arXiv subjects

Hassan Habibi Gharakheili

Publications and source records attributed to Hassan Habibi Gharakheili.

At least 19 recordsLinked to original sources

arXiv2026

Semantic Matching of Behavioral Primitives for MUD-Based IoT Device Identification

Accurate identification of Internet of Things (IoT) devices, such as cameras, lightbulbs, and voice assistants, is important for security management and policy enforcement. Existing approaches identify devices from packet- or flow-level traffic characteristics. Manufacturer Usage Description (MUD) profiles provide a complementary, policy-level representation of intended device communication via Access Control Entries (ACEs), which encode protocol, endpoint, direction, and port semantics. While exact ACE matching is effective when observed behavior overlaps with the reference profile, it provides little evidence when that overlap becomes sparse. This paper investigates the semantic matching of individual ACEs as behavioral primitives for MUD-based IoT device identification. Our specific contributions are threefold. (1) Using 28 publicly available MUD profiles containing 1,023 ACE instances, we show that representing individual ACEs preserves behavioral distinctions that are obscured when an entire profile is