arXiv subjects

Linga Reddy Cenkeramaddi

Publications and source records attributed to Linga Reddy Cenkeramaddi.

8 recordsLinked to original sources

arXiv2026

On-Device Interpretable Tsetlin Machine-Based Intrusion Detection for Secure IoMT

The rapid evolution of digital health technologies is redefining healthcare services worldwide. The integration of wireless communication and Internet-enabled medical devices within Internet of Medical Things (IoMT) networks enables continuous, real-time patient monitoring. However, this increased connectivity raises cybersecurity and patient safety risks due to increasingly sophisticated cyberattacks. This paper proposes a novel on-device, interpretable Tsetlin Machine (TM)-based Intrusion Detection System (IDS) to identify various phases of cyberattacks in IoMT environments. The TM is a rule-driven and transparent machine learning (ML) approach that represents attack patterns using propositional logic. Extensive evaluations on the MedSec-25 dataset, encompassing various phases of realistic cyberattacks, show that the proposed model outperforms ML models, attaining an F1-score (macro) of 97.83%. Moreover, the proposed model offers explicit explanations of its decisions to enhance transparency using feature-level contributions, class-wise vote scores, and clause activation heatmaps. Edge deployment (Raspberry Pi) further supports real-time on-device inference and intrusion detection. The combination of interpretability and high performance makes the proposed model well-suited for IoMT healthcare, where trust, reliability, safety, and timely decision-making are critical.

cs.CR↗

arXiv2026

A Tsetlin Machine-driven Intrusion Detection System for Next-Generation IoMT Security

The rapid adoption of the Internet of Medical Things (IoMT) is transforming healthcare by enabling seamless connectivity among medical devices, systems, and services. However, it also introduces serious cybersecurity and patient safety concerns as attackers increasingly exploit new methods and emerging vulnerabilities to infiltrate IoMT networks. This paper proposes a novel Tsetlin Machine (TM)-based Intrusion Detection System (IDS) for detecting a wide range of cyberattacks targeting IoMT networks. The TM is a rule-based and interpretable machine learning (ML) approach that models attack patterns using propositional logic. Extensive experiments conducted on the CICIoMT-2024 dataset, which includes multiple IoMT protocols and cyberattack types, demonstrate that the proposed TM-based IDS outperforms traditional ML classifiers. The proposed model achieves an accuracy of 99.5\% in binary classification and 90.7\% in multi-class classification, surpassing existing state-of-the-art approaches. Moreover, to enhance model trust and interpretability, the proposed TM-based model presents class-wise vote scores and clause activation heatmaps, providing clear insights into the most influential clauses and the dominant class contributing to the final model decision.

cs.CR↗

arXiv2024

A Survey of Application of Machine Learning in Wireless Indoor Positioning Systems

Indoor human positioning has become increasingly important for applications such as health monitoring, breath monitoring, human identification, safety and rescue operations, and security surveillance. However, achieving robust indoor human positioning remains challenging due to various constraints. Numerous attempts have been made in the literature to develop efficient indoor positioning systems (IPSs), with a growing focus on machine learning (ML) based techniques. This paper aims to compare and analyze current ML-based wireless techniques and approaches for indoor positioning, providing a comprehensive review of enabling technologies for human detection, positioning, and activity recognition. The study explores different input measurement data, including RSSI, TDOA, etc., for various IPSs. Key positioning techniques such as RSSI-based fingerprinting, Angle-based, and Time-based approaches are examined in conjunction with various ML methods. The survey compares the positioning accuracy, scalability, and algorithm complexity, with the goal of determining the suitable technology in various services. Finally, the paper compares distinct datasets focused on indoor localization, which have been published using diverse technologies. Overall, the paper presents a comprehensive comparison of existing techniques and localization models.

