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Delhi’s toxic air, especially in the last decade, has invited a technoscientific scramble to understand, interpret, and provide fixes. The process takes place at a particular historical conjuncture marked by heightened scientism, the dominance of the market, and the splintering of scientific knowledge and practices. Technoscientific interest in air is then varied, and produces competing perspectives and policy interventions, further complicated by the state’s persistent denialism on the one hand, and a push towards a national scientific project on the other. Yet, the issue is framed in a manner that papers over tensions and sutures within the technosciences, and privileges analysis and action that are abstracted from the lived experiences of the urban majority. In this article, we trace the emergence of the technosciences of air in Delhi and locate ‘the people’ in it, before moving to a more localised and situated understanding of air to argue that residents know air pollution in ways that differ from the technosciences, but are critical to appreciate and engage with, if alliances and effective actions are to be forged. We point towards greater emphasis on health to bring together technoscientific and popular praxis around air pollution.

The demand for cyber security awareness, education, evaluation of learning levels of students etc., has increased in the past few years. In order to meet this rising demand, several cyber security learning and training platforms have been developed. Capture the flag (CTF) platforms and cyber ranges have become primary tools that facilitate education, training and recruitment of cyber security personnel. These tools evaluate and rank the participants on the basis of challenges solved by them. A discrete evaluation mechanism focusing only on flags solved, fails to ensure that the effort and knowledge demonstrated by the participants while solving the challenge, are factored into the scoring system. Most of these tools do not even distinguish between participants actually solving the flags vs. those who might copy a captured flag without actually working on the problem. Further, in flag only scoring systems, participants feel discouraged as they fail to score without finding the flags – despite putting in enormous time and effort. In this paper, we present our novel approach to quantify participant’s learning, efforts, and any unethical practices. We award partial scores by automatically capturing their behavior while solving the CTF problems. We also provide an accurate ranking system with automated solved challenge detection which replaces the need for manual flag submission. In our system, participants get hybrid scores based on their efforts, and organizations get a better and an effective evaluation tool.

Due to a high penetration of renewable energy, power systems operational planning today needs to capture unprecedented uncertainties in a short period. Fast probabilistic state estimation (SE), which creates probabilistic load flow estimates, represents one such planning tool. This paper describes a graphical model for probabilistic SE modeling that captures both the uncertainties and the power grid via embedding physical laws, i.e., KCL and KVL. With such a modeling, the resulting maximum a posteriori (MAP) SE problem is formulated by measuring state variables and their interactions. To resolve the computational difficulty in calculating the marginal distribution for interested quantities, a distributed message passing method is proposed to compute MAP estimates using increasingly available cyber resources, i.e., computational and communication intelligence. A modified message passing algorithm is then introduced to improve the convergence and optimality. Simulation results illustrate the probabilistic SE and demonstrate the improved performance over traditional deterministic approaches via: 1) the more accuracy mean estimate; 2) the confidence interval covering the true state; and 3) the reduced computational time.

Cyber-physical critical infrastructures such as power plants are no longer air-gapped. Due to IP-Convergence, the control systems and sensor/actuator communication networks are often directly or indirectly connected to the Internet. While network intrusion detection can provide certain cyber defense capabilities, that is not sufficient due to covert attacks or insider attacks. Therefore, in recent years, a lot of research is being carried out to detect intrusion by observing anomalies in the plants’ physical dynamics. In this work, we propose a robust anomaly detection mechanism based on a semi-supervised machine learning technique allowing us near real-time localization of attacks. Deep neural network architecture is used to detect anomaly – based on reconstruction error. We demonstrate our method’s efficacy on the SWaT dataset. Our method outperforms other existing anomaly detection techniques with an AUC score of 0.9275.

Power Grid, Water/Sewage control system, and Industrial automation are some examples of critical infrastructures. They are critical because malfunctioning of any of these may lead to severe industrial accidents. It may also have severe implications for the national economy and security. Nation-states are gearing up for potential cyber warfare, signs of which are already visible in various incidents such as Stuxnet, BlackEnergy, and many other recent attacks. As a result, research in cyber defense for such critical systems is an urgent need for all countries. IIT Kanpur has established the National Interdisciplinary Center for Cyber Security and Cyber Defense of Critical Infrastructures (C3i Center) to engage in research in this crucial area. In the past, the authors carried out a lot of cybersecurity experiments on co-Simulator-based platforms. These experiments show that they do not allow us to penetrate at the granularity required to defend real systems and therefore, physical test-beds are to be constructed. In this chapter, the authors describe how to build various test-beds.

Blockchains, in the past decade, have gathered a lot of attraction due to its potential to disrupt the way in which trust in the digital world is generated. Blockchain, the technology powering cryptocurrency worth billions of dollars, is tamper-resistant, immutable ledger that is maintained in a decentralized manner by the miners who do not trust each other. This characteristic of blockchain to induce trust in the untrusted environment, by establishing a commonly agreed upon (among the members of the blockchain network) shared ledger, can be used to solve problems in domains such as finance, supply chain, health care, IoT, etc. In this chapter, we will introduce blockchain and present its applications in cybersecurity.

