arXiv subjects

Gaurav Raina

Publications and source records attributed to Gaurav Raina.

At least 19 recordsLinked to original sources

arXiv2025

Design Considerations Based on Stability for a Class of TCP Algorithms

Transmission Control Protocol (TCP) continues to be the dominant transport protocol on the Internet. The stability of fluid models has been a key consideration in the design of TCP and the performance evaluation of TCP algorithms. Based on local stability analysis, we formulate some design considerations for a class of TCP algorithms. We begin with deriving sufficient conditions for the local stability of a generalized TCP algorithm in the presence of heterogeneous round-trip delays. Within this generalized model, we consider three specific variants of TCP: TCP Reno, Compound TCP, and Scalable TCP. The sufficient conditions we derive are scalable across network topologies with one, two, and many bottleneck links. We are interested in networks with intermediate and small drop-tail buffers as they offer smaller queuing delays. The small buffer regime is more attractive as the conditions for stability are decentralized. TCP algorithms that follow our design considerations can provide stable operation on any network topology, irrespective of the number of bottleneck links or delays in the network.

cs.NI↗

arXiv2024

A Survey on Offensive AI Within Cybersecurity

Artificial Intelligence (AI) has witnessed major growth and integration across various domains. As AI systems become increasingly prevalent, they also become targets for threat actors to manipulate their functionality for malicious purposes. This survey paper on offensive AI will comprehensively cover various aspects related to attacks against and using AI systems. It will delve into the impact of offensive AI practices on different domains, including consumer, enterprise, and public digital infrastructure. The paper will explore adversarial machine learning, attacks against AI models, infrastructure, and interfaces, along with offensive techniques like information gathering, social engineering, and weaponized AI. Additionally, it will discuss the consequences and implications of offensive AI, presenting case studies, insights, and avenues for further research.

cs.CR↗

arXiv2023

Synchronisation in TCP networks with Drop-Tail Queues

The design of transport protocols, embedded in end-systems, and the choice of buffer sizing strategies, within network routers, play an important role in performance analysis of the Internet. In this paper, we take a dynamical systems perspective on the interplay between fluid models for transport protocols and some router buffer sizing regimes. Among the flavours of TCP, we analyse Compound, as well as Reno and Illinois. The models for these TCP variants are coupled with a Drop-Tail policy, currently deployed in routers, in two limiting regimes: a small and an intermediate buffer regime. The topology we consider has two sets of long-lived TCP flows, each passing through separate edge routers, which merge at a common core router. Our analysis is inspired by time delayed coupled oscillators, where we obtain analytical conditions under which the sets of TCP flows synchronise. These conditions are made explicit in terms of coupling strengths, which depend on protocol parameters, and on network parameters like feedback delay, link capacity and buffer sizes. We find that variations in the coupling strengths can lead to limit cycles in the queue size. Packet-level simulations corroborate the analytical insights. For design, small Drop-Tail buffers are preferable over intermediate buffers as they can ensure both low latency and stable queues.

cs.NI↗

arXiv2021

Stability, convergence and bifurcation in some models of chemical kinetics

In this paper, we analyze the stability, convergence, and bifurcation properties of the Boissonade-De Kepper (BD) model which played a key role in the development of nonlinear chemical dynamics. We first outline conditions for local stability, which may help guide design considerations. Then, we show that the BD model undergoes a Hopf bifurcation when the stability condition gets violated. Using Poincaré normal forms and center manifold theory, we derive explicit analytic expressions for determining the type of the Hopf bifurcation and the stability of the limit cycles. This provides insights on the system dynamics just beyond the stable regime. Some of the analytical insights are corroborated with numerical computations. We also show that the mathematical results obtained in this paper may have wider applicability beyond the BD model.

math.DS↗

arXiv2019

A Framework for End-to-End Deep Learning-Based Anomaly Detection in Transportation Networks

We develop an end-to-end deep learning-based anomaly detection model for temporal data in transportation networks. The proposed EVT-LSTM model is derived from the popular LSTM (Long Short-Term Memory) network and adopts an objective function that is based on fundamental results from EVT (Extreme Value Theory). We compare the EVT-LSTM model with some established statistical, machine learning, and hybrid deep learning baselines. Experiments on seven diverse real-world data sets demonstrate the superior anomaly detection performance of our proposed model over the other models considered in the comparison study.

