Department of Computer Science and Engineering

Indian Institute of Technology Hyderabad (IITH) is a premier institute of science and technology established in 2008. IITH has been consistently ranked in the&nbsp;<a href="https://iith.ac.in/reports/" rel="noopener noreferrer">top 10 institutes</a>&nbsp;in India for Engineering&nbsp;<a href="https://www.nirfindia.org/2021/EngineeringRanking.html" rel="noopener noreferrer" target="_blank">according to NIRF</a>&nbsp;making it one of the most coveted schools for science and technology in the country.

IITH offers&nbsp;<a href="https://iith.ac.in/academics/under-graduate/">undergraduate programs</a>&nbsp;in all the classical engineering disciplines, applied sciences, design, as well as several modern interdisciplinary areas. Students are given a flexibility to explore a broad set of areas, and potentially pursue a minor or double major in a discipline that is not their own. Students who wish to seek a deeper understanding of their own discipline are strongly encouraged to get involved in cutting-edge research with the help of a faculty to mentor them, and earn an Honors in their own field.

The very foundation of IIT Hyderabad is based on&nbsp;<a href="https://iith.ac.in/research/">research and innovation</a>. The vibrant research culture is evident from the number of patents and publications that IITH produces consistently every year. IITH offers&nbsp;<a href="https://iith.ac.in/academics/programmes-offered/">graduate programs</a>&nbsp;at both a masters, and a doctoral level, in several diverse areas. There are separate programs for technology, design, science, and liberal arts. The&nbsp;<a href="https://iith.ac.in/academics/post-graduate/">MTech program</a>&nbsp;is offered in various modes and durations to cater to the ever-growing need of postgraduate level professionals in the country.

IITH encourages and supports&nbsp;<a href="https://iic-iith.netlify.app/index.html" rel="noopener noreferrer" target="_blank">innovation and entrepreneurship</a>&nbsp;at every stage. The minor program in entrepreneurship is open to all students of IITH, and many of the courses are offered by industrialists who are entrepreneurs themselves. There are various&nbsp;<a href="https://iith.ac.in/research/centres-incubators/">innovation centers and incubators</a>&nbsp;that promote entrepreneurship and help mentor young innovators. IITH has been very successful in building tie-ups with leading academic institutions around the globe.

IITH enjoys a very special relationship with Japanese universities and industries that goes beyond academic and research collaborations. In fact, some of the iconic buildings in IITH campus will carry the signature of Japanese architecture.

IITH creates a unique holistic ecosystem for education that offers interactive learning, a very flexible academic structure, cutting-edge research, strong industry collaboration, and entrepreneurship. This is an environment which enables students and faculty to translate their dreams into realities.

IIT Hyderabad

Network Functions Virtualization (NFV) promises programmability and cost savings by replacing hardware middleboxes with more flexible Virtual Network Functions (VNFs) on commodity servers. But, the current virtualization technologies do not fully isolate the system resources like Last Level Cache (LLC) and Memory Bandwidth (MB); therefore, co-location of VNFs on the same commodity server causes interference effects which might severely impact the performance of VNFs in terms of throughput, latency, etc. Contention at LLC is one of the root causes of this performance degradation and it is addressed by LLC resource partitioning. But, it remains unexplored the impact of both LLC and MB on VNF performance. In this work, we investigate the importance of MB partitioning along with LLC partitioning to achieve performance isolation in NFV-based systems. Allocating these system resources among co-located VNFs to meet Service Level Agreements (SLAs) is challenging due to the dynamic nature of traffic and varying functionality of VNFs. In this work, we formulate the resource allocation problem as an Integer Linear Programming (ILP) for maximizing the number of accepted VNF requests with SLA guarantees. Since the problem is NP-hard, we present a polynomial time $\epsilon $ -approximation scheme. Further, we propose a heuristic approach named NFVPermit, a resource manager for NFV-based systems that tries to ensure performance isolation among co-located VNFs based on their current traffic rates and SLA requirements. Through extensive experiments, we show how NFVPermit outperforms state-of-the-art and baseline approaches. © 2004-2012 IEEE.

