Entropy-Based Collaborative Detection of DDoS Attacks on Community Networks

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1 Entropy-Based Collaborative Detection of DDoS Attacks on Community Networks Krishnamoorthy.D 1, Dr.S.Thirunirai Senthil, Ph.D 2 1 PG student of M.Tech Computer Science and Engineering, PRIST University, Puducherry 2 Professor, Department of Computer Science and Engineering, PRIST University, Puducherry Abstract- In Networking attack becoming harmful and tremendous in field of hacking. Distributed Denial- of- Service (DDoS) attacks are a critical threat to the Internet. It is extremely hard to trace back the attackers because of memory less feature of the internet routing mechanisms. As a result, there is no effective and efficient method to deal with this issue.in this paper, traces back of the attackers a efficiently identified and also to protect the data from the attackers using entropy variations. In the existing system, some approaches have been suggested to identify the attackers such as probabilistic Packet Marking (PPM), Deterministic Packet Marking (DPM) This method is used to identify the attackers efficiently and supports a large scalability. In the proposing system, this method is applied to block the attackers in a wide area of network which was much efficient and protect the data from the attackers. Keywords- DDOS, IP trace back, Entropy Variation, Flows Introduction It is one of the extraordinary challenge to track back the DDOS attacks, that attackers generate huge amount of requests to victims through compromised computers(zombies), in order to denying normal services or degrading the quality of services. Recent survey shows that than 70 internet operators in the world demonstrated that DDOS attack are increasing dramatically and individual attacks are more strong and sophisticated. IP trace back means the capability of identifying the actual source of any packet across the internet; with the help of IP trace back schemes identify the zombies from which the DDOS attack packets entered the internet. IP trace back using information theoretical parameters, and there is no packet marking in the proposed strategy; we, therefore, can avoid the inherited shortcomings of the packet marking mechanisms. We categorize packets that are passing through a router into flows, which are defined by the upstream router where a packet came from, and the destination address of the packet. During no attack periods, routers are required to observe and record entropy variations of local flows. In this paper, we use flow entropy variation or entropy variation interchangeably. Once a DDoS attack has been identified, the victim initiates the following pushback process to identify the locations of zombies: the victim first identifies which of its upstream routers are in the attack tree based on the flow entropy variations it has accumulated, and then submits requests to the related immediate upstream routers. We categorize the packets that are passing through a router into flows. A flow is defined by a pair the upstream router where the packet came from and the destination address of the packet. Entropy is an information theoretic concept, which is a measure of randomness. We employ R S. Publication (rspublication.com), Page 138

2 entropy variation in this paper to measure changes of randomness of flows at a router for a given time interval. We notice that entropy variation is only one of the possible metrics. Figure 1 : DDOS Attacks via Entropy Variations. Figure 1 shows the data replication that uses local server and remote server, where replication is copy of data from local server to remote server. In this paper, we propose distributed data replication techniques to find query delay, data availability and data collision problems. We propose distributed data replication techniques to balance the smooth tradeoffs between data availability and query delay. Simulation results show that the proposed method can achieve a balance among these metrics. The rest of the paper is organized as follows: The section 2, preliminaries of data replication. In Section 3, we describe the proposed data replication schemes in detail. Section 4 evaluates the Performance analyzes and simulation result and Section 5, we concludes the paper. I. PRELIMINARIES Greedy method use minor data item which needs less memory size; by replicating them we can save memory size for other data items. This method uses the following function AFi (k) = (aik/sk). Every node assign the data item in increasing order of AFi, no other data can be replicated by memory. In this method data are replicated more frequently that are accessed locally. It did not consider the collaboration between other neighboring nodes. One-To-One Optimization (OTOO) method uses every mobile node which only collaborates with neighbor node to choose what data to be hosted. Every node Ni computes the Combined Access Frequency value of Ni and Nj to data item Dk at Ni, denoted as CAF1ij (k). CAF1ij (k) = (aik + ajk (1 fij)) / si, it assigns the data item in increasing order of CAF1 no other data item can be replicated. This method progress the data accessibility, it might occur that node Ni should crowd Dj the node that crowd Dj is not available to Ni because of network separation. Reliable (R) method used for mobile node, if the accessing links between nodes are equal more collaboration with the node can progress the data accessibility. In others, every mobile node supply the division of its memory to grasp data items for other dependable nodes, i.e. the link failure possibility is less than given threshold value. R S. Publication (rspublication.com), Page 139