cs.LG↗

arXiv2019

LSTM-Based Anomaly Detection: Detection Rules from Extreme Value Theory

In this paper, we explore various statistical techniques for anomaly detection in conjunction with the popular Long Short-Term Memory (LSTM) deep learning model for transportation networks. We obtain the prediction errors from an LSTM model, and then apply three statistical models based on (i) the Gaussian distribution, (ii) Extreme Value Theory (EVT), and (iii) the Tukey's method. Using statistical tests and numerical studies, we find strong evidence against the widely employed Gaussian distribution based detection rule on the prediction errors. Next, motivated by fundamental results from Extreme Value Theory, we propose a detection technique that does not assume any parent distribution on the prediction errors. Through numerical experiments conducted on several real-world traffic data sets, we show that the EVT-based detection rule is superior to other detection rules, and is supported by statistical evidence.

cs.LG↗

arXiv2019

Compound TCP with Random Early Detection (RED): stability, bifurcation and performance analyses

The problem of increased queueing delays in the Internet motivates the study of currently implemented transport protocols and active queue management (AQM) policies. We study Compound TCP (default protocol in Windows) with Random Early Detection (RED). RED uses an exponentially weighted moving average of the queue size to make packet-dropping decisions, aiming to control the queue size. One must study RED with current protocols in order to explore its viability in the context of increased queueing delays. We derive a non-linear time-delayed model for Compound TCP-RED. We derive a sufficient condition for local stability of this model, and examine the impact of (i) round-trip time (RTT) of the TCP flows, (ii) queue averaging parameter and (iii) packet-dropping thresholds. Further, we establish that the system undergoes a Hopf bifurcation as any of the above parameters is varied. This suggests the emergence of limit cycles in the queue size, which may lead to synchronisation of TCP flows and loss of link utilisation. Next, we study a regime where queue size averaging is not performed, and packet-dropping decisions are based on instantaneous queue size. In this regime, we derive the necessary and sufficient condition for local stability. A comparison of the stability results for Compound TCP-RED in the two regimes--with and without queue size averaging--reveals that averaging may not be beneficial to system stability. Packet-level simulations show that the queue size indeed exhibits limit cycle oscillations as system parameters are varied. We then outline a simple threshold-based queue policy, that could ensure stable low-latency operation. We show that the threshold policy outperforms RED in terms of queueing delay, flow completion time and packet loss. We highlight that the threshold-based policy could mitigate the issue of increased queueing delays in the Internet.

cs.NI↗

arXiv2019

Impact of queue feedback on the stability and dynamics of a Rate Control Protocol (RCP) with two delays

Rate Control Protocol (RCP) uses feedback from routers to assign flows their fair rate. RCP estimates the fair rate using two forms of feedback: rate mismatch and queue size. An outstanding design question for RCP is whether the queue size feedback is useful or not. To address this, we analyze stability and the bifurcation properties of RCP in both the cases i.e., with and without queue size feedback. The model considers flows with two different round-trip times, operating over a single bottleneck link. By using an exogenous bifurcation parameter, we show that the system loses stability via a Hopf bifurcation and hence we can expect a limit cycle branching from the fixed point. We highlight that the presence of queue feedback can readily destabilize the system. Using Poincar{è} normal forms and the center manifold theorem, we show that the Hopf bifurcation is super-critical in the case of RCP without queue feedback. Whereas, in the presence of queue feedback, we show that the system can undergoes a sub-critical Hopf bifurcation for some parameter values. A sub-critical Hopf bifurcation can result in either large amplitude limit cycles or unstable limit cycles, and hence should be avoided in engineering applications. Thus, the presence of queue feedback would create adverse effects on the stability of the emerging limit cycles. In essence, the analytical results of RCP with two delays favor the design choice that uses feedback based only on rate mismatch. The theoretical analysis is validated with numerical computations and some packet level simulations as well.

cs.NI↗

arXiv2019

Do we need two forms of feedback in the Rate Control Protocol (RCP)?

