Search arXiv⌕ Search
Home/Archive/arXiv

arXiv · 2609.05701

XAI-SDN: An Explainable Entropy-Guided Machine Learning Framework for Real-Time DDoS Detection in Software Defined Networks

Adeel Ahmad·Ali Akarma·Ahmad Ali·Hammad Muneer·Toqeer Ali Syed
View original source ↗Read source document ↗Export metadata ↓

Abstract

One of the biggest risks faced by Software Defined Networks (SDN) is the Distributed Denial of Service (DDoS) attack in which a compromised controller can make an entire network unusable. To address these challenges, we suggest an entropy-guided machine learning framework, called XAI-SDN, for real-time DDoS detection in SDN environments which is lightweight and explainable. The framework extends the flow features extracted by CICFlowMeter with eight Shannon entropy metrics obtained by an $\mathcal{O}(1)$ rolling algorithm and uses a Random Forest classifier with SHAP TreeExplainer for providing transparency at the prediction level. On a fixed temporal split, XAI-SDN achieves an accuracy of 99.9987\%, a macro F1-score of 99.9621\%, and an AUC-ROC of 1.0000 on the full 3.59 million flows of the CIC-DDoS2019 SYN benchmark. The pipeline sustains 0.0165~ms per flow (60{,}606 flows/s) without the use of SHAP and 0.5122~ms per flow (1{,}953 flows/s) with full support of SHAP under the 99.14\% prevalence of DDoS traffic, which is a step towards achieving a balance between the detection performance and operational transparency in next-generation SDN security.

Explore related subjects

cs.CRcs.AIcs.NI

Keep this discovery

BibTeXRIS

Adeel Ahmad, Ali Akarma, Ahmad Ali, Hammad Muneer, Toqeer Ali Syed. 2026-09-04. XAI-SDN: An Explainable Entropy-Guided Machine Learning Framework for Real-Time DDoS Detection in Software Defined Networks. https://arxiv.org/abs/2609.05701

Cite the original work for its findings. Save a collection to share your selection of sources.

Discover connections

cs.AI subjectcs.CR subjectcs.NI subject

Connections use source metadata and explicit phrase matches, not verified experimental comparisons.

KEEP EXPLORING

Related papers

arXiv2026

Identifying AI Web Scrapers Using Canary Tokens

From pre-training to query-time augmentation, web-scraped data helps to improve the quality and contextual relevancy of content generated by large language models (LLMs). However, large-scale web scraping to feed LLMs can affect site stability and raise legal, privacy, or ethics concerns. If website owners wish to limit LLM-related web scraping on their site, due to these or other concerns, they may turn to scraper access control mechanisms like the Robots Exclusion Protocol. To be most effective, such mechanisms require site owners to first identify the scrapers that they wish to restrict (e.g., via User-Agent strings). Existing mechanisms to identify LLM-related scrapers rely on voluntary disclosure by companies, one-off experiments by researchers, or crowd-sourced reports -- methods that are neither reliable nor scalable. This paper proposes a novel technique for accurately and automatically inferring LLM-related scrapers. We host dynamic websites that serve unique canary tokens to each visiting scraper, then prompt LLMs for information about our sites. If an LLM consistently generates outputs containing tokens unique to a scraper, it provides evidence of exposure to that scraper. Via experiments across 22 production LLM systems, we demonstrate that our approach can reliably identify which scrapers feed which LLM, including several that are not publicly known or disclosed by the companies. Our approach provides a promising avenue for unprivileged third parties to infer which scrapers serve data to which LLMs, potentially enabling better control over unwanted scraping.

cs.CR↗
arXiv2026

A Scan-Based Analysis of Internet-Exposed IoT Devices Using Shodan Data

An open measurement problem in IoT security is whether scan-observable network configurations encode population-level exposure risk beyond individual devices. An analysis of internet-exposed IoT endpoints using a controlled multi-country sample from Shodan Search and Shodan InternetDB, selecting 100 hosts identified via TCP port 7547 (TR-069/CWMP) and evenly distributed across the ten most represented countries. Hosts are enriched with scan-derived metadata and analyzed using feature-relevance assessment, cross-country comparisons of open and risky port exposure, and supervised classification of higher-risk exposure profiles. The analysis reveals consistent cross-country differences in exposure structure, with mean risky-port counts ranging from 0.4 to 1.0 per host, and achieves balanced accuracy of approximately 0.61 when classifying higher-risk exposure profiles.

cs.CR↗
arXiv2026

Large Language Models for Agentic NetOps and AIOps: Architectures, Evaluation, and Safety

Large language models (LLMs) are increasingly being used in network operations (NetOps) and artificial intelligence for IT operations (AIOps) for tasks ranging from telemetry retrieval and incident diagnosis to configuration planning and bounded remediation. As these systems acquire greater access to operational tools, the central question is no longer only what an LLM can do, but whether operational assurance increases commensurately with the authority granted to it. This survey examines that question through a structured, evidence-stratified review of agentic NetOps and AIOps. We organise the field around autonomy, tool scope, evidence traces, assurance controls, evaluation, security, and governance, and introduce an operational assurance contract that links each autonomy level to permitted tools, required evidence, independent gates, execution budgets, rollout and rollback duties, and audit requirements. The synthesis reveals a capability--assurance gap: evidence is comparatively strong for read-oriented assistance and tool-grounded diagnosis, but becomes substantially less complete as systems approach configuration change, bounded execution, and closed-loop operation. We therefore argue that evaluation should move beyond static question answering and model accuracy towards workflow-level assessment of evidence quality, tool use, policy and invariant compliance, staged execution, recovery, calibration, cost, and human intervention. We also examine prompt-borne attacks, poisoned or stale operational evidence, excessive agency, privilege boundaries, and weak auditability. Taken together, the survey frames agentic NetOps and AIOps as constrained operational control, in which useful autonomy depends on independently enforced assurance rather than model capability alone.

cs.NI↗