arXiv subjects

Vivek Balachandran

Publications and source records attributed to Vivek Balachandran.

14 recordsLinked to original sources

arXiv2026

Data Obfuscation for Secure Use of Classical Values in Quantum Computation

Quantum computing often requires classical data to be supplied to execution environments that may not be fully trusted or isolated. While encryption protects data at rest and in transit, it provides limited protection once computation begins, when classical values are encoded into quantum registers. This paper explores data obfuscation for protecting classical values during quantum computation. To the best of our knowledge, we present the first explicit data obfuscation technique designed to protect classical values during quantum execution. We propose an obfuscation technique that encodes sensitive data into structured quantum representations across multiple registers, avoiding direct exposure while preserving computational usability. Reversible quantum operations and amplitude amplification allow selective recovery of valid encodings without revealing the underlying data. We evaluate the feasibility of the proposed method through simulation and analyze its resource requirements and practical limitations. Our results highlight data obfuscation as a complementary security primitive for quantum computing.

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arXiv2026

QSpy: A Quantum RAT for Circuit Spying and IP Theft

As quantum computing platforms increasingly adopt cloud-based execution, users submit quantum circuits to remote compilers and backends, trusting that what they submit is exactly what will be run. This shift introduces new trust assumptions in the submission pipeline, which remain largely unexamined. In this paper, we present QSpy, the first proof-of-concept Quantum Remote Access Trojan capable of intercepting quantum circuits in transit. Once deployed on a user's machine, QSpy silently installs a rogue certificate authority and proxies outgoing API traffic, enabling a man-in-the-middle (MITM) attack on submitted quantum circuits. We show that the intercepted quantum circuits may be forwarded to a remote server, which is capable of categorizing, storing, and analyzing them, without disrupting execution or triggering authentication failures. Our prototype targets IBM Qiskit APIs on a Windows system, but the attack model generalizes to other delegated quantum computing workflows. This work highlights the urgent need for submission-layer protections and demonstrates how even classical attack primitives can pose critical threats to quantum workloads.

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arXiv2025

Protecting Quantum Circuits Through Compiler-Resistant Obfuscation

Quantum circuit obfuscation is becoming increasingly important to prevent theft and reverse engineering of quantum algorithms. As quantum computing advances, the need to protect the intellectual property contained in quantum circuits continues to grow. Existing methods often provide limited defense against structural and statistical analysis or introduce considerable overhead. In this paper, we propose a novel quantum obfuscation method that uses randomized U3 transformations to conceal circuit structure while preserving functionality. We implement and assess our approach on QASM circuits using Qiskit AER, achieving over 93\% semantic accuracy with minimal runtime overhead. The method demonstrates strong resistance to reverse engineering and structural inference, making it a practical and effective approach for quantum software protection.

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arXiv2025

ODoQ: Oblivious DNS-over-QUIC

The Domain Name System (DNS), which converts domain names to their respective IP addresses, has advanced enhancements aimed at safeguarding DNS data and users' identity from attackers. The recent privacy-focused advancements have enabled the IETF to standardize several protocols. Nevertheless, these protocols tend to focus on either strengthening user privacy (like Oblivious DNS and Oblivious DNS-over-HTTPS) or reducing resolution latency (as demonstrated by DNS-over-QUIC). Achieving both within a single protocol remains a key challenge, which we address in this paper. Our proposed protocol -- 'Oblivious DNS-over-QUIC' (ODoQ) -- leverages the benefits of the QUIC protocol and incorporates an intermediary proxy server to protect the client's identity from exposure to the recursive resolver.

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arXiv2025

Bridging the Gap in Phishing Detection: A Comprehensive Phishing Dataset Collector

To combat phishing attacks -- aimed at luring web users to divulge their sensitive information -- various phishing detection approaches have been proposed. As attackers focus on devising new tactics to bypass existing detection solutions, researchers have adapted by integrating machine learning and deep learning into phishing detection. Phishing dataset collection is vital to developing effective phishing detection approaches, which highly depend on the diversity of the gathered datasets. The lack of diversity in the dataset results in a biased model. Since phishing websites are often short-lived, collecting them is also a challenge. Consequently, very few phishing webpage dataset repositories exist to date. No single repository comprehensively consolidates all phishing elements corresponding to a phishing webpage, namely, URL, webpage source code, screenshot, and related webpage resources. This paper introduces a resource collection tool designed to gather various resources associated with a URL, such as CSS, Javascript, favicons, webpage images, and screenshots. Our tool leverages PhishTank as the primary source for obtaining active phishing URLs. Our tool fetches several additional webpage resources compared to PyWebCopy Python library, which provides webpage content for a given URL. Additionally, we share a sample dataset generated using our tool comprising 4,056 legitimate and 5,666 phishing URLs along with their associated resources. We also remark on the top correlated phishing features with their associated class label found in our dataset. Our tool offers a comprehensive resource set that can aid researchers in developing effective phishing detection approaches.