World University root node

IEEE Transactions on Network and Service Management

NFVPermit: Toward Ensuring Performance Isolation in NFV-Based Systems

In this paper we present a novel method, Knowledge Persistence (), for faster evaluation of Knowledge Graph (KG) completion approaches. Current ranking-based evaluation is quadratic in the size of the KG, leading to long evaluation times and consequently a high carbon footprint. addresses this by representing the topology of the KG completion methods through the lens of topological data analysis, concretely using persistent homology. The characteristics of persistent homology allow to evaluate the quality of the KG completion looking only at a fraction of the data. Experimental results on standard datasets show that the proposed metric is highly correlated with ranking metrics (Hits@N, MR, MRR). Performance evaluation shows that is computationally efficient: In some cases, the evaluation time (validation+test) of a KG completion method has been reduced from 18 hours (using Hits@10) to 27 seconds (using ), and on average (across methods & data) reduces the evaluation time (validation+test) by ≈ 99.96%. © 2023 ACM.

ACM Web Conference 2023 - Proceedings of the World Wide Web Conference, WWW 2023

Can Persistent Homology provide an efficient alternative for Evaluation of Knowledge Graph Completion Methods?

In an undirected graph G=(V,E), we say that (A,B) is a pair of perfectly matched sets if A and B are disjoint subsets of V and every vertex in A (resp. B) has exactly one neighbor in B (resp. A). The size of a pair of perfectly matched sets (A,B) is |A|=|B|. The PERFECTLY MATCHED SETS problem is to decide whether a given graph G has a pair of perfectly matched sets of size k. We show that PMS is W[1]-hard when parameterized by solution size k even when restricted to split graphs and bipartite graphs. We observe that PMS is FPT when parameterized by clique-width, and give FPT algorithms with respect to the parameters distance to cluster, distance to co-cluster and treewidth. Complementing FPT results, we show that PMS does not admit a polynomial kernel when parameterized by vertex cover number unless NP⊆coNP/poly. We also provide an exact exponential algorithm running in time O⁎(1.966n). Finally, considering graphs with structural assumptions, we show that PMS remains NP-hard on planar graphs. © 2023 Elsevier B.V.

Fulltext

Theoretical Computer Science

Perfectly matched sets in graphs: Parameterized and exact computation

We present a Karchmer-Wigderson game to study the complexity of hazard-free formulas. This new game is both a generalization of the monotone Karchmer-Wigderson game and an analog of the classical Boolean Karchmer-Wigderson game. Therefore, it acts as a bridge between the existing monotone and general games. Using this game, we prove hazard-free formula size and depth lower bounds that are provably stronger than those possible by the standard technique of transferring results from monotone complexity in a black-box fashion. For the multiplexer function we give (1) a hazard-free formula of optimal size and (2) an improved low-depth hazard-free formula of almost optimal size and (3) a hazard-free formula with alternation depth 2 that has optimal depth. We then use our optimal constructions to obtain an improved universal worst-case hazard-free formula size upper bound. We see our results as a step towards establishing hazard-free computation as an independent missing link between Boolean complexity and monotone complexity. © Christian Ikenmeyer, Balagopal Komarath, and Nitin Saurabh; licensed under Creative Commons License CC-BY 4.0.

Leibniz International Proceedings in Informatics, LIPIcs

Karchmer-Wigderson Games for Hazard-Free Computation

Tiled Chip Multicore Processors (TCMP) with packet switching Network-on-Chip (NoC) have become a common method for on-chip connectivity. The performance of the entire system may suffer if a malicious Hardware Trojan (HT) is present in the NoC routers as it might negatively disrupt communication between tiles. Detecting Trojans in complicated multi-processor System on Chips (SoCs) using traditional pre and post silicon validation approaches is a huge difficulty. In this paper the presence of multiple HTs in the routing unit is modelled and its impact analysis is done for both synthetic traffic and real benchmarks using gem5 simulator. It can be observed that the presence of multiple trojans decrease the overall performance of the system. © 2023 IEEE.

