arXiv subjects

Shishir Nagaraja

Publications and source records attributed to Shishir Nagaraja.

At least 19 recordsLinked to original sources

arXiv2026

Energy-Aware LoRaWAN Design for Long-Lived Agricultural Sensing: Insights from a Multi-Year Deployment and Controlled Platform Comparison

Agricultural IoT nodes are often far from mains power and have limited access for maintenance. Therefore, IoT deployments benefit from low-power, long-range communication such as LoRaWAN. However, communication alone does not guarantee multi-year operation. Previous work has measured communication energy, modelled system lifetime, and documented long-lived deployments. This paper connects long-term deployment evidence with controlled platform measurements. The AgriTrust deployment has operated for 2 years and 8 months using MKR WAN1310-based Squirrel Box nodes. Building on this evidence, we compared the MKR WAN1310 and STM32WL LoRaWAN platforms using time-resolved current traces during confirmed-uplink cycles. For this outdoor comparison, we tested nine combinations of payload size and data rate with three repetitions each. Both platforms achieved 3/3 ACKed packets in every condition. The STM32WL had lower cycle energy than the WAN1310 in all tested conditions. Cycle energy ranged from 0.0350 J to 0.3550 J for STM32WL and from 0.0698 J to 0.5662 J for WAN1310. These communication events are brief, while sleep and leakage currents persist between cycles. The STM32WL sleep current was recorded at 59.6 nA, while the WAN1310 sleep current was 104 microamperes, approximately 1745 times that of the STM32WL. This difference shows that sleep current is a first-order determinant of long-term viability. However, the whole-system energy budget also depends on sensing loads, duty cycle, communication energy, and harvested-energy support. The study should be treated as a controlled characterisation, not a universal comparison across all sites, firmware stacks, or LoRaWAN modes.

cs.NI↗

arXiv2026

WaveVerif: Acoustic Side-Channel based Verification of Robotic Workflows

In this paper, we present a framework that uses acoustic side-channel analysis (ASCA) to monitor and verify whether a robot correctly executes its intended commands. We develop and evaluate a machine-learning-based workflow verification system that uses acoustic emissions generated by robotic movements. The system can determine whether real-time behavior is consistent with expected commands. The evaluation takes into account movement speed, direction, and microphone distance. The results show that individual robot movements can be validated with over 80\% accuracy under baseline conditions using four different classifiers: Support Vector Machine (SVM), Deep Neural Network (DNN), Recurrent Neural Network (RNN), and Convolutional Neural Network (CNN). Additionally, workflows such as pick-and-place and packing could be verified with similarly high confidence. Our findings demonstrate that acoustic signals can support real-time, low-cost, passive verification in sensitive robotic environments without requiring hardware modifications.

cs.CR↗

arXiv2026

Chained Attacks on Drone-Based Federated Learning: From Network Disruption to Device Impersonation

Edge Intelligence (EI) has emerged as a transformative model for mission-critical unmanned platforms, such as drone swarms, by enabling collaborative model training at the network periphery. However, the security of FL deployments depends on both network availability and robust client authentication mechanisms. This paper investigates a chained attack against drone-based FL systems that combines network-layer denial-of-service with credential-based impersonation. We demonstrate that an adversary can: (1) force legitimate drones offline using 802.11 deauthentication attacks, and (2) subsequently impersonate the disconnected drone using extracted credentials. Through a systematic literature review and empirical validation using the Flower framework on two distinct testbeds of Raspberry Pi and Jetsons, we quantify the impact of availability disruptions under Independent and Identically Distributed (IID) and Non-Independently and Identically Distributed (Non-IID) data distributions, and confirm that single-factor authentication permits post-disconnect impersonation. Our findings reveal that even short-term wireless interruptions cascade into substantial training instability, particularly under non-IID conditions, while the authentication gap enables adversaries to seamlessly replace disconnected nodes. We discuss the compounded implications for mission-critical drone deployments and outline directions for future defenses addressing both availability and authentication vulnerabilities.

cs.CR↗

arXiv2025

Decentralized Weather Forecasting via Distributed Machine Learning and Blockchain-Based Model Validation

