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Abstract

Moving Target Defense (MTD) has emerged as a key technique in various security applications as it takes away the attacker’s ability to perform reconnaissance for exploiting a system’s vulnerabilities. However, most of the existing research in the field assumes unrealistic access to information about the attacker’s motivations and/or actions when developing MTD strategies. Many of the existing approaches also assume complete knowledge regarding the vulnerabilities of a system and how each of these vulnerabilities can be exploited by an attacker. In this work, we aim to create algorithms that generate effective Moving Target Defense strategies that do not rely on prior knowledge about the attackers. Our work assumes that the only way the defender receives information about its own reward is via interaction with the attacker in a repeated game setting. Depending on the amount of information that can be obtained from the interactions, we devise two different algorithms using multi-armed bandit formulation to identify efficient strategies. We then evaluate our algorithms using data mined from the National Vulnerability Database to showcase that they match the performance of the state-of-the-art techniques, despite using a lot less amount of information.

Abstract

Autonomous systems are often deployed in the open world where it is hard to obtain complete specifications of objectives and constraints. Operating based on an incomplete model can produce negative side effects (NSEs), which affect the safety and reliability of the system. We focus on mitigating NSEs in environments modeled as Markov decision processes (MDPs). First, we learn a model of NSEs using observed data that contains state-action trajectories and the severity of the associated NSEs. Unlike previous works that associate NSEs with state-action pairs, our framework associates NSEs with entire trajectories, which is more general and captures non-Markovian dependence on states and actions. Second, we learn finite state controllers (FSCs) that predict the NSE severity for a given trajectory and generalize well to unseen data. Finally, we develop a constrained MDP model that uses information from both the underlying MDP and the learned FSC for planning while avoiding NSEs. Our empirical evaluation demonstrates the effectiveness of our approach in learning and mitigating Markovian and non-Markovian NSEs.

Abstract

A significant cause of air pollution in urban areas worldwide is the high volume of road traffic. Long-term exposure to severe pollution can cause serious health issues. One approach towards tackling this problem is to design a pollution-aware traffic routing policy that balances multiple objectives of i) avoiding extreme pollution in any area ii) enabling short transit times, and iii) making effective use of the road capacities. We propose a novel sampling-based approach for this problem. We provide the first construction of a Markov Chain that can sample integer max flow solutions of a planar graph, with theoretical guarantees that the probabilities depend on the aggregate transit length. We designed a traffic policy using diverse samples and simulated traffic on real-world road maps using the SUMO traffic simulator. We observe a considerable decrease in areas with severe pollution when experimented with maps of large cities across the world compared to other approaches.

Abstract

We present the design, implementation, and analysis of autonomous brokers for a smart grid ecosystem, in the context of PowerTAC. PowerTAC simulates the real-world smart grid ecosystem through wholesale, retail, and balancing markets. The complexity of grid operations makes designing broker components using artificial intelligence (AI) inevitable. We begin with the challenges prevalent in devising such autonomous brokers. Then, we describe the design, implementation, and performance of our AI-based autonomous agent VidyutVanika (VV). We discuss two variants of VV: VV18 and VV21, which were successful in the annual PowerTAC tournaments, 2018 and 2021, respectively.

Abstract

Air pollution is a growing concern across the world. Road traffic is one of the major contributors to air pollution in urban areas. One of the approaches to solve this problem is to design a transportation policy that i) avoids extreme pollution in any area, ii) enables short transit times, and iii) makes effective use of the road capacities. Previous work to address this problem named MaxFlow-MCMC algorithm, proposed a novel sampling-based approach for this problem. In this work, we propose a significantly faster extension to the algorithm without compromising on the performance involving the following contributions: (a) We provide the first construction of a Markov Chain to sample a set of k-optimal max flow solutions directly from a planar graph. (b) We simulate traffic on large-scale real-world roadmaps using the SUMO traffic simulator. We observe a significant speed improvement in the range of 22 to 242 times in our experiments while obtaining lesser average pollution.