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arXiv2025

Overcoming DNSSEC Islands of Security: A TLS and IP-Based Certificate Solution

The Domain Name System (DNS) serves as the backbone of the Internet, primarily translating domain names to IP addresses. Over time, various enhancements have been introduced to strengthen the integrity of DNS. Among these, DNSSEC stands out as a leading cryptographic solution. It protects against attacks (such as DNS spoofing) by establishing a chain of trust throughout the DNS nameserver hierarchy. However, DNSSEC's effectiveness is compromised when there is a break in this chain, resulting in "Islands of Security", where domains can authenticate locally but not across hierarchical levels, leading to a loss of trust and validation between them. Leading approaches to addressing these issues were centralized, with a single authority maintaining some kind of bulletin board. This approach requires significantly more infrastructure and places excessive trust in the entity responsible for managing it properly. In this paper, we propose a decentralized approach to addressing gaps in DNSSEC's chain of trust, commonly referred to as "Islands of Security". We leverage TLS and IP-based certificates to enable end-to-end authentication between hierarchical levels, eliminating the need for uniform DNSSEC deployment across every level of the DNS hierarchy. This approach enhances the overall integrity of DNSSEC, while reducing dependence on registrars for maintaining signature records to verify the child nameserver's authenticity. By offering a more flexible and efficient solution, our method strengthens DNS security and streamlines deployment across diverse environments.

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arXiv2025

Phish-Blitz: Advancing Phishing Detection with Comprehensive Webpage Resource Collection and Visual Integrity Preservation

Phishing attacks are increasingly prevalent, with adversaries creating deceptive webpages to steal sensitive information. Despite advancements in machine learning and deep learning for phishing detection, attackers constantly develop new tactics to bypass detection models. As a result, phishing webpages continue to reach users, particularly those unable to recognize phishing indicators. To improve detection accuracy, models must be trained on large datasets containing both phishing and legitimate webpages, including URLs, webpage content, screenshots, and logos. However, existing tools struggle to collect the required resources, especially given the short lifespan of phishing webpages, limiting dataset comprehensiveness. In response, we introduce Phish-Blitz, a tool that downloads phishing and legitimate webpages along with their associated resources, such as screenshots. Unlike existing tools, Phish-Blitz captures live webpage screenshots and updates resource file paths to maintain the original visual integrity of the webpage. We provide a dataset containing 8,809 legitimate and 5,000 phishing webpages, including all associated resources. Our dataset and tool are publicly available on GitHub, contributing to the research community by offering a more complete dataset for phishing detection.

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arXiv2025

Phishing Webpage Detection: Unveiling the Threat Landscape and Investigating Detection Techniques

In the realm of cybersecurity, phishing stands as a prevalent cyber attack, where attackers employ various tactics to deceive users into gathering their sensitive information, potentially leading to identity theft or financial gain. Researchers have been actively working on advancing phishing webpage detection approaches to detect new phishing URLs, bolstering user protection. Nonetheless, the ever-evolving strategies employed by attackers, aimed at circumventing existing detection approaches and tools, present an ongoing challenge to the research community. This survey presents a systematic categorization of diverse phishing webpage detection approaches, encompassing URL-based, webpage content-based, and visual techniques. Through a comprehensive review of these approaches and an in-depth analysis of existing literature, our study underscores current research gaps in phishing webpage detection. Furthermore, we suggest potential solutions to address some of these gaps, contributing valuable insights to the ongoing efforts to combat phishing attacks.

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arXiv2025

A Hybrid Encryption Framework Combining Classical, Post-Quantum, and QKD Methods

This paper introduces a hybrid encryption framework combining classical cryptography (EdDSA, ECDH), post-quantum cryptography (ML-DSA-6x5, ML-KEM-768), and Quantum Key Distribution (QKD) via Guardian to counter quantum computing threats. Our prototype implements this integration, using a key derivation function to generate secure symmetric and HMAC keys, and evaluates its performance across execution time and network metrics. The approach improves data protection by merging classical efficiency with PQC's quantum resilience and QKD's key security, offering a practical transition path for cryptographic systems. This research lays the foundation for future adoption of PQC in securing digital communication.

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arXiv2025

Quantum Opacity, Classical Clarity: A Hybrid Approach to Quantum Circuit Obfuscation

Quantum computing leverages quantum mechanics to achieve computational advantages over classical hardware, but the use of third-party quantum compilers in the Noisy Intermediate-Scale Quantum (NISQ) era introduces risks of intellectual property (IP) exposure. We address this by proposing a novel obfuscation technique that protects proprietary quantum circuits by inserting additional quantum gates prior to compilation. These gates corrupt the measurement outcomes, which are later corrected through a lightweight classical post-processing step based on the inserted gate structure. Unlike prior methods that rely on complex quantum reversals, barriers, or physical-to-virtual qubit mapping, our approach achieves obfuscation using compiler-agnostic classical correction. We evaluate the technique across five benchmark quantum algorithms -- Shor's, QAOA, Bernstein-Vazirani, Grover's, and HHL -- using IBM's Qiskit framework. The results demonstrate high Total Variation Distance (above 0.5) and consistently negative Degree of Functional Corruption (DFC), confirming both statistical and functional obfuscation. This shows that our method is a practical and effective solution for the security of quantum circuit designs in untrusted compilation flows.