2023 International Conference on Control, Communication and Computing, ICCC 2023

Impact Analysis of Distributed DoS Attack by Multiple HTs in TCMP Architectures

Humans learn continually throughout their lifespan by accumulating diverse knowledge and fine-tuning it for future tasks. When presented with a similar goal, neural networks suffer from catastrophic forgetting if data distributions across sequential tasks are not stationary over the course of learning. An effective approach to address such continual learning (CL) problems is to use hypernetworks which generate task dependent weights for a target network. However, the continual learning performance of existing hypernetwork based approaches are affected by the assumption of independence of the weights across the layers in order to maintain parameter efficiency. To address this limitation, we propose a novel approach that uses a dependency preserving hypernetwork to generate weights for the target network while also maintaining the parameter efficiency. We propose to use recurrent neural network (RNN) based hypernetwork that can generate layer weights efficiently while allowing for dependencies across them. In addition, we propose novel regularisation and network growth techniques for the RNN based hypernetwork to further improve the continual learning performance. To demonstrate the effectiveness of the proposed methods, we conducted experiments on several image classification continual learning tasks and settings. We found that the proposed methods based on the RNN hypernetworks outperformed the baselines in all these CL settings and tasks. © 2023 IEEE.

Proceedings - 2023 IEEE Winter Conference on Applications of Computer Vision, WACV 2023

Continual Learning with Dependency Preserving Hypernetworks

The Last Level Cache (LLC) of modern multicore processors is normally shared between different cores and applications. Dynamic cache partitioning is applied to the LLC for fairly distributing the LLC space among the applications. Recently, Covert Channel Attacks (CCA) becomes a major security issue for modern multicore systems. In CCA, two malicious applications: spy and Trojan, run in two different cores. Trojan normally runs in a secure core and knows some secret information. Through CCA, Trojan communicates this information to the spy. A well-known technique to perform such an attack is Prime Probe (P+P). It performs the attack by using the shared behavior of LLC space. Cache partitioning is considered a defense against such CCA. Partitioning makes the applications isolated in the LLC and they cannot evict each other block from the LLC. Hence, the existing P+P based attacks are not possible while dynamic partitioning is applied to LLC. However, in this work, we have proposed a modified CCA (based on P+P) which can establish a covert channel on top of the dynamic cache partitioning technique applied to LLC. Such kinds of attacks must need to be handled carefully in modern processors. A possible defense mechanism for the new attack is also discussed in this paper. © 2023 IEEE.