Weather forecasting plays a vital role in disaster preparedness, agriculture, and resource management, yet current centralized forecasting systems are increasingly strained by security vulnerabilities, limited scalability, and susceptibility to single points of failure. To address these challenges, we propose a decentralized weather forecasting framework that integrates Federated Learning (FL) with blockchain technology. FL enables collaborative model training without exposing sensitive local data; this approach enhances privacy and reduces data transfer overhead. Meanwhile, the Ethereum blockchain ensures transparent and dependable verification of model updates. To further enhance the system's security, we introduce a reputation-based voting mechanism that assesses the trustworthiness of submitted models while utilizing the Interplanetary File System (IPFS) for efficient off-chain storage. Experimental results demonstrate that our approach not only improves forecasting accuracy but also enhances system resilience and scalability, making it a viable candidate for deployment in real-world, security-critical environments.

cs.LG↗

arXiv2024

Time Constant: Actuator Fingerprinting using Transient Response of Device and Process in ICS

Command injection and replay attacks are key threats in Cyber Physical Systems (CPS). We develop a novel actuator fingerprinting technique named Time Constant. Time Constant captures the transient dynamics of an actuator and physical process. The transient behavior is device-specific. We combine process and device transient characteristics to develop a copy-resistant actuator fingerprint that resists command injection and replay attacks in the face of insider adversaries. We validated the proposed scheme on data from a real water treatment testbed, as well as through real-time attack detection in the live plant. Our results show that we can uniquely distinguish between process states and actuators based on their Time Constant.

cs.CR↗

arXiv2023

SoK: Acoustic Side Channels

We provide a state-of-the-art analysis of acoustic side channels, cover all the significant academic research in the area, discuss their security implications and countermeasures, and identify areas for future research. We also make an attempt to bridge side channels and inverse problems, two fields that appear to be completely isolated from each other but have deep connections.

cs.CR↗

arXiv2022

Can You Still See Me?: Reconstructing Robot Operations Over End-to-End Encrypted Channels

Connected robots play a key role in Industry 4.0, providing automation and higher efficiency for many industrial workflows. Unfortunately, these robots can leak sensitive information regarding these operational workflows to remote adversaries. While there exists mandates for the use of end-to-end encryption for data transmission in such settings, it is entirely possible for passive adversaries to fingerprint and reconstruct entire workflows being carried out -- establishing an understanding of how facilities operate. In this paper, we investigate whether a remote attacker can accurately fingerprint robot movements and ultimately reconstruct operational workflows. Using a neural network approach to traffic analysis, we find that one can predict TLS-encrypted movements with around ~60% accuracy, increasing to near-perfect accuracy under realistic network conditions. Further, we also find that attackers can reconstruct warehousing workflows with similar success. Ultimately, simply adopting best cybersecurity practices is clearly not enough to stop even weak (passive) adversaries.

cs.CR↗

arXiv2022

Reconstructing Robot Operations via Radio-Frequency Side-Channel

Connected teleoperated robotic systems play a key role in ensuring operational workflows are carried out with high levels of accuracy and low margins of error. In recent years, a variety of attacks have been proposed that actively target the robot itself from the cyber domain. However, little attention has been paid to the capabilities of a passive attacker. In this work, we investigate whether an insider adversary can accurately fingerprint robot movements and operational warehousing workflows via the radio frequency side channel in a stealthy manner. Using an SVM for classification, we found that an adversary can fingerprint individual robot movements with at least 96% accuracy, increasing to near perfect accuracy when reconstructing entire warehousing workflows.

