Browsing by Author "Adiga, Abhijin"

Now showing 1 - 3 of 3
  • Thumbnail Image
    Containing Cascading Failures in Networks: Applications to Epidemics and Cybersecurity
    Saha, Sudip (Virginia Tech, 2016-10-05)
    Many real word networks exhibit cascading phenomena, e.g., disease outbreaks in social contact networks, malware propagation in computer networks, failures in cyber-physical systems such as power grids. As they grow in size and complexity, their security becomes increasingly important. In this thesis, we address the problems of controlling cascading failures in various network settings. We address the cascading phenomena which are either natural (e.g., disease outbreaks) or malicious (e.g., cyber attacks). We consider the nodes of a network as being individually or collectively controlled by self-interested autonomous agents and study their strategic decisions in the presence of these failure cascades. There are many models of cascading failures which specify how a node would fail when some neighbors have failed, such as: (i) epidemic spread models in which the cascading can be viewed as a natural and stochastic process and (ii) cyber attack models where the cascade is driven by malicious intents. We present our analyses and algorithms for these models in two parts. Part I focuses on problems of controlling epidemic spread. Epidemic outbreaks are generally modeled as stochastic diffusion processes. In particular, we consider the SIS model on networks. There exist heuristic centralized approaches in the literature for containing epidemic spread in SIS/SIR models; however no rigorous performance bounds are known for these approaches. We develop algorithms with provable approximation guarantees that involve either protective intervention (e.g., vaccination) or link removal (e.g., unfriending). Our approach relies on the characterization of the SIS model in terms of the spectral radius of the network. The centralized approaches, however, are sometimes not feasible in practice. For example, targeted vaccination is often not feasible because of limited compliance to directives. This issue has been addressed in the literature by formulating game theoretic models for the containment of epidemic spread. However they generally assume simplistic propagation models or homogeneous network structures. We develop novel game formulations which rely on the spectral characterization of the SIS model. In these formulations, the failures start from a random set of nodes and propagate through the network links. Each node acts as a self-interested agent and makes strategic intervention decisions (e.g., taking vaccination). Each agent decides its strategy to optimize its payoff (modeled by some payoff function). We analyze the complexity of finding Nash equilibria (NE) and study the structure of NE for different networks in these game settings. Part II focuses on malware spread in networks. In cybersecurity literature malware spreads are often studied in the framework of ``attack graph" models. In these models, a node represents either a physical computing unit or a network configuration and an edge represents a physical or logical vulnerability dependency. A node gets compromised if a certain set of its neighbors are compromised. Attack graphs describe explicit scenarios in which a single vulnerability exploitation cascades further into the network exploiting inherent dependencies among the network components. Attack graphs are used for studying cascading effects in many cybersecurity applications, e.g., component failure in enterprise networks, botnet spreads, advanced persistent attacks. One distinct feature of cyber attack cascades is the stealthy nature of the attack moves. Also, cyber attacks are generally repeated. How to control stealthy and repeated attack cascades is an interesting problem. Dijk et. al.~cite{van2013flipit} first proposed a game framework called ``FlipIt" for reasoning about the stealthy interaction between a defender and an attacker over the control of a system resource. However, in cybersecurity applications, systems generally consists of multiple resources connected by a network. Therefore it is imperative to study the stealthy attack and defense in networked systems. We develop a generalized framework called ``FlipNet" which extends the work of Dijk et. al.~cite{van2013flipit} for network. We present analyses and algorithms for different problems in this framework. On the other hand, if the security of a system is limited to the vulnerabilities and exploitations that are known to the security community, often the objective of the system owner is to take cost-effective steps to minimize potential damage in the network. This problem has been formulated in the cybersecurity literature as hardening attack graphs. Several heuristic approaches have been shown in the litrature so far but no algorithmic analysis have been shown. We analyze the inherent vulnerability of the network and present approximation hardening algorithms.
  • Thumbnail Image
    Disparities in spread and control of influenza in slums of Delhi: findings from an agent-based modelling study
    Adiga, Abhijin; Chu, Shuyu; Kuhlman, Christopher J.; Lewis, Bryan L.; Marathe, Achla; Nordberg, Eric K.; Swarup, Samarth; Vullikanti, Anil; Wilson, Mandy L. (BMJ Publishing Group, 2017-11-03)
    Objectives: This research studies the role of slums in the spread and control of infectious diseases in the National Capital Territory of India, Delhi, using detailed social contact networks of its residents. Methods: We use an agent-based model to study the spread of influenza in Delhi through person-to-person contact. Two different networks are used: one in which slum and non-slum regions are treated the same, and the other in which 298 slum zones are identified. In the second network, slum-specific demographics and activities are assigned to the individuals whose homes reside inside these zones. The main effects of integrating slums are that the network has more home-related contacts due to larger family sizes and more outside contacts due to more daily activities outside home. Various vaccination and social distancing interventions are applied to control the spread of influenza. Results: Simulation-based results show that when slum attributes are ignored, the effectiveness of vaccination can be overestimated by 30%–55%, in terms of reducing the peak number of infections and the size of the epidemic, and in delaying the time to peak infection. The slum population sustains greater infection rates under all intervention scenarios in the network that treats slums differently. Vaccination strategy performs better than social distancing strategies in slums. Conclusions: Unique characteristics of slums play a significant role in the spread of infectious diseases. Modelling slums and estimating their impact on epidemics will help policy makers and regulators more accurately prioritise allocation of scarce medical resources and implement public health policies.
  • Thumbnail Image
    Predicting the Current and Future Distribution of the Invasive Weed Ageratina adenophora in the Chitwan–Annapurna Landscape, Nepal
    Poudel, Anju Sharma; Shrestha, Bharat Babu; Joshi, Mohan Dev; Muniappan, Rangaswamy (Muni); Adiga, Abhijin (International Mountain Society, 2020-05)
    With increasing globalization, trade, and human movement, the rate of alien species introduction has increased all around the globe. In addition, climate change is thought to exacerbate the situation by allowing range expansion of invasive species into new areas. Predicting the distribution of invasive species under conditions of climate change is important for identifying susceptible areas of invasion and developing strategies for limiting their expansion. We used Maxent modeling to predict the distribution of one of the world’s most aggressive invasive weeds, Ageratina adenophora (Sprengel) R. King and H. Robinson, in the Chitwan–Annapurna Landscape (CHAL) of Nepal under current conditions and 3 future climate change trajectories based on 3 representative concentration pathways (RCPs 2.6, 4.5, and 8.5) in 2 different time periods (2050 and 2070) using species occurrence data, and bioclimatic and topographic variables. Minimum temperature in the coldest month was the most important variable affecting the distribution of A. adenophora. About 38% (12,215 km2) of the CHAL area is climatically suitable for A. adenophora, with the Middle Mountain physiographic region being the most suitable one. A predicted increase in current suitable areas ranges from 1 to 2% under future climate scenarios (RCP 2.6 and RCP 8.5). All protected areas and 3 physiographic regions (Siwaliks, High Mountain, High Himalaya) are likely to gain climatically suitable areas in future climate scenarios. The upper elevational distribution limit of the weed is expected to expand by 31–48 m in future climate scenarios, suggesting that the weed will colonize additional areas at higher elevations in the future. In conclusion, our results showed that a vast area of CHAL is climatically suitable for A. adenophora. Expected further range expansion and upslope migration in the future make it essential to initiate effective management measures to prevent further negative impacts of this invasive plant.