There is considerable interest in the networking community in explicit congestion control as it may allow the design of a fair, stable, low loss, low delay, and high utilization network. The Rate Control Protocol (RCP) is an example of such a congestion control protocol. The current design of RCP suggests that it should employ two forms of feedback; i.e. rate mismatch and queue size, in order to manage its flow control algorithms. An outstanding design question in RCP is whether the presence of queue size feedback is useful or not, given feedback based on rate mismatch. In this paper, we address this question using tools from control and bifurcation theory. We linearize the actual non-linear system and analyze the local asymptotic stability, robust stability and rate of convergence of both the design choices, i.e., with and without queue size feedback. But such analyses do not offer clear design recommendations on whether the queue feedback is useful or not. This motivates a bifurcation-theoretic analysis where we have to take non-linear terms into consideration, which helps to learn additional dynamical properties of the RCP system. In particular, we proceed to analyze two non-linear properties, namely, the type of Hopf bifurcation and the asymptotic stability of the bifurcating limit cycles. Analytical results reveal that the presence of queue feedback in RCP can induce a sub-critical Hopf bifurcation, which can lead to undesirable system behavior. Whereas, in the absence of queue feedback, the Hopf bifurcation is always super-critical where the bifurcating limit cycles are stable and of small amplitude. The analysis is corroborated by numerical computations and some packet-level simulations as well. Based on our work, the suggestion for RCP is to only include feedback based on rate mismatch in the design of the protocol.

cs.NI↗

arXiv2019

Stability and non-linear dynamics of Dual congestion control schemes with two delays

In this paper, we analyze some local stability and local bifurcation properties of the Proportionally fair, TCP fair, and the Delay-based dual algorithms in the presence of two distinct time delays. In particular, our focus is on the interplay between different notions of fairness, stability, and bifurcation theoretic properties. Different notions of fairness give rise to different non-linear models for the class of Dual algorithms. One can devise conditions for local stability, for each of these models, but such conditions do not offer clear design recommendations on which fairness criteria is desirable. With a bifurcation-theoretic analysis, we have to take non-linear terms into consideration, which helps to learn additional dynamical properties of the various systems. In the case of TCP fair and Delay dual algorithms, with two delays, we present evidence that they can undergo a sub-critical Hopf bifurcation, which has not been previously revealed through analysis of the single delay variants of these algorithms. A sub-critical Hopf bifurcation can result in either large amplitude limit cycles or unstable limit cycles, and hence should be avoided in engineering applications. In the case of the Proportionally fair algorithm, we provide strong evidence to suggest that all one should expect is the occurrence of a super-critical Hopf bifurcation, which leads to stable limit cycles with small amplitude. Thus, from a design perspective, our analysis favors the use of Proportional fairness in the class of dual congestion control algorithms. To best of our knowledge, this is the first study that presents evidence to suggest that fluid models representing Internet congestion control algorithms may undergo a sub-critical Hopf bifurcation.

nlin.CD↗

arXiv2019

Effect of two forms of feedback on the performance of the Rate Control Protocol (RCP)

The Rate Control Protocol (RCP) uses explicit feedback from routers to control network congestion. RCP estimates it's fair rate from two forms of feedback: rate mismatch and queue size. An important design question that remains open in RCP is whether the presence of queue size feedback is helpful, given the presence of feedback from rate mismatch. The feedback from routers to end-systems is time delayed, and may introduce instabilities and complex non-linear dynamics. Delay dynamical systems are often modeled using delay differential equations to facilitate a mathematical analysis of their performance and dynamics. The RCP models with and without queue size feedback give rise to two distinct non-linear delay differential equations. Earlier work on this design question was based on methods of linear systems theory. For further progress, it is quite natural to employ nonlinear techniques. In this study, we approach this design question using tools from control and bifurcation theory. The analytical results reveal that the removal of queue feedback could enhance both stability and convergence properties. Further, using Poincaré normal forms and center manifold theory, we investigate two nonlinear properties, namely, the type of Hopf bifurcation and the asymptotic stability of the bifurcating limit cycles. We show that the presence of queue feedback in the RCP can lead to a sub-critical Hopf bifurcation, which would give rise either to the onset of large amplitude limit cycles or to unstable limit cycles. Whereas, in the absence of queue feedback, the Hopf bifurcation is always super-critical and the bifurcating limit cycles are stable. The analysis is complemented with computations and some packet-level simulations as well. In terms of design, our study suggests that the presence of both forms of feedback may be detrimental to the performance of RCP.