Abstract

Finding or tracking the location of an object accurately is a crucial problem in defense applications, robotics and computer vision. Radars fall into the spectrum of high-end defense sensors or systems upon which the security and surveillance of the entire world depends. There has been a lot of focus on the topic of Multi Sensor Tracking in recent years, with radars as the sensors. The Indian Air Force uses a Multi Sensor Tracking (MST) system to detect flights pan India, developed and supported by BEL(Bharat Electronics Limited), a defense agency we are working with. In this paper, we describe our Machine Learning approach, which is built on top of the existing system, the Air force uses. For purposes of this work, we trained our models on about 13 million anonymized real Multi Sensor tracking data points provided by radars performing tracking activity across the Indian air space. The approach has shown an increase in the accuracy of tracking by 5 percent from 91 to 96. The model and the corresponding code were transitioned to BEL, which has been tested in their simulation environment with a plan to take forward for ground testing. Our approach comprises of 3 steps: (a) We train a Neural Network model and a CatBoost model and ensemble them using a Logistic Regression model to predict one type of error, namely Splitting error, which can help to improve the accuracy of tracking. (b) We again train a Neural Network model and a CatBoost model and ensemble them using a different Logistic Regression model to predict the second type of error, namely Merging error, which can further improve the accuracy of tracking. (c) We use cosine similarity to find nearest neighbour and correct the data points, predicted to have Splitting/Merging errors, by predicting the original global track of these data points.

Abstract

Finding or tracking the location of an object accurately is a crucial problem in defense applications, robotics and computer vision. Radars fall into the spectrum of high-end defense sensors or systems upon which the security and surveillance of the entire world depends. There has been a lot of focus on the topic of Multi Sensor Tracking in recent years, with radars as the sensors. The Indian Air Force uses a Multi Sensor Tracking (MST) system to detect flights pan India, developed and supported by BEL (Bharat Electronics Limited), a defense agency we are working with. In this paper, we describe our Machine Learning approach, which is built on top of the existing system, the Air force uses. For purposes of this work, we trained our models on about 13 million anonymized real Multi Sensor tracking data points provided by radars performing tracking activity across the Indian air space. The approach has shown an increase in the accuracy of tracking by 5 percent from 91 to 96. The model and the corresponding code were transitioned to BEL, which has been tested in their simulation environment with a plan to take forward for ground testing. Our approach comprises of 3 steps:(a) We train a Neural Network model and a CatBoost model and ensemble them using a Logistic Regression model to predict one type of error, namely Splitting error, which can help to improve the accuracy of tracking.(b) We again train a Neural Network model and a CatBoost model and ensemble them using a different Logistic Regression model to predict the second type of error, namely Merging error, which can further improve the accuracy of tracking.(c) We use cosine …

Abstract

A method for viding maximum traffic flow throughput and controlling vehicular pollution concentration is pro vided . The method includes ( i ) processing a source location and a destination location obtained from a user device associated with a user to determine at least one possible route to reach the destination location from the source location , ( ii ) determining at least one maximum flow route that includes at least one edge with an edge capacity from the at least one possible route , ( iii ) determining maximum traffic flow capacity for the at least one maximum flow route , ( iv ) determining active traffic flow routes by comparing continuously if shared or common edges of a plurality of route solutions is less than or equal to a pre - defined limit and ( v ) partitioning the maximum traffic flow capacity for the active traffic flow routes and permuting each partition of the maximum traffic flow capacity for the active traffic flow routes .

Abstract

Moving target defense (MTD) has emerged as a key technique that can be used in various security applications to reduce the threat of attackers by taking away their ability to perform reconnaissance and exploit vulnerabilities. However, most of the existing research in the field assumes unrealistic access to information about the attacker’s motivations and/or actions when developing MTD strategies. Many of the existing approaches also assume complete knowledge regarding the vulnerabilities of a particular application and how each of these vulnerabilities can be exploited by an attacker. In this work, we propose an algorithm that generates effective MTD strategies for web applications that does not rely on prior knowledge about the attackers. Our approach assumes that the only information the defender receives about its own reward function, is via interaction with the attacker in a repeated game setting. We evaluate our algorithm using data which is mined from the National Vulnerability Database to show that it matches the performance of the state of the art techniques, despite using much less information.