cs.CR↗

arXiv2022

Fingerprinting Robot Movements via Acoustic Side Channel

In this paper, we present an acoustic side channel attack which makes use of smartphone microphones recording a robot in operation to exploit acoustic properties of the sound to fingerprint a robot's movements. In this work we consider the possibility of an insider adversary who is within physical proximity of a robotic system (such as a technician or robot operator), equipped with only their smartphone microphone. Through the acoustic side-channel, we demonstrate that it is indeed possible to fingerprint not only individual robot movements within 3D space, but also patterns of movements which could lead to inferring the purpose of the movements (i.e. surgical procedures which a surgical robot is undertaking) and hence, resulting in potential privacy violations. Upon evaluation, we find that individual robot movements can be fingerprinted with around 75% accuracy, decreasing slightly with more fine-grained movement meta-data such as distance and speed. Furthermore, workflows could be reconstructed with around 62% accuracy as a whole, with more complex movements such as pick-and-place or packing reconstructed with near perfect accuracy. As well as this, in some environments such as surgical settings, audio may be recorded and transmitted over VoIP, such as for education/teaching purposes or in remote telemedicine. The question here is, can the same attack be successful even when VoIP communication is employed, and how does packet loss impact the captured audio and the success of the attack? Using the same characteristics of acoustic sound for plain audio captured by the smartphone, the attack was 90% accurate in fingerprinting VoIP samples on average, 15% higher than the baseline without the VoIP codec employed. This opens up new research questions regarding anonymous communications to protect robotic systems from acoustic side channel attacks via VoIP communication networks.

cs.CR↗

arXiv2022

Preventing or Mitigating Adversarial Supply Chain Attacks; a legal analysis

The world is currently strongly connected through both the internet at large, but also the very supply chains which provide everything from food to infrastructure and technology. The supply chains are themselves vulnerable to adversarial attacks, both in a digital and physical sense, which can disrupt or at worst destroy them. In this paper, we take a look at two examples of such successful attacks and consider what their consequences may be going forward, and analyse how EU and national law can prevent these attacks or otherwise punish companies which do not try to mitigate them at all possible costs. We find that the current types of national regulation are not technology specific enough, and cannot force or otherwise mandate the correct parties who could play the biggest role in preventing supply chain attacks to do everything in their power to mitigate them. But, current EU law is on the right path, and further vigilance may be what is necessary to consider these large threats, as national law tends to fail at properly regulating companies when it comes to cybersecurity.

cs.CY↗

arXiv2022

The Dangers of Computational Law and Cybersecurity; Perspectives from Engineering and the AI Act

Computational Law has begun taking the role in society which has been predicted for some time. Automated decision-making and systems which assist users are now used in various jurisdictions, but with this maturity come certain caveats. Computational Law exists on the platforms which enable it, in this case digital systems, which means that it inherits the same flaws. Cybersecurity addresses these potential weaknesses. In this paper we go through known issues and discuss them in the various levels, from design to the physical realm. We also look at machine-learning specific adversarial problems. Additionally, we make certain considerations regarding computational law and existing and future legislation. Finally, we present three recommendations which are necessary for computational law to function globally, and which follow ideas in safety and security engineering. As indicated, we find that computational law must seriously consider that not only does it face the same risks as other types of software and computer systems, but that failures within it may cause financial or physical damage, as well as injustice. Consequences of Computational Legal systems failing are greater than if they were merely software and hardware. If the system employs machine-learning, it must take note of the very specific dangers which this brings, of which data poisoning is the classic example. Computational law must also be explicitly legislated for, which we show is not the case currently in the EU, and this is also true for the cybersecurity aspects that will be relevant to it. But there is great hope in EU's proposed AI Act, which makes an important attempt at taking the specific problems which Computational Law bring into the legal sphere. Our recommendations for Computational Law and Cybersecurity are: Accommodation of threats, adequate use, and that humans remain in the centre of their deployment.

cs.CY↗

arXiv2022

YASM (Yet Another Surveillance Mechanism)

