens, or capa- bilities, to those senders whose traffic it agrees to accept. The senders then include these tokens in packets. This en- ables verification points distributed around the network to check that traffic has been certified as legitimate by both endpoints and the path in between, and to cleanly discard unauthorized traffic. We show that our approach addresses many of the limitations of the currently popular approaches to DoS based on anomaly detection, traceback, and push- back. Further, we argue that our approach can be readily implemented in today’s technology, is suitable for incre- mental deployment, and requires no more of a security in- frastructure than that already needed to fix BGP’s security weaknesses. Finally, our proposal facilitates innovation in application and networking protocols, something increas- ingly curtailed by existing DoS measures.

Abstract— Denial of Service attacks have become a weapon for extortion and vandalism causing damages in the millions of dollars to commercial and government sites. Legal prosecution is a powerful deterrent, but requires attribution of attacks, currently a difficult task. In this paper we propose a method to automatically fingerprint and identify repeated attack scenarios—a combination of attacking hosts and attack tool. Such fingerprints not only aid in attribution for criminal and civil prosecution of attackers, but also help justify and focus response measures. Since packet contents can be easily manipulated, we base our fingerprints on the spectral characteristics of the attack stream which are hard to forge. We validate our methodology by applying it to real attacks captured at a regional ISP and comparing the outcome with header-based classification. Finally, we conduct controlled experiments to identify and isolate factors that affect the attack fingerprint.

Policies and Procedures: Having a plan of action is key in handling any potential attacks. With the risk of a denial of service attack significantly lowered as a result of making tweaks to your system, your consultant will work with management to develop a plan to execute should an attack occur. This plan will include specific procedures to keep your network running smoothly despite an attack.

Denial of Service is becoming an important concern for networks. The approach taken for mesh terrestrial networks has been to use the anti-clogging technique based on an exchange of cookies in order to screen out requests with bogus source addresses by sending replies including a cookie to the claimed address. This technique however turns out to be inefficient to thwart DoS attacks in satellite networks because of the inherent broadcast capability of the satellite system and thus continue to send bogus messages with correct cookies, the terrestrial solutions are not suitable to satellite networks.

Distributed Denial-of-Service attacks are still a big threat to the Internet. Several proposals for coping with the attacks have been made in the recent past, but neither of them are successful on themselves alone. In this paper, we present a system that helps in the defence in depth of a network from DDoS attacks. In addition to state-of-art active and passive security defences, we propose a honeypot for such attacks. The goal is to convincingly simulate the success of the compromise of a system to a potential DDoS at- tacker. Thereby, we can implement the lessons learned by the hon- eypot in our other systems to harden them against such attacks. On the other hand, we protect the rest of our network infrastruc- ture form the impact of such an attack.

This tutorial describes what Denial of Service (DoS) attacks are, how they can be carried out in IP networks, and how one can defend against them. Distributed DoS (DDoS) attacks are included here as a subset of DoS attacks. A DoS attack has two phases: a deployment and an attack phase. A DoS program must first be deployed on one or more compromised hosts before an attack is possible. Mitigation of DoS attacks requires thus defense mechanisms for both phases. Completely reliable protection against DoS attacks is, however, not possible. There will always be vulnerable hosts in the Internet, and many attack mechanisms are based on ordinary use of protocols. Defense in depth is thus needed to mitigate the effect of DoS attacks. This paper describes shortly many defense mechanisms proposed in the literature. The goal is not to implement all possible defenses. Instead, one should optimize the trade-off between security costs and acquired benefits in handling the most important risks. Mitigation of DoS attacks is thus closely related to risk management.

technology, the perpetrator is able to multiply the effectiveness of the Denial of Service significantly by harnessing the resources of multiple unwitting accomplice computers which serve as attack platforms. Typically a DDoS master program is installed on one computer using a stolen account. The master program, at a designated time, then communicates to any number of "agent" programs, installed on computers anywhere on the internet. The agents, when they receive the command, initiate the attack. Using client/server technology, the master program can initiate hundreds or even thousands of agent programs within seconds

2) Worm Hole attacks: A wormhole attack’s objective is similar to rushing attack but the technique used is different. During a wormhole attack, two or more malicious nodes collude together by establishing a tunnel using an efficient communication medium (wired connection or high-speed wireless connection, etc.). During the route discovery phase of on-demand routing protocols, the Route Request messages are forwarded between the malicious nodes using the tunnel [5]. Therefore, the first Route Request message that reaches the destination node is the one forwarded by the malicious node. Consequently, the malicious nodes are added in the path from source to destination. Once the malicious nodes are included in the routing path, the malicious nodes either drop all the packets, resulting in complete denial of service, or drop the packets selectively to avoid detection.[5]

1a. DENIAL-OF-SERVICE (DOS) ATTACK: In a denial-of-service (DoS) attack, an attacker attempts to prevent legitimate users from accessing any services and information. By attacking your computer and its network connection or sites you are making use of, an attacker may able to prevent you from accessing any of your email, websites, online accounts (banking, etc.), or other services that rely on the affected computer.

Distributed Denial of Service attack (DDoS) attack has affected a large number of networks all over the world. It is not a single kinkd of attack; instead it comprises variety of attacks which occur at protocol level as well as application level. These attacks are reviewed in this paper. With attacks, defense comes naturally. Defense approaches regarding DDOS employ several methods and architectures, which are studied in this paper.

On denial-of-service (DoS) attacks for wireless sensor networks (WSNs), we investigated the security aspects of the physical layer. We conducted the simulative performance analysis of jamming attacks for signal-to-noise ratio (SNR), bit error rate (BER), network throughput and packet delivery ratio (PDR) using IEEE 802.15.4 based OPNET simulative model for WSN under constant and varying intensity of jamming attacks. Under constant jamming attack, simulations revealed that average sink node PDR degrades from 79.01% in a normal scenario, to 59.22% in jammed scenario. Also, normal scenario shows maximum PDR of 89.68% and minimum PDR of 70.02% while jammed scenario shows a maximum PDR of 64.93% and minimum PDR of 49.90%. Under varying intensity of jamming attack, simulations revealed that average sink node PDR decreases, from 79.01% in a normal scenario, by 5.54%, 4.53%, 6.36% and 3.35% with the introduction of one, two, three and four jammers respectively. Further, the average SNR decreases, from 73.59%, in a normal scenario, by 5.43%, 5.63%, 10.44% and 20.39% with the introduction of one, two, three and four jammers respectively.

In a denial of service, attackers may do attacks from a single device or from multiple devices that they control. When attackers attack systems from multiple devices or places that are distributed in the network, it is called a distributed denial of service (DDos) attack. But when attackers attack systems from a single device or place, it is called a single-source denial of service (SDos) attack. DDos attacks have strong impact than SDos attacks, because of the amount of bandwidth, CPU, memory that can be affected. In practice, protecting systems against DDos attacks is proven to be harder than defending against SDos attacks.

attack in this DoS attack is distributed geographically in nature. That is, the attacker attacks from different locations onto the communication channel as well as onto the mobile nodes. The number of messages by different malicious nodes is sent to the legitimate users in different time slots and due to which the communication between the mobile nodes themselves and with the infrastructure is obscured. Hence, the network is unavailable for useful work. Since, the number of messages is sent from different locations, the network is busy in validating them, so, it denies the work of legitimate users and therefore, Distributed Denial of Service attack occurs in the communication.

We have presented the results of our project which aims to defend a server against Denial-of-Service attacks using a technique based on client puzzles. We developed a new model for puzzle distribution using a robust service and solutions to the puzzles allow clients to access communication channels. Here we also generate the key only for the valid clients and clients must solve the puzzle within the estimated time. Using these two we can find the spoofing node. This is shown by our experimental results.

Abstract: The Wireless Sensor Networks (WSNs) are emerging as one of the most reliable technologies for implementing ubiquitous computing ultimately leading to an all-pervasive paradigm of computing infrastructure that can be utilized for several interesting applications. Denial of Service (DoS) is an attack where a number of compromised systems attack a single target, thereby causing denial of service for users of the targeted system. The flood of incoming messages to the target system essentially forces it to shut down, thereby denying service to the system to legitimate users. DoS attacks disrupt the entire or a part of WSN network. Detection and avoidance of DoS attacks is necessary. For that we design message observation and common key authentication mechanisms by which cluster head (CH) as well as any other sensor nodes in network can able to identify the communicating node is an attacker node or not and isolate that attacker node. This approach is efficiently, detects and avoids Dos attack completely.

The Smurf Attack is a denial-of-service attack in which large numbers of Internet Control Message Protocol (ICMP) packets with the intended victim's spoofed source IP are broadcast to a computer network using an IP Broadcast address. Most devices on a network will, by default, respond to this by sending a reply to the source IP address. If the number of machines on the network that receive and respond to these packets is very large, flooding the victim's computer with traffic. This can slow down the victim's computer to the point where it becomes impossible to work on.