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The recent tide of Distributed Denial of Service attacks against high-profile web sites, demonstrate how damaging the DDoS attacks are and how defenseless the Internet is under such attacks. The services of these web sites were unavailable for hours or even days as a result of the attacks. In this attack the adversary simultaneously send a large volume of traffic to a victim host or network. The victim is overwhelmed by so much traffic that it can provide little or no service to its legitimate clients. The burst traffic and high volume are the common characteristics of App-DDoS attacks and flash crowds, it is not easy for current techniques to distinguish them merely by statistical characteristics of traffic. Therefore, App-DDoS attacks may be stealthier and more dangerous for the popular Websites than the general Net-DDoS attacks when they mimic the normal flash crowd. This project proposes a scheme to capture the spatial-temporal patterns of a normal flash crowd event and to implement the App-DDoS attacks detection. Since the traffic characteristics of low layers are not enough to distinguish the App-DDoS attacks from the normal flash crowd event, the objective of this project is to find an effective method to identify whether the surge in traffic is caused by App-DDoS attackers or by normal Web surfers. This project defines the Access Matrix (AM) to capture spatial-temporal patterns of normal flash crowd and to monitor App-DDoS attacks during flash crowd event. Hidden semi-Markov model is used to describe the dynamics of AM and to achieve a numerical and automatic detection. Principal component analysis and independent component analysis used to deal with the multidimensional data for Hidden semi-Markov model and finally the monitoring architecture validate the real flash crowd traffic.
The following are the software tools are required to implement the system and tested using Unit testing applications.
Language : Java JDK 1.6
J2EE Technologies : Servlets, JSP
Application Server : Apache Tomcat 5.0
Operating System : Windows XP
Processor : Pentium IV 500MHz.
Monitor : SVGA
RAM : 128 MB SDRAM
Secondary Storage : 40GB HDD
Floppy Drive : 1.44MB
Any attack on the Internet today can be highly devastating. Distributed Denial of Service (DDoS) attacks are among the most malicious Internet attacks, that overwhelm a victim system with data such that the victim response time is slowed or totally stopped. There have been many instances where DDoS attacks have caused damages worth billions of dollars. Defending against DDoS attacks has hence become a major priority in the Internet community The attacker's objective is to interrupt or reduce the quality of experienced by legitimate users. Many attacks have innocent counterparts (e.g., someone sends me very large E-mail services as attachment, and blocks my access to other messages)
Flash crowd: It is a sudden, large surge in traffic to a particular Web site
Always on Time
Marked to Standard
Denial of Service (DoS): It is an explicit attempt to prevent legitimate users of a service from using that service
i) attackers have more bandwidth than victim, e.g. T3 (45Mpbs) attacks T1 (1.544 Mbps).
ii) attackers amplify their bandwidth engaging other computers to attack victim with higher bandwidth, e.g. 100 56Kbps attack a T1
Resource starvation: consumes system resources like CPU, memory, disk space on the victim machine using flooding
Smurf, Fraggle, Syn flood: Attacker sends sustained packets to broadcast address of the Simplifying network with source address is forged to read the victim's IP address. Since traffic was sent to broadcast address all hosts in the amplifying LAN will answer to the victim's IP address If a few SYN packets are sent by the attacker every 10 seconds, the victim will never clear the queue and stops to respond.
Hidden semi Markov Model:
We apply the hidden semi-Markov model (HSMM) to characterize legitimate request patterns to a Web server and to detect DDoS (distributed denial of service) attacks on it. Measurements of real workload often indicate that a significant amount of variability is present in the traffic observed over a wide range of time scales, exhibiting self similar or long range dependent characteristics Major advantages of using an HSMM are its efficiency in estimating the model parameters to account for an observed sequence, and the estimated parameters can capture various statistical properties of the workload, including self-similarity, long-range and short-range dependence. Therefore, use of this HSMM is effective in better understanding the nature of Web workload and in detecting the anomalous behavior that a DDoS attack may present.
At present most of the systems are vulnerable to Dos attack. DoS attacks are of particular interest and concern to the Internet community because they seek to render target systems inoperable and/or target networks inaccessible. "Traditional" DoS attacks, however, typically generate a large amount of traffic from a given host or subnet and it is possible for a site to detect such an attack in progress and defend themselves. Distributed DoS attacks are a much more nefarious extension of DoS attacks because they are designed as a coordinated attack from many sources simultaneously against one or more targets. There are some attack detection mechanisms as follows
Signature detection :
Signature detection (also known as misuse detection),where we look for patterns signaling well known attacks
Identifying something out of ordinary is essentially anomaly detection.
PHAD (packet header anomaly detector):
PHAD extends the four attributes normally used in network anomaly detection systems (source and destination IP address, source and destination port numbers). Transport headers (TCP, UDP) fields are tested as appropriate for each protocol. In testing, we discovered that many attacks could be detected because of unusual values in these fields. In addition to IP address anomalies, we found that some attacks generate unusually small packet sizes, unusual combinations of TCP flags (e.g. urgent data, missing acknowledgements, reserved flags).