Client-Side Scanning (CSS) see in the Child Sexual Abuse Material Detection (CSAMD) represent ubiquitous mass scanning. Apple proposed to scan their systems for such imagery. CSAMD was since pushed back, but the European Union decided to propose forced CSS to combat and prevent child sexual abuse and weaken encryption. CSS is mass surveillance of personal property, pictures and text, without considerations of privacy and cybersecurity and the law. We first argue why CSS should be limited or not used and discuss issues with the way pictures cryptographically are handled and how the CSAMD preserves privacy. In the second part, we analyse the possible human rights violations which CSS in general can cause within the regime of the European Convention on Human Rights. The focus is the harm which the system may cause to individuals, and we also comment on the proposed Child Abuse Regulation. We find that CSS is problematic because they can rarely fulfil their purposes, as seen with antivirus software. The costs for attempting to solve issues such as CSAM outweigh the benefits and is not likely to change. The CSAMD as proposed is not likely to preserve the privacy or security in the way of which it is described source materials. We also find that CSS in general would likely violate the Right to a Fair Trial, Right to Privacy and Freedom of Expression. Pictures could have been obtained in a way that could make any trial against a legitimate perpetrator inadmissible or violate their right for a fair trial, the lack of any safeguards to protect privacy on national legal level, which would violate the Right for Privacy, and it is unclear if the kind of scanning could pass the legal test which Freedom of Expression requires. Finally, we find significant issues with the proposed Regulation, as it relies on techno-solutionist arguments and disregards knowledge on cybersecurity.

cs.CY↗

arXiv2022

The Opportunity to Regulate Cybersecurity in the EU (and the World): Recommendations for the Cybersecurity Resilience Act

Safety is becoming cybersecurity under most circumstances. This should be reflected in the Cybersecurity Resilience Act when it is proposed and agreed upon in the European Union. In this paper, we define a range of principles which this future Act should build upon, a structure and argue why it should be as broad as possible. It is based on what the cybersecurity research community for long have asked for, and on what constitutes clear hard legal rules instead of soft. Important areas such as cybersecurity should be taken seriously, by regulating it in the same way we see other types of critical infrastructure and physical structures, and be uncompromising and logical, to encompass the risks and potential for chaos which its ubiquitous nature entails. We find that principles which regulate cybersecurity systems' life-cycles in detail are needed, as is clearly stating what technology is being used, due to Kirkhoffs principle, and dismissing the idea of technosolutionism. Furthermore, carefully analysing risks is always necessary, but so is understanding when and how the systems manufacturers may fail or almost fail. We do this through the following principles: Ex ante and Ex post assessment, Safety and Security by Design, Denial of Obscurity, Dismissal of Infallibility, Systems Acknowledgement, Full Transparency, Movement towards a Zero-trust Security Model, Cybersecurity Resilience, Enforced Circular Risk Management, Dependability, Hazard Analysis and mitigation or limitation, liability, A Clear Reporting Regime, Enforcement of Certification and Standards, Mandated Verification of Security and Continuous Servicing. To this, we suggest that the Act employs similar authorities and mechanisms as the GDPR and create strong national authorities to coordinate inspection and enforcement in each Member State, with ENISA being the top and coordinating organ.

cs.CY↗

arXiv2022

When is Software a Medical Device? Understanding and Determining the 'Intention' and Requirements for Software as a Medical device in EU law

The role of software in society has changed drastically since the start of the 21st century. Software can now partially or fully facilitate anything from diagnosis to treatment of a disease, regardless of whether it is psychological or pathological, with the consequence of software being comparable to any other type of medical equipment, and this makes discovering when software must comply with such rules vital to both manufacturers and regulators. In lieu of the Medical Device Regulation we expand on the idea of intention, and identify the criteria software must fulfil to be considered medical devices within EU-law.

cs.CY↗

arXiv2022

Dissecting liabilities in adversarial surgical robot failures: A national (Danish) and European law perspective

Over the last decade, surgical robots have risen in prominence and usage. For surgical robots, connectivity is necessary to accept software updates, accept instructions, and transfer sensory data, but it also exposes the robot to cyberattacks, which can damage the patient or the surgeon. These injuries are normally caused by safety failures, as seen in accidents with industrial robots, but cyberattacks are caused by security failures instead. We create a taxonomy for both types of failures in this paper specifically for surgical robots. These robots are increasingly sold and used in the European Union (EU); we therefore consider how surgical robots are viewed and treated by EU law. Specifically, which rights regulators and manufacturers have, and which legal remedies and actions a patient or manufacturer would have in a single national legal system in the union, if injuries were to occur from a security failure caused by an adversary that cannot be unambiguously identified. We find that the selected national legal system can adequately deal with attacks on surgical robots, because it can on one hand efficiently compensate the patient. This is because of its flexibility; secondly, a remarkable absence of distinction between safety vs security causes of failure and focusing instead on the detrimental effects, thus benefiting the patient; and third, liability can be removed from the manufacturer by withdrawing its status as party if the patient chooses a separate public law measure to recover damages. Furthermore, we find that current EU law does consider both security and safety aspects of surgical robots, without it mentioning it through literal wording, but it also adds substantial liabilities and responsibilities to the manufacturers of surgical robots, gives the patient special rights and confers immense powers on the regulators.