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arXiv2025

ObfusQate: Unveiling the First Quantum Program Obfuscation Framework

This paper introduces ObfusQate, a novel tool that conducts obfuscations using quantum primitives to enhance the security of both classical and quantum programs. We have designed and implemented two primary categories of obfuscations: quantum circuit level obfuscation and code level obfuscation, encompassing a total of eight distinct methods. Quantum circuit-level obfuscation leverages on quantum gates and circuits, utilizing strategies such as quantum gate hiding and identity matrices to construct complex, non-intuitive circuits that effectively obscure core functionalities and resist reverse engineering, making the underlying code difficult to interpret. Meanwhile, code-level obfuscation manipulates the logical sequence of program operations through quantum-based opaque predicates, obfuscating execution paths and rendering program behavior more unpredictable and challenging to analyze. Additionally, ObfusQate can be used to obfuscate malicious code segments, making them harder to detect and analyze. These advancements establish a foundational framework for further exploration into the potential and limitations of quantum-based obfuscation techniques, positioning ObfusQate as a valuable tool for future developers to enhance code security in the evolving landscape of software development. To the best of our knowledge, ObfusQate represents the pioneering work in developing an automated framework for implementing obfuscations leveraging quantum primitives. Security evaluations show that obfuscations by ObfusQate maintain code behavior with polynomial overheads in space and time complexities. We have also demonstrated an offensive use case by embedding a keylogger into Shor's algorithm and obfuscating it using ObfusQate. Our results show that current Large language models like GPT 4o, GPT o3 mini and Grok 3 were not able to identify the malicious keylogger after obfuscation.

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arXiv2024

QIris: Quantum Implementation of Rainbow Table Attacks

This paper explores the use of Grover's Algorithm in the classical rainbow table, uncovering the potential of integrating quantum computing techniques with conventional cryptographic methods to develop a Quantum Rainbow Table Proof-of-Concept. This leverages on Quantum concepts and algorithms which includes the principle of qubit superposition, entanglement and teleportation, coupled with Grover's Algorithm to enable a more efficient search through the rainbow table. The paper also details on the hardware constraints and the work around to produce better results in the implementation stages. Through this work we develop a working prototype of quantum rainbow table and demonstrate how quantum computing could significantly improve the speed of cyber tools such as password crackers and thus impact the cyber security landscape.

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arXiv2024

From ML to LLM: Evaluating the Robustness of Phishing Webpage Detection Models against Adversarial Attacks

Phishing attacks attempt to deceive users into stealing sensitive information, posing a significant cybersecurity threat. Advances in machine learning (ML) and deep learning (DL) have led to the development of numerous phishing webpage detection solutions, but these models remain vulnerable to adversarial attacks. Evaluating their robustness against adversarial phishing webpages is essential. Existing tools contain datasets of pre-designed phishing webpages for a limited number of brands, and lack diversity in phishing features. To address these challenges, we develop PhishOracle, a tool that generates adversarial phishing webpages by embedding diverse phishing features into legitimate webpages. We evaluate the robustness of three existing task-specific models - Stack model, VisualPhishNet, and Phishpedia - against PhishOracle-generated adversarial phishing webpages and observe a significant drop in their detection rates. In contrast, a multimodal large language model (MLLM)-based phishing detector demonstrates stronger robustness against these adversarial attacks but still is prone to evasion. Our findings highlight the vulnerability of phishing detection models to adversarial attacks, emphasizing the need for more robust detection approaches. Furthermore, we conduct a user study to evaluate whether PhishOracle-generated adversarial phishing webpages can deceive users. The results show that many of these phishing webpages evade not only existing detection models but also users.

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arXiv2024

Mitigating Bias in Machine Learning Models for Phishing Webpage Detection

The widespread accessibility of the Internet has led to a surge in online fraudulent activities, underscoring the necessity of shielding users' sensitive information from cybercriminals. Phishing, a well-known cyberattack, revolves around the creation of phishing webpages and the dissemination of corresponding URLs, aiming to deceive users into sharing their sensitive information, often for identity theft or financial gain. Various techniques are available for preemptively categorizing zero-day phishing URLs by distilling unique attributes and constructing predictive models. However, these existing techniques encounter unresolved issues. This proposal delves into persistent challenges within phishing detection solutions, particularly concentrated on the preliminary phase of assembling comprehensive datasets, and proposes a potential solution in the form of a tool engineered to alleviate bias in ML models. Such a tool can generate phishing webpages for any given set of legitimate URLs, infusing randomly selected content and visual-based phishing features. Furthermore, we contend that the tool holds the potential to assess the efficacy of existing phishing detection solutions, especially those trained on confined datasets.

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