eess.SP↗

arXiv2023

MADG: Margin-based Adversarial Learning for Domain Generalization

Domain Generalization (DG) techniques have emerged as a popular approach to address the challenges of domain shift in Deep Learning (DL), with the goal of generalizing well to the target domain unseen during the training. In recent years, numerous methods have been proposed to address the DG setting, among which one popular approach is the adversarial learning-based methodology. The main idea behind adversarial DG methods is to learn domain-invariant features by minimizing a discrepancy metric. However, most adversarial DG methods use 0-1 loss based $\mathcal{H}Δ\mathcal{H}$ divergence metric. In contrast, the margin loss-based discrepancy metric has the following advantages: more informative, tighter, practical, and efficiently optimizable. To mitigate this gap, this work proposes a novel adversarial learning DG algorithm, MADG, motivated by a margin loss-based discrepancy metric. The proposed MADG model learns domain-invariant features across all source domains and uses adversarial training to generalize well to the unseen target domain. We also provide a theoretical analysis of the proposed MADG model based on the unseen target error bound. Specifically, we construct the link between the source and unseen domains in the real-valued hypothesis space and derive the generalization bound using margin loss and Rademacher complexity. We extensively experiment with the MADG model on popular real-world DG datasets, VLCS, PACS, OfficeHome, DomainNet, and TerraIncognita. We evaluate the proposed algorithm on DomainBed's benchmark and observe consistent performance across all the datasets.

cs.CV↗

arXiv2022

Impact of NOMA and CoMP Implementation Order on the Performance of Ultra-Dense Networks

Non-orthogonal multiple access (NOMA) is a promising multiple access technology to improve the throughput and spectral efficiency of the users for 5G and beyond cellular networks. Similarly, coordinated multi-point transmission and reception (CoMP) is an existing technology to improve the coverage of cell-edge users. Hence, NOMA along with CoMP can potentially enhance the throughput and coverage of the users. However, the order of implementation of CoMP and NOMA can have a significant impact on the system performance of Ultra-dense networks (UDNs). Motivated by this, we study the performance of the CoMP and NOMA based UDN by proposing two kinds of user grouping and pairing schemes that differ in the order in which CoMP and NOMA are performed for a group of users. Detailed simulation results are presented comparing the proposed schemes with the state-of-the-art systems with varying user and base station densities. Through numerical results, we show that the proposed schemes can be used to achieve a suitable coverage-throughout trade-off in UDNs.

eess.SP↗

arXiv2021

Enhanced User Grouping and Pairing Scheme for CoMP-NOMA-based Cellular Networks

Non-orthogonal multiple access (NOMA) has been identified as one of the promising technologies to enhance the spectral efficiency and throughput for the fifth generation (5G) and beyond 5G cellular networks. Alternatively, Coordinated multi-point transmission and reception (CoMP) improves the cell edge users' coverage. Thus, CoMP and NOMA can be used together to improve the overall coverage and throughput of the users. However, user grouping and pairing for CoMP-NOMA-based cellular networks have not been suitably studied in the existing literature. Motivated by this, we propose a user grouping and pairing scheme for a CoMP-NOMA-based system. Detailed numerical results are presented comparing the proposed scheme with the purely OMA-based benchmark system, NOMA only, and CoMP only systems. We show through simulation results that the proposed scheme offers a trade-off between throughput and coverage as compared to the existing NOMA or CoMP based system.

cs.IT↗

arXiv2021

Rate-Splitting Random Access Mechanism for Massive Machine Type Communications in 5G Cellular Internet-of-Things

The cellular Internet-of-Things has resulted in the deployment of millions of machine-type communication (MTC) devices. These massive number of devices must communicate with a single gNodeB (gNB) via the random access channel (RACH) mechanism. However, existing RACH mechanisms are inefficient when dealing with such large number of devices. To address this issue, we propose the rate-splitting random access (RSRA) mechanism, which uses rate splitting and decoding in rate-splitting multiple access (RSMA) to improve RACH success rates. The proposed mechanism divides the message into common and private messages and enhances the decoding performance. We demonstrate, using extensive simulations, that the proposed RSRA mechanism significantly improves the success rate of MTC in cellular IoT networks. We also evaluate the performance of the proposed mechanism with increasing number of devices and received power difference.

cs.IT↗

arXiv2019

The Modular X- and Gamma-Ray Sensor (MXGS)of the ASIM Payload on the International Space Station

The Modular X- and Gamma-ray Sensor (MXGS) is an imaging and spectral X- and Gamma-ray instrument mounted on the starboard side of the Columbus module on the International Space Station. Together with the Modular Multi-Spectral Imaging Assembly (MMIA) (Chanrion et al. this issue) MXGS constitutes the instruments of the Atmosphere-Space Interactions Monitor (ASIM) (Neubert et al. this issue). The main objectives of MXGS are to image and measure the spectrum of X- and $γ$-rays from lightning discharges, known as Terrestrial Gamma-ray Flashes (TGFs), and for MMIA to image and perform high speed photometry of Transient Luminous Events (TLEs) and lightning discharges. With these two instruments specifically designed to explore the relation between electrical discharges, TLEs and TGFs, ASIM is the first mission of its kind.

physics.ins-det↗