The traffic behavior of University of Louisville network with the interconnected backbone routers and the number of Virtual Local Area Network (VLAN) subnets is investigated using the Random Matrix Theory (RMT) approach. We employ the system of equal interval time series of traffic counts at all router to router and router to subnet connections as a representation of the inter-VLAN traffic. The cross-correlation matrix C of the traffic rate changes between different traffic time series is calculated and tested against null-hypothesis of random interactions. The majority of the eigenvalues i of matrix C fall within the bounds predicted by the RMT for the eigenvalues of random correlation matrices. The distribution of eigenvalues and eigenvectors outside of the RMT bounds displays prominent and systematic deviations from the RMT predictions. Moreover, these deviations are stable in time. The method we use provides a unique possibility to accomplish three concurrent tasks of traffic analysis. The method verifies the uncongested state of the network, by establishing the profile of random interactions. It recognizes the system-specific large-scale interactions, by establishing the profile of stable in time non-random interactions. Finally, by looking into the eigenstatistics we are able to detect and allocate anomalies of network traffic interactions.
Hidden Markov model has been proposed to model the distribution of network-wide traffic in @cite_23 . The observation window is used to distinguish denial of service (DoS) flooding attack mixed with the normal background traffic.
@cite_23
2004469928
Hidden semi-Markov Model (HsMM) has been well studied and widely applied to many areas. The advantage of using an HsMM is its efficient forward-backward algorithm for estimating model parameters to best account for an observed sequence. In this paper, we propose an HsMM to model the distribution of network-wide traffic and use an observation window to distinguish DoS flooding attacks mixed within the normal background traffic. Several experiments are conducted to validate our method. Internet users are increases, distributed denial of service (DDoS) attack present a very serious threat to the stability of the internet. The DDoS attack, which is consuming all of the computing or communication resources necessary for the service, is known very difficult to protect. The threat posed by network attacks on large network, such as the internet, demands effective detection method. Therefore, an intrusion detection system on large network is need to efficient real-time detection. In this paper, we propose the entropy-based detection mechanism against DDoS attacks in order to guarantee the transmission of normal traffic and prevent the flood of abnormal traffic. The OPNET simulation results show that our ideas can provide enough services in DDoS attack. Denial of service attacks, viruses and worms are common tools for malicious adversarial behaviour in networks. We propose the use of our autonomic routing protocol, the cognitive packet network (CPN), as a means to defend nodes from distributed denial of service (DDoS) attacks, where one or more attackers generate flooding traffic from multiple sources towards selected nodes or IP addresses. We use both analytical and simulation modelling, and experiments on our CPN testbed, to evaluate the advantages and disadvantages of our approach in the presence of imperfect detection of DDoS attacks, and of false alarms. We describe a simple but effective traffic model that can be used to understand the effects of denial-of-service (DoS) attacks based on query floods in Gnutella networks. We run simulations based on the model to analyze how different choices of network topology and application level load balancing policies can minimize the effect of these types of DoS attacks. In addition, we also study how damage caused by query floods is distributed throughout the network, and how application-level policies can localize the damage.