cs.CY↗

arXiv2021

VoIPLoc: Passive VoIP call provenance via acoustic side-channels

We propose VoIPLoc, a novel location fingerprinting technique and apply it to the VoIP call provenance problem. It exploits echo-location information embedded within VoIP audio to support fine-grained location inference. We found consistent statistical features induced by the echo-reflection characteristics of the location into recorded speech. These features are discernible within traces received at the VoIP destination, enabling location inference. We evaluated VoIPLoc by developing a dataset of audio traces received through VoIP channels over the Tor network. We show that recording locations can be fingerprinted and detected remotely with a low false-positive rate, even when a majority of the audio samples are unlabelled. Finally, we note that the technique is fully passive and thus undetectable, unlike prior art. VoIPLoc is robust to the impact of environmental noise and background sounds, as well as the impact of compressive codecs and network jitter. The technique is also highly scalable and offers several degrees of freedom terms of the fingerprintable space.

cs.CR↗

arXiv2020

A Unified Access Control Model for Calibration Traceability in Safety-Critical IoT

Calibration plays an important role in ensuring device accuracy within safety-critical IoT deployments. The process of calibration involves a number of parties which must collaborate to support calibration. Calibration checks often precede safety-critical operations such as preparing a robot for surgery, requiring inter-party interaction to complete checks. At the same time, the parties involved in a calibration ecosystem may share an adversarial relationship with a subset of other parties. For instance, a surgical robot manufacturer may wish to hide the identities of third-parties from the operator (hospital), in order to maintain confidentiality of business relationships around its robot products. Thus, information flows that reveal who-calibrates-for-whom need to be managed to ensure confidentiality. Similarly, information about what-is-being-calibrated and how-often-it-is-calibrated may compromise operational confidentiality. For example, calibration-verification of connected medical devices may reveal the timing of surgical procedures and compromise PII when combined with other meta information. We show that the challenge of managing information flows between the parties involved in calibration cannot be met by any of the classical access control models, as any one of them or a simple conjunction of a subset such as the lattice model fails to meet the desired access control requirements. We demonstrate that a new unified access control model that combines BIBA, BLP, and Chinese Walls holds rich promise. We study the case for unification, system properties, and develop an XACML-based authorisation framework which enforces the unified model. Upon evaluation against a baseline simple conjunction of the three models individually, our unified model outperforms this, demonstrating it is capable of solving the novel access control challenges thrown up by digital-calibration supply chains.

cs.CR↗

arXiv2020

Unlinking super-linkers: the topology of epidemic response (Covid-19)

A key characteristic of the spread of infectious diseases is their ability to use efficient transmission paths within contact graphs. This enables the pathogen to maximise infection rates and spread within a target population. In this work, we devise techniques to localise infections and decrease infection rates based on a principled analysis of disease transmission paths within human-contact networks (proximity graphs). Experimental results of disease spreading shows that that at low visibility rates contact tracing slows disease spreading. However to stop disease spreading, contact tracing requires both significant visibility (at least 60%) into the proximity graph and the ability to place half of the population under isolation. We find that pro-actively isolating super-links -- key proximity encounters -- has significant benefits: targeted isolation of a fourth of the population based on 35% visibility into the proximity graph prevents an epidemic outbreak. It turns out that isolating super-spreaders is more effective than contact tracing and testing but less effective than targeting super-links. We highlight the important role of topology in epidemic outbreaks. We argue that proactive innoculation of a population by disabling super-links and super-spreaders may have an important complimentary role alongside contact tracing and testing as part of a sophisticated public-health response to epidemic outbreaks.

cs.SI↗