Pratik Narang

Game-theoretic strategies for IDS deployment in peer-to-peer networks

This work studies the problem of optimal positioning of Intrusion Detection Systems (IDSs) in a Peer-to-Peer (P2P) environment involving a number of peers and super-peers. This scenario applies to network architectures like that of Gnutella, Skype or Tor, which involve a huge number of leaf-peers and a selected number of super-peers who have higher responsibilities in the network. A malicious entity may become part of the P2P network by joining from any part of the network. It can attack a super-peer and thus disrupt the functioning of the P2P network. Peers may try to secure the network by running IDSs at certain strategically-chosen locations in the network. But a deterministic schedule of running and positioning the IDSs can be observed and thwarted by an adversary. In this paper, we explore the problem of strategically positioning IDSs in a P2P network with a randomized, game-theoretic approach. Our approach distributes the responsibility of running the IDSs between the peers in a randomized fashion and minimizes the probability of a successful attack.

Noise-resistant mechanisms for the detection of stealthy peer-to-peer botnets

Abstract The problem of detection of malicious network traffic is adversarial in nature. Accurate detection of stealthy Peer-to-Peer botnets is an ongoing research problem. Past research on detection of P2P botnets has frequently used machine learning algorithms to build detection models. However, most prior work lacks the evaluation of such detection models in the presence of deliberate injection of noise by an adversary. Furthermore, detection of P2P botnets in the presence of benign P2P traffic has received little attention from the research community. This work proposes a novel approach for the detection of stealthy P2P botnets (in presence of benign P2P traffic) using conversation-based mechanisms and new features based on Fourier transforms and information entropy. We use real-world botnet data to compare the performance of our features with traditional ‘flow-based’ features employed by past research, and demonstrate that our approach is more resilient towards the injection of noise in the communication patterns by an adversary. We build detection models with multiple supervised machine learning algorithms. With our approach, we could detect P2P botnet traffic in the presence of injected noise with True Positive rate as high as 90%.

Temperature compensation of ISFET based pH sensor using artificial neural networks

This paper presents a new Machine Learning based temperature compensation technique for Ion-Sensitive Field-Effect Transistor (ISFET). The circuit models for various electronic devices like MOSFET are available in commercial Technology Computer Aided Design (TCAD) tools such as LT-SPICE but no built-in model exists for ISFET. Considering SiO2 as the sensing film, an ISFET circuit model was created in LT-SPICE and simulations were carried out to obtain characteristic curves for SiO2 based ISFET. A Machine Learning (ML) model was trained using the data collected from the simulations performed using the ISFET macromodel in the read-out circuitry. The simulations were performed at various temperatures and the temperature drift behavior of ISFET was fed into the ML model. Constant pH (predicted by the system) curves were obtained when the device is tested for various pH (7 and 10) solutions at different ambient temperatures.

Downlink power control for latency aware grid energy savings in green cellular networks

Mobile service providers can achieve cost savings by deploying Base Stations (BSs) which harvest renewable energy as they reduce the energy drawn from the grid and its associated cost. The cost savings can be further enhanced by careful management of the system resources. Furthermore, mobile operators require that such resource management be carefully coupled with managing the quality of service (QoS) to ensure customer satisfaction. This process involves trade-off between energy drawn from the grid and the QoS performance. In contrast to prior research which has addressed the problem of joint management of grid energy savings and the QoS performance using user-association reconfiguration or BS on/off schemes, we present a framework for doing so using BS downlink power control. Our proposed framework is evaluated through simulations using a real BS deployment from London, UK, and we show its superior performance over existing benchmarks. We demonstrate that our framework can lead to around 40% grid energy savings with better network latency performance as compared to the traditionally used scheme.

Unwanted Traffic Identification in Large-Scale University Networks: A Case Study

To mitigate the malicious impact of P2P traffic on University networks, in this article the authors have proposed the design of payload-oblivious privacy-preserving P2P traffic detectors. The proposed detectors do not rely on payload signatures, and hence, are resilient to P2P client and protocol changes—a phenomenon which is now becoming increasingly frequent with newer, more popular P2P clients/protocols. The article also discusses newer designs to accurately distinguish P2P botnets from benign P2P applications. The datasets gathered from the testbed and other sources range from Gigabytes to Terabytes containing both unstructured and structured data assimilated through running of various applications within the University network. The approaches proposed in this article describe novel ways to handle large amounts of data that is collected at unprecedented scale in authors’ University network.