ALAD (application layer anomaly detector):
Instead of modeling single packets, as in PHAD, we model incoming TCP connections to the well known server ports (0-1023).Although this misses a few attacks that exploit IP, UDP or higher numbered ports (such as X servers), it does (or should) catch most attacks against servers, which usually use TCP. The attackers will keep trying to establishing connections to servers by huge number of requests which will generate the flash crowd in
network and resource starvation.
Here each router marks packets with dynamic probability. Specifically, each router marks a packet with a probability proportional to the distance it has to travel. As such, a packet that has to traverse long distances is marked with higher probability, compared with a packet with shorter distances to traverse. This modification ensures that a packet is marked with much higher probability compared to existing mechanisms, which greatly reduces effectiveness of spoofed marks. It can reduce the number of false positives by 90%
1) All the legitimate packets would be marked at least once by an intermediate router before it reaches the destination (victim).
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2) There is an upper bound on the probability that a spoofed (illegitimate) packet reaches the destination without being marked. This upper bound is a function of the distance between the sender (attacker) and the destination (victim). The attackers will set TTL to high, but the spoofs will be find and reduce the TTL by routers based on distance to destination.
1. The Existing Attack detection mechanism uses only the concept of request rate of the particular user and flash crowd event in network.
2.Other existing defense methods may be those based on schemes.
Those schemes are not effective for the DDoS attack detection
They may annoy users and introduce additional service delays.
3 Though anomaly detection can detect novel attacks, it has the disadvantage that it is not capable of discerning intent. It can only signal that some event is unusual, but not necessarily hostile, thus generating false alarms
The goal of the proposed system is to add some new attack detection with addition of existing system. We proposed a attack detection
mechanism, a scheme ,based on document popularity using Access Matrix that will define the temporal patterns. Pattern indicates the website links that have some sequence of path. We used a sequence anomaly detector based on hidden semi-Markov model to detect the App-DDOS attacks.
The basic idea behind the proposed system is to isolate and protect legitimate traffic from huge volumes of DDoS traffic when an attack occurs.
Our first step is to distinguish packets that contain genuine source IP addresses from those that contain spoofed addresses. This is done by redirecting a client to a new IP address and port number (to receive web service) through a standard HTTP redirect message.
The proposed system uses some advanced detection technique with addition to existing technique to detect the App-DDOS attack.
The proposed system uses Access Matrix to maintain the access
sequence of every user.
The following are the modules obtained by the detailed design of the proposed system.
Hidden semi Markov Model
This module is to distinguish packets that contain genuine source IP addresses from those that contain spoofed address. Once the very first TCP SYN packet of a client gets through, the proposed system immediately redirects the client to a pseudo-IP address (still belonging to the website) and port number pair, through a standard HTTP URL redirect message. Certain bits from this IP address and the port number pair will serve as the Message Authentication code (MAC) for the client's IP address. MAC is a symmetric authentication scheme that allows a party A, which shares a secret key k with another party A, which shares a secret key k with another party B, to authenticate a message M sent to B with a signature MAC (M,k) has the property that, with overwhelming probability, no one can forge it without knowing the secret key k.
This module is to prevent attackers who are using genuine address or spoofed address. Since a legitimate client uses its real IP address to communicate with the server, it will receive the HTTP redirect message (hence the MAC). So, all its future packets will have the correct MAC inside their destination IP addresses and thus be protected. The DDos traffic with spoofed IP addresses, on the other hand, will be filtered because the attackers will not receive the MAC sent to them. So, this technique effectively separates legitimate traffic from DDos traffic with spoofed IP addresses.
Attacker Prevention (IP Handler Mechanism)
If the server find that the request rate from a IP is a higher than the limit, the IP will be moved to blocked state, and further the response will not be provided. Each time if a new request arrives, the server will get its IP and check whether this IP is in blocked state or Normal state.
If it is in blocked state the service will not be provided or else the request is handled and immediate response is given for the normal users.
The attackers will try to attack the popular websites by sending the queries on the URL path. If the queries are executed then some unexpected results will happen for websites. For example modify and delete queries will leads to more problems for popular sites. This module will check the URL path and redirect the request if it contains the unwanted queries.
Here in this Access Matrix module we will store the Online Shopping's list of sequence access path information in a separate table. Here the necessary information like user's id, IP address port number access time and the recent sequence of access path information is stored in another separate table for future reference.
Hidden semi-Markov model:
Here in this module we will check the client's sequence access path information with the access matrix table to identify the attacker. If the sequence of access path differs, we will update and name that ip address in separate table as attacker.
Hidden Semi Markov Model
Hidden Semi Markov model Sequence path checker