3 II. PROPOSED WORK In wireless ad hoc both proactive and reactive routing networks will support multimedia application. The essential for mobility prediction is of excessive consequence. Node mobility partitions the network. In generally, mobile networks having limited transmission range. If a mobile node move out of range, it can t be provide any services afterward. If the node flexibility is calculated into higher order value, then data can be replicated in a suitable or proper node to improve data accessibility. If the nodes are connected symmetrically, but the network is not partitioned, then every mobile node often sends sync message to their neighbor and the distance is valued. Boundary value defines which is greater than the least transmission range and less than the extreme transmission range between two nodes U and V. if node V move away from the transmission region R, then it can t able to communicate with one another. The second major issue is power consumption. Mobile nodes require more power to access data items to other nodes. The node must know about their own context information. I.e. source, neighboring node and destination require sending and receiving the data. In the future every node must contain information about its neighboring node, data item, location and distance etc. This information must provide effective way to increases data availability. Algorithm: DDOS (Distributed Denial of Services) Total flow = 21;.for (i=1; i<=total flow; i++) { f[i] = 0;} Observe the traffic at each router Time during which packet loss occurred is noted ( s) Packet loss period = 30 50s Traffic is measured during the packet loss period Traffic = 16489packets Maximum traffic is selected as threshold for attack detection Threshold = For (i=1; i<=total flow; i++) { If (f [i] > Threshold) Attacker flow ID = I } Find source of i 15. AttackerID = source (i) III. PERFORMANCE ANALYSIS AND SIMULATION RESULT DDoS attack and Flash crowd can be identified on the basis of total traffic of each flow. If maximum traffic is considered as the threshold value, then if it is greater than the R S. Publication (rspublication.com), Page 140

4 entropy H(X), then the victim starts the IP Trace back algorithm. Figure 2 : DDOS Attacks Vs No. of Data Request Figure 3 : DDOS Attacks Vs No. of Data Request CONCLUSION This paper employs by storing the information of flow entropy variations at routers. Once the DDOS attack has been identified it performs pushback tracing procedure. The Trace back algorithm first identifies its upstream router where the attack flows comes from and then submits the Trackback request to the related upstream router. This procedure continues until the most far away zombies are identified. But in my existing case I used the static value to determine to determine the entropy rate. But in my proposed strategies I used dynamic value to determine the entropy rate which is based upon the packet size of the client s behavior. REFERENCES [1] K. Park and H. Lee, On the Effectiveness of Probabilistic Packet Marking for IP Traceback under Denial of Service Attack, Proc. IEEE INFOCOM. [2] S.Yu and W.Zhou, Entropy-Based Collaborative Detection of DDoS Attacks on Community Networks, Proc. Sixth Ann. R S. Publication (rspublication.com), Page 141

5 IEEEInt l Conf. Pervasive Computing and Comm., pp , [3] Hoai-Vu Nguyen and Yongsun Choi, Proactive Detection of DDoS Attacks Utilizing k- NN Classifier in an Anti-DDos Framework proc. IEEEINFOCOM [4] Anusha. J, Entropy Based Detection of DDOS Attacks IJSEC [5] G. Jin and J. Yang, Deterministic Packet Marking Based on Redundant Decomposition for IP raceback, IEEE Comm. Letters,vol.10, no. 3, pp , Mar [6] K. Lu et al., Robust and Efficient Detection of DDoS Attacks for Large-Scale Internet, Computer Networks, vol. 51, no. 9, pp , [7] R. Chen, J. Park, and R. Marchany, A Divide-and-Conquer Strategy for Thwarting Distributed Denial-of-Service Attacks, IEEE Trans. Parallel and Distributed Systems, vol. 18, no. 5, pp , May [8] IP Flow-Based Technology, ArborNetworks, [9] A. Belenky and N. Ansari, IP Traceback with Deterministic Packet Marking, IEEE Comm. Letters, vol. 7, no. 4, pp [10] M.T. Goodrich, Probabilistic Packet Marking for Large-Scale IP Traceback, IEEE/ACM Trans. Networking. R S. Publication (rspublication.com), Page 142

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