cs.NI↗

arXiv2019

Grids versus Graphs: Partitioning Space for Improved Taxi Demand-Supply Forecasts

Accurate taxi demand-supply forecasting is a challenging application of ITS (Intelligent Transportation Systems), due to the complex spatial and temporal patterns. We investigate the impact of different spatial partitioning techniques on the prediction performance of an LSTM (Long Short-Term Memory) network, in the context of taxi demand-supply forecasting. We consider two tessellation schemes: (i) the variable-sized Voronoi tessellation, and (ii) the fixed-sized Geohash tessellation. While the widely employed ConvLSTM (Convolutional LSTM) can model fixed-sized Geohash partitions, the standard convolutional filters cannot be applied on the variable-sized Voronoi partitions. To explore the Voronoi tessellation scheme, we propose the use of GraphLSTM (Graph-based LSTM), by representing the Voronoi spatial partitions as nodes on an arbitrarily structured graph. The GraphLSTM offers competitive performance against ConvLSTM, at lower computational complexity, across three real-world large-scale taxi demand-supply data sets, with different performance metrics. To ensure superior performance across diverse settings, a HEDGE based ensemble learning algorithm is applied over the ConvLSTM and the GraphLSTM networks.

cs.LG↗

arXiv2018

Taxi Demand-Supply Forecasting: Impact of Spatial Partitioning on the Performance of Neural Networks

In this paper, we investigate the significance of choosing an appropriate tessellation strategy for a spatio-temporal taxi demand-supply modeling framework. Our study compares (i) the variable-sized polygon based Voronoi tessellation, and (ii) the fixed-sized grid based Geohash tessellation, using taxi demand-supply GPS data for the cities of Bengaluru, India and New York, USA. Long Short-Term Memory (LSTM) networks are used for modeling and incorporating information from spatial neighbors into the model. We find that the LSTM model based on input features extracted from a variable-sized polygon tessellation yields superior performance over the LSTM model based on fixed-sized grid tessellation. Our study highlights the need to explore multiple spatial partitioning techniques for improving the prediction performance in neural network models.

cs.LG↗

arXiv2018

Stability, convergence, and limit cycles in some human physiological processes

Mathematical models for physiological processes aid qualitative understanding of the impact of various parameters on the underlying process. We analyse two such models for human physiological processes: the Mackey-Glass and the Lasota equations, which model the change in the concentration of blood cells in the human body. We first study the local stability of these models, and derive bounds on various model parameters and the feedback delay for the concentration to equilibrate. We then deduce conditions for non-oscillatory convergence of the solutions, which could ensure that the blood cell concentration does not oscillate. Further, we define the convergence characteristics of the solutions which govern the rate at which the concentration equilibrates when the system is stable. Owing to the possibility that physiological parameters can seldom be estimated precisely, we also derive bounds for robust stability\textemdash which enable one to ensure that the blood cell concentration equilibrates despite parametric uncertainty. We also highlight that when the necessary and sufficient condition for local stability is violated, the system transits into instability via a Hopf bifurcation, leading to limit cycles in the blood cell concentration. We then outline a framework to characterise the type of the Hopf bifurcation and determine the asymptotic orbital stability of limit cycles. The analysis is complemented with numerical examples, stability charts and bifurcation diagrams. The insights into the dynamical properties of the mathematical models may serve to guide the study of dynamical diseases.

eess.SY↗

arXiv2018

Global stability of the Rate Control Protocol (RCP) and some implications for protocol design