Critical infrastructures such as power grid, water treatment plants, chemical plants, oil and gas refineries, transportation systems, etc., are examples of large-scale cyber-physical systems. In the recent years, there has been multitude of cyberattacks on such systems such as STUXNET attack in Iran, BlackEnergy malware and ransomware attacks on power distribution systems in Ukraine, and malware attacks on German Steel plant to mention a few. Critical infrastructure security is a major area of concern for all governments. In the United States, several laboratories such as Sandia National Lab, Idaho National Lab, and National Institutes of Standards have built critical infrastructure cyber-security experimentation test-beds. These test-beds are built with industry-scale equipment from various sectors of critical infrastructures to allow researchers to detect vulnerabilities in multitude of products that are used by utility companies in their systems and to research efficacy of their remediation techniques. These test-beds also allow utilities to bring in hardware and software products for hardware/software-in-the-loop testing for vulnerability detection or cyber-security product validation. In India, there has been no such test-bed for such research and validation activities. In the interdisciplinary center for cyber-security and cyber defense of critical infrastructures (C3i) at the Indian Institute of Technology Kanpur, we are building such a facility—a first in India. We already have built a lab-scale test-bed for power distribution automation and discovered a plethora of vulnerabilities in the architecture, protocols, and widely used products in our test-bed. In this article, we first describe the lab-scale test-bed to provide an insight into the utility of such a test-bed, and then we describe our ongoing development of industry-scale test-beds.

Software-defined radio (SDR) can have high communication quality with a reconfigurable RF front-end. One of the main challenges of a reconfigurable RF front-end is finding an optimal configuration among all possible configurations. In order to efficiently find an optimal configuration, Environment-Adaptable Fast (EAF) optimization utilizes calculated signal-to-interference-and-noise ratio (SINR) and narrows down the searching space (Jun et al., Environment-adaptable efficient optimization for programming of reconfigurable Radio Frequency (RF) receivers, 2014). However, we found several limitations for applying the EAF optimization to a realistic large-scale Radio Frequency-Field Programmable Gate Array (RF-FPGA) system. In this paper, we first investigated two estimation issues of RF impairments: a saturation bias of nonlinearity estimates and limited resources for RF impairment estimation. Using the estimated results, the SINR formula was calculated and used for the Environment-Adaptable Fast Multi-Resolution (EAF-MR) optimization, which was designed by applying the EAF optimization to multi-resolution optimization. Finally, our simulation set-up demonstrated the efficiency improvement of the EAF-MR optimization for a large-scale RF-FPGA.

A grand challenge for state estimation in newly built smart grid lies in how to deal with the increasing uncertainties. To solve the problem, we propose a data-driven state estimation approach based on recent targeted investment on sensors, data storage, and computing devices. An architecture is proposed to use power system physics and pattern to systematically clean historical data and conduct supervised learning, where historical similar measurements and their states are used to learn the relationship between the current measurement and the state. In order to deal with nonlinearity, kernel trick is used to produce linear mapping in a carefully selected higher dimensional space. To speed up the data-driven approach for online services, we analyze power system data set and discover its clustering property due to the periodic pattern of power systems. This leads to significant dimension reduction and the idea of preorganizing data points in a tree structure for inquiry, leading to 1000 times speedup. Numerical results show that the proposed data-driven approach works well in a smart grid setting with increasing uncertainties and it produces an online state estimate excelling current industrial approach.

In today’s information driven society and economy, web facing applications are most common way to run information dissemination, banking, e-commerce etc. Web applications are frequently targeted by attackers through intelligently crafted http requests to exploit vulnerabilities existing in the application, front-end, and the web-clients. Some of the most frequent such attacks are SQL Injection, Cross-Site Scripting, Path-traversal, Command Injection, Cross-site request forgery etc. Detecting these attacks up front and blocking them, or redirecting the request to a honey-pot could be a way to prevent web applications from being exploited. In this work, we developed a number of machine learning models for detecting and classifying http requests into normal, and various types of attacks. Currently, the models are applied as an ensemble on the http server logs, to classify and build data analytics on the http requests received by any web server in order to garner threat intelligence, and threat landscape. We also implemented an online log-analysis version that analyzes logs every 15 s to classify http requests in the recent 15 s. However, it can also be used as a web application firewall to block the http requests based on the classification results. We also have implemented an intrusion protection mechanism by redirecting http requests classified upfront as malicious towards a web honeypot. We compare various existing signature based, regular expression based, and machine learning based techniques against our models for detection and classification of http based attacks, and show that our methods achieve better performance over existing techniques.