arXiv subjects

Nimesha Wickramasinghe

Publications and source records attributed to Nimesha Wickramasinghe.

4 recordsLinked to original sources

arXiv2026

Mind the Gap: Policy vs Reality in Post-Quantum TLS Deployment

Post-quantum cryptography (PQC) has evolved from a long-term planning concern into an operational priority. Following NIST's standardization of PQC, governments and standard bodies published transition roadmaps outlining migration timelines, priority sectors, and deployment strategies. However, our survey of these policies reveals substantial divergence in technical prescriptions and urgency. It remains unclear how widely PQC has been adopted in practice and how policy differences translate into observable deployment outcomes. To address this gap, we present the first longitudinal measurement study of post-quantum TLS (PQ-TLS) adoption. By establishing more than 2 billion TLS handshakes, we analyze cryptographic negotiation behavior across 1 million domains from 11 globally distributed vantage points. Despite varied policy guidance, we observe configuration convergence: PQ-TLS deployment overwhelmingly centers on a single hybrid construction, and much of the apparent progress is driven by managed infrastructure providers. National timelines and sectoral priorities show limited correspondence with observed deployment patterns. Contrary to early experimental studies suggesting measurable overhead, we find that PQ-TLS introduces no meaningful latency increase in Internet settings, although it is frequently deployed alongside legacy TLS configurations. Together, these findings highlight a gap between policy expectations and early deployment reality, and provide empirical insight to inform more grounded PQ-TLS transition.

cs.NI↗

arXiv2025

SoK: Decoding the Enigma of Encrypted Network Traffic Classifiers

The adoption of modern encryption protocols such as TLS 1.3 has significantly challenged traditional network traffic classification (NTC) methods. As a consequence, researchers are increasingly turning to machine learning (ML) approaches to overcome these obstacles. In this paper, we comprehensively analyze ML-based NTC studies, developing a taxonomy of their design choices, benchmarking suites, and prevalent assumptions impacting classifier performance. Through this systematization, we demonstrate widespread reliance on outdated datasets, oversights in design choices, and the consequences of unsubstantiated assumptions. Our evaluation reveals that the majority of proposed encrypted traffic classifiers have mistakenly utilized unencrypted traffic due to the use of legacy datasets. Furthermore, by conducting 348 feature occlusion experiments on state-of-the-art classifiers, we show how oversights in NTC design choices lead to overfitting, and validate or refute prevailing assumptions with empirical evidence. By highlighting lessons learned, we offer strategic insights, identify emerging research directions, and recommend best practices to support the development of real-world applicable NTC methodologies.

cs.CR↗

arXiv2025

Less is More: Simplifying Network Traffic Classification Leveraging RFCs

The rapid growth of encryption has significantly enhanced privacy and security while posing challenges for network traffic classification. Recent approaches address these challenges by transforming network traffic into text or image formats to leverage deep-learning models originally designed for natural language processing, and computer vision. However, these transformations often contradict network protocol specifications, introduce noisy features, and result in resource-intensive processes. To overcome these limitations, we propose NetMatrix, a minimalistic tabular representation of network traffic that eliminates noisy attributes and focuses on meaningful features leveraging RFCs (Request for Comments) definitions. By combining NetMatrix with a vanilla XGBoost classifier, we implement a lightweight approach, LiM ("Less is More") that achieves classification performance on par with state-of-the-art methods such as ET-BERT and YaTC. Compared to selected baselines, experimental evaluations demonstrate that LiM improves resource consumption by orders of magnitude. Overall, this study underscores the effectiveness of simplicity in traffic representation and machine learning model selection, paving the way towards resource-efficient network traffic classification.

cs.CR↗

arXiv2021

Uncovering IP Address Hosting Types Behind Malicious Websites

Hundreds of thousands of malicious domains are created everyday. These malicious domains are hosted on a wide variety of network infrastructures. Traditionally, attackers utilize bullet proof hosting services (e.g. MaxiDed, Cyber Bunker) to take advantage of relatively lenient policies on what content they can host. However, these IP ranges are increasingly being blocked or the services are taken down by law enforcement. Hence, attackers are moving towards utilizing IPs from regular hosting providers while staying under the radar of these hosting providers. There are several practical advantages of accurately knowing the type of IP used to host malicious domains. If the IP is a dedicated IP (i.e. it is leased to a single entity), one may blacklist the IP to block domains hosted on those IPs as welll as use as a way to identify other malicious domains hosted the same IP. If the IP is a shared hosting IP, hosting providers may take measures to clean up such domains and maintain a high reputation for their users.

cs.CR↗