Proceedings - International Symposium on Quality Electronic Design, ISQED

ACPC: Covert Channel Attack on Last Level Cache using Dynamic Cache Partitioning

We consider the problem of subset selection for ℓp subspace approximation and (k, p)-clustering. Our aim is to efficiently find a small subset of data points such that solving the problem optimally for this subset gives a good approximation to solving the problem optimally for the original input. For ℓp subspace approximation, previously known subset selection algorithms based on volume sampling and adaptive sampling proposed in Deshpande and Varadarajan (STOC’07, 2007), for the general case of p∈ [1 , ∞) , require multiple passes over the data. In this paper, we give a one-pass subset selection with an additive approximation guarantee for ℓp subspace approximation, for any p∈ [1 , ∞) . Earlier subset selection algorithms that give a one-pass multiplicative (1 + ϵ) approximation work under the special cases. Cohen et al. (SODA’17, 2017) gives a one-pass subset section that offers multiplicative (1 + ϵ) approximation guarantee for the special case of ℓ2 subspace approximation. Mahabadi et al. (STOC’20, 2020) gives a one-pass noisy subset selection with (1 + ϵ) approximation guarantee for ℓp subspace approximation when p∈ { 1 , 2 } . Our subset selection algorithm gives a weaker, additive approximation guarantee, but it works for any p∈ [1 , ∞) . We also focus on (k, p)-clustering, where the task is to group the data points into k clusters such that the sum of distances from points to cluster centers (raised to the power p) is minimized for p∈ [1 , ∞) . The subset selection algorithms are based on Dp sampling due to Wei (NIPS’16, 2016) which is an extension of D2 sampling proposed in Arthur and Vassilvitskii (SODA’07, 2007). Due to the inherently adaptive nature of the Dp sampling, these algorithms require taking multiple passes over the input. In this work, we suggest one pass subset selection for (k, p)-clustering that gives constant factor approximation with respect to the optimal solution with an additive approximation guarantee. Bachem et al. (NIPS’16, 2016) also gives one pass subset selection for k-means for p= 2 ; however, our result gives a solution for a more generic problem when p∈ [1 , ∞) . At the core, our contribution lies in showing a one-pass MCMC-based subset selection algorithm such that its cost incurred due to the sampled points closely approximates the corresponding optimal cost, with high probability. © 2023, The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.

Algorithmica

One-Pass Additive-Error Subset Selection for ℓp Subspace Approximation and (k, p)-Clustering

Softwarized Unmanned Aerial Vehicles (UAVs) use network programmability concept of Software-Defined Network (SDN) to separate the hardware control layer from the data layer via OpenFlow protocols. The softwarized UAV enable ubiquitous connection, as well as a flexible, cost-effective, and improved method for upgrading all network services without shutting down the entire system. However, the connectivity of UAVs with OpenFlow switches and their heavy reliance on unsecured communication protocols makes the entire network vulnerable. This is a critical concern, particularly in combat surveillance, where eavesdropping, adding, changing, or deleting messages during communications between deployed UAVs and SDN controller is a possible threat. To mitigate the aforementioned issues, this paper presents a novel secure data sharing framework for softwarized UAV environments that incorporates blockchain and Deep Learning (DL). First we present a blockchain-based technique to reg-ister, verify and thereafter validate the communication entities in softwarized UAV environment using smart contract-based Proof-of-Authentication (PoA) consensus mechanism. Additionally, a new deep neural network architecture-based flow analyzer is designed to detect illegitimate transactions. The latter combines a Stacked Contractive Sparse AutoEncoder with Attention-based Long Short-term Memory Neural Network (SCSAE-ALSTM) to improve intrusion detection process. The effectiveness of our framework over several standard baseline methodologies is demonstrated by security analysis and experimental findings. © 2022 IEEE.

Department of Electrical Engineering

2022 IEEE International Conference on Communications Workshops, ICC Workshops 2022

Blockchain and Deep Learning Empowered Secure Data Sharing Framework for Softwarized UAVs

Fine-grained action recognition involves comparison of similar actions of variable-length size consisting of subtle interactions between human and specific objects. Hence, we propose a dynamic kernel-based approach to handle the variable-length patterns for effective recognition of fine-grained actions. Initially, we extract local spatio-temporal features for each video to capture appearance and motion information effectively. An action-independent Gaussian mixture model (AIGMM) is trained on the extracted features of all fine-grained actions to analyze spatio-temporal information and preserve the local similarities among fine-grained actions. Then, the statistics of AIGMM, namely, mean, covariance, and posteriors are used to build the kernels for finding the similarity between any two fine-grained actions by mapping statistics to kernel feature space. We demonstrate the effectiveness of proposed approach using three dynamic kernels i.e., GMM mean interval kernel, supervector kernel, intermediate matching kernel on four varieties of fine-grained action datasets, namely, MERL, JIGSAWS, KSCGR, and MPII cooking2 © 2021 Elsevier Ltd