The Rate Control Protocol (RCP) is a congestion control protocol that relies on explicit feedback from routers. RCP estimates the flow rate using two forms of feedback: rate mismatch and queue size. However, it remains an open design question whether queue size feedback in RCP is useful, given the presence of rate mismatch. The model we consider has RCP flows operating over a single bottleneck, with heterogeneous time delays. We first derive a sufficient condition for global stability, and then highlight how this condition favors the design choice of having only rate mismatch in the protocol definition.

eess.SY↗

arXiv2018

Taxi demand forecasting: A HEDGE based tessellation strategy for improved accuracy

A key problem in location-based modeling and forecasting lies in identifying suitable spatial and temporal resolutions. In particular, judicious spatial partitioning can play a significant role in enhancing the performance of location-based forecasting models. In this work, we investigate two widely used tessellation strategies for partitioning city space, in the context of real-time taxi demand forecasting. Our study compares (i) Geohash tessellation, and (ii) Voronoi tessellation, using two distinct taxi demand datasets, over multiple time scales. For the purpose of comparison, we employ classical time-series tools to model the spatio-temporal demand. Our study finds that the performance of each tessellation strategy is highly dependent on the city geography, spatial distribution of the data, and the time of the day, and that neither strategy is found to perform optimally across the forecast horizon. We propose a hybrid tessellation algorithm that picks the best tessellation strategy at each instant, based on their performance in the recent past. Our hybrid algorithm is a non-stationary variant of the well-known HEDGE algorithm for choosing the best advice from multiple experts. We show that the hybrid tessellation strategy performs consistently better than either of the two strategies across the data sets considered, at multiple time scales, and with different performance metrics. We achieve an average accuracy of above 80% per km^2 for both data sets considered at 60 minute aggregation levels.

cs.LG↗

arXiv2018

Impact of delayed acceleration feedback on the classical car-following model

Delayed feedback plays a vital role in determining the qualitative dynamical properties of a platoon of vehicles driving on a straight road. Motivated by the positive impact of Delayed Acceleration Feedback (DAF) in various scenarios, in this paper, we incorporate DAF into the Classical Car-Following Model (CCFM). We begin by deriving the Classical Car-Following Model with Delayed Acceleration Feedback (CCFM-DAF). We then derive the necessary and sufficient condition for local stability of the CCFM-DAF. Next, we show that the CCFM-DAF transits from the locally stable to the unstable regime via a Hopf bifurcation; thus leading to the emergence of limit cycles in system dynamics. We then propose a suitable linear transformation that enables us to analyze the local bifurcation properties of the CCFM-DAF by studying the analogous properties of the CCFM. We also study the impact of DAF on three important dynamical properties of the CCFM; namely, non-oscillatory convergence, string stability and robust stability. Our analyses are complemented with a stability chart and a bifurcation diagram. Our work reveals the following detrimental effects of DAF on the CCFM: (i) reduction in the locally stable region, (ii) increase in the frequency of the emergent limit cycles, (iii) decrease in the amplitude of the emergent limit cycles, (iv) destruction of the non-oscillatory property, (vi) increased risk of string instability, and (vii) reduced resilience towards parametric uncertainty. Thus, we report a practically-relevant application wherein DAF degrades the performance in several metrics of interest.

eess.SY↗

arXiv2017

Congestion costs incurred on Indian Roads: A case study for New Delhi

We conduct a preliminary investigation into the levels of congestion in New Delhi, motivated by concerns due to rapidly growing vehicular congestion in Indian cities. First, we provide statistical evidence for the rising congestion levels on the roads of New Delhi from taxi GPS traces. Then, we estimate the economic costs of congestion in New Delhi. In particular, we estimate the marginal and the total costs of congestion. In calculating the marginal costs, we consider the following factors: (i) productivity loss, (ii) air pollution costs, and (iii) costs due to accidents. In calculating the total costs, in addition to the above factors, we also estimate the costs due to the wastage of fuel. We also project the associated costs due to productivity loss and air pollution till 2030. The projected traffic congestion costs for New Delhi comes around 14658 million US$/yr for the year 2030. The key takeaway from our current study is that costs due to productivity loss, particularly from buses, dominates the overall economic costs. Additionally, the expected increase in fuel wastage makes a strong case for intelligent traffic management systems.

physics.soc-ph↗