K. Haribabu

Adaptive lookup for unstructured peer-to-peer overlays

Scalability and efficient global search in unstructured peer-to-peer overlays have been extensively studied in the literature. The global search comes at the expense of local interactions between peers. Most of the unstructured peer-to-peer overlays do not provide any performance guarantee. In this work we propose a novel Quality of Service enabled lookup for unstructured peer-to-peer overlays that will allow the userpsilas query to traverse only those overlay links which satisfy the given constraints. Additionally, it also improves the scalability by judiciously using the overlay resources. Our approach selectively forwards the queries using QoS metrics like latency, bandwidth, and overlay link status so as to ensure improved performance in a scenario where the degree of peer joins and leaves are high. User is given only those results which can be downloaded with the given constraints. Also, the protocol aims at minimizing the message overhead over the overlay network.

Detecting Sybils in Peer-to-Peer Overlays Using Neural Networks and CAPTCHAs

Over the years, peer-to-peer networks have emerged as one of the most popular file sharing medium over The Internet, capable of providing user anonymity to the clients if desired. However, modern P2P networks suffer from the bane of malicious entities we refer to as Sybils, which forge multiple identities to negatively influence or even control the entire network. This paper suggests a novel solution to eradicate the Sybil threat using a unique combination of neural networks and CAPTCHA. We capture common behavioral patterns of participating Sybil entities, in terms of certain quantitative variables, and ascertain their true identities by feeding these variables to a neural network, followed by sending CAPTCHA to the alleged entity ensuring a very high success rate in identifying malicious entities in the network. Network simulations have shown the proposed approach to be highly effective in countering the Sybil threat by giving a high degree of accuracy in detecting the malicious nodes.

A survey: Hybrid SDN

Abstract A full deployment of Software Defined Networking (SDN) paradigm poses multi-dimensional challenges viz., technical, financial and business challenges. Technical challenges of scalability, fault tolerance, centralization guarantees exist. Financial challenges of budget constraints, non-availability of phased transition model exist. Business challenges like acceptability, building confidence among network operators etc. exist. Therefore, a direct and sudden transition from legacy networks to pure SDN seems unlikely. A hybrid deployment of SDN can be one of the plausible intermediate paths, primarily because it provides an environment where both legacy and SDN nodes can work together. Thus, an incremental deployment strategy can be developed. Further, hybrid SDN can enforce the benefits of both the traditional networks and SDN paradigm. Hybrid SDN deployment has many advantages including adaptability to budget constraints, central programmability of the network, fallback to time-tested legacy mechanisms and so on. But there are challenges specific to hybrid models, like added complexity of running multiple paradigms together, realizing cooperation between control planes, etc. We envision that more research work is needed to maximize the benefits and limit the drawbacks. In this paper, we present a comprehensive survey of hybrid SDN models, techniques, inter-paradigm coexistence and interaction mechanisms. Firstly, we delineate an overview of hybrid SDN roots and consequently we discuss the definition, architectural pillars, benefits and limitations of hybrid SDN. Further, we categorize the different models under various headings, that can be used for deploying hybrid SDN. Next, we do a comparative analysis of each model. We discuss implementation approaches in each model and challenges that may arise in the deployment of hybrid SDN.

GAUR: a method to detect Sybil groups in peer-to-peer overlays

Sybil attack is an important problem as the peer-to-peer networks grow in size and become prominent means for distributing multimedia. In order to validate the feasibility of using psychometric tests as an approach to detect Sybils in their entirety as Sybil groups, an experiment is conducted by taking the tests on a population of considerable size. Selected people were given multiple questionnaires corresponding to multiple identities in the network. The survey data is analysed using DBSCAN clustering algorithm using several metrics. The results show that 75% of the Sybil groups were detected with 67% completeness.

Enhanced search in peer-to-peer networks using fuzzy logic

The efficiency of a Peer-to-Peer file sharing overlay is dependent on the lookup procedure. Huge size of peer-to-peer networks demands a scalable efficient lookup algorithm. In this paper we look at fuzzy logic approach in which we assign probabilities to each node based on the content it has. Lookup is guided by the probabilities. The results show that this algorithm is much better than standard lookup algorithms.

Detecting Sybils in Peer-to-Peer File Replication Systems

The test of a peer-to-peer file sharing network is how efficiently the objects are discovered and retrieved. One of the most important factors that contribute towards this is optimal replication of the objects across the network. One of the security threats to replication model is Sybil attack. In this paper we propose an approach that aims at detecting sybil identities in peer-to-peer file sharing networks. The sybils can corrupt, hide or destroy the replicas in file sharing network. This approach makes use of the fact that sybil doesn’t scale its storage to the factor of its identities. The approach safeguards the availability and accessibility of objects in a peer-to-peer network from sybil attack. Experimental evaluations have shown that our approach works very efficiently in detecting sybils. More than 50% of the sybils were detected in first few seconds of the simulation and loss or damage of objects is reduced to less than .0001%.

Real-time monitoring of network latency in Software Defined Networks

Latency in a network is an important parameter that can be utilized by Service providers and end users alike. Delay on a network path is often measured using end-to-end probing packets. When multiple end systems measure end-to-end latency, there are overlaps in their paths. Since end systems do not have this knowledge, it results in redundant work and network overhead. In this paper, we propose a method to measure end-to-end path latency in Software Defined Networks (SDN). This method avoids redundant work and measures latency in real-time. Our proposal is an improvement over the looping technique. We simplified the looping technique by using IP TTL as a counter. In order to avoid duplicate work, latency is measured per link and stored in the controller. End systems may register their flow labels with the SDN controller to receive latency information. For each registered flow, controller composes individual link latencies on that path to compute end-to-end latency. We also propose another approach to measure latency using queue lengths at network switches. This technique removes network overhead. In our simulations, improved looping technique is found to be giving better results with reduced computational and network overhead, while the proposed queue length technique shows comparable results.

Detecting Sybils in Peer-to-Peer Overlays Using Psychometric Analysis Methods

Peer to peer networks are fast becoming the most popular file sharing media, guaranteeing complete user anonymity to the clients. However, modern P2P networks suffer from Sybil attacks, which forge multiple identities to influence the global decisions in the network. This paper suggests a novel solution to minimize Sybils influence using unique combination of Psychometric Tests, Color Tests & CAPTCHAs. Our survey has shown that the proposed approach is promising in detecting not just Sybils but Sybil groups. The results have shown that 30-50% of Sybil groups are detected.

Indexing through Querying in Unstructured Peer-to-Peer Overlay Networks

The efficiency of a Peer-to-Peer file sharing overlay is measured in terms of the scalability and versatility of its object lookup strategy. In these networks peers carry out distributed query relaying to discover the service providers. Existing lookup mechanisms like flooding and random walks in unstructured P2P overlays create huge communication overhead and increased response time. In this work we propose efficient lookup in unstructured peer-to-peer overlay networks using indexing through querying, distributing indices through queries. Our simulation studies show that by our approach more than 97% of the queries are answered in one hop and the rest in few hops thus reducing the network load. Our approach is efficient in worst case scenarios where contents are distributed over thousands of peers and the overlay network condition is highly dynamic.

Meticulous Measurement of Control Packets in SDN

The data packet statistics sent by OpenFlow compliant switches cumulatively includes statistics about control traffic which is used for network control and management. This reduces the accuracy of calculation of QoS metrics and thus hampers network monitoring. We present here a novel algorithm to accurately measure the fraction of control packets in SDN within 3% error rate.