Introduction do DDos Attacks in 2017

How DDoS even works for 192.168 l?

We live in an information society. We are used to obtain required 192.168 l information in a timely fashion in order to make knowledgeable and responsible decisions. Inability to collect information may lead to financial losses, cultural conflits or even human injuries. In this thesis, we focus on denial of service attacks, a common class of attacks against the availability of resources in computer networks.

Denial of Service attacks

A denial of service attack (DoS attack) is an attempt to make a computer resource unavailable to its intended users. A distributed denial of service attack (DDoS attack) occurs when multiple attack sources collaborate to achieve this goal. Most DDoS attacks employ the IP spoofing to conceal identities of attacking machines [BBH09].

DoS attacks taxonomy

DoS attacks can be classified by many criterias. The following taxonomy is loosely derived from the work of Mirkovic and Reiher.
∙ Vulnerability (Semantic): Vulnerability attacks exploit a specific feature or an implementation bug of some protocol or of an application installed at the victim in order to consume excess amounts of its resources.
∙ Flooding (Brute-force): Flooding attacks are performed by initiating a vast amount of seemingly legitimate transactions or by sending a huge volume of unsolicited and unexpected traffic. Exhausted resources may include available computational time, available operating memory, free space in various buffers and status tables or available incoming bandwidth. A non-exhaustive list of existing flooding DoS attacks and their classification by key protocol into HTTP-based, TCP-based. Flooding attacks can be then treated in two groups – attacks at the network layer and
at the application layer: Network layer: Properties of network and transport layer protocols are exploited.

Introduction

∙ Low rate DoS attack (shrew attack): Low rate DoS attacks attempt to deny bandwidth to TCP flows while sending packets at a sufficiently low average rate to evade the detection.
∙ Pulsing attacks: Compromised hosts send out short bursts of attack packets to the victim instead of generating a constant packet flood during pulsing attacks.
∙ Reflection: A reflector is any IP host that will return a packet if sent a packet.

Attackers

orchestrate the hosts under their control to send the spoofed traffic purportedly coming from the victim to reflectors. The result is that the flood at the victim arrives from a significantly higher number of sources, an exceedingly diffuse flood likely clogging every single path to the victim from the rest of the internet 192.168 l.
∙ Amplification: An amplification attack is a type of the reflection attack in which reflectors’ responses are larger than queries. Therefore the volume of the attack traffic from the source to the victim is multiplied.
Application layer: Application layer protocols are exploited. Connection establishments on the network layer and the transport layer are required. Application-layer requests originating from compromised hosts may be difficult to distinguish from those generated by legitimate users.

Testing environment

Numerous ways of the DoS attack experimenting have been presented in the literature. Most notable approaches are listed.
∙ Testing in a real environment: Although results can be very precise, due to disruptive effects of DoS attacks the testing in real environments is rarely performed. Also results from different networks are not comparable and tests on a global scale often require an agreement of multiple parties.
∙ Testing in a testbed environment: Testbed environments 192.168 l provide a sufficient fidelity, but experiments have shown results are not comparable between different testbeds.
∙ Traffic simulation: Simulations enable an easy and fast creation of topologies, however, scaling is an ongoing issue. Simulated nodes possess an infinite CPU and bus capacity, which can interfere with results especially when complex DDoS attacks are simulated.
∙ Packet traces replay: Results are comparable and tests can be repeated. However, well-known documented up-to-date packet traces are sparse. Alternatively a proprietary set of packet traces can be generated using the overlay methodology, but it is hard to determine whether the background traffic is attack-free and sharing of proprietary traces is complicated because of privacy concerns.

Detector placement

The deployment of the DoS attack detection system at the victim end is historically the most common. The victim has the highest motivation to mitigate attacks and the attack impact is most easily observed. However, victim end countermeasures cannot help in case of a truly damaging attack that overwhelms access links to the victim. Also, existing countermeasures (e.g., rate limiting, traffic filtering) are frequently performed on a per-subnet basis. Therefore, legitimate users from the attacker’s subnet may suffer the denial of service effect as a result of the defense’s collateral damage. Identification of attack sources is nearly impossible without the cooperation of intermediate networks.

An intermediate network is any network, which is traversed by the attack traffic en route from the source to the victim. Detection and defense at the intermediate network lower the global congestion and to a certain level offer a capability to identify source attack nodes. However, a deployment incentive is an open question since intermediate networks usually benefit little from the ability to stop ongoing attacks. Also, network devices on high rate backbone links reserve a vast majority of their resources to routing and switching purposes. Finally, outgoing DoS 192.168 l attacks can be detected directly at the source hosts or at first mile routers. Source end detectors can prevent congestion, allow for an easy identification of attacking hosts and can apply complex detection algorithms. Detection algorithms may be more resource demanding than in case of the victim detectors, because the source end detectors analyze less data and the combined computational power of all detectors is higher than the computational power of a limited set of victim detectors. However, source end detectors observe only a portion of attack traffic and they do not have access to the internal victim state, which makes the recognition of attack traffic difficult. The problem of deployment incentive is similar as with detectors for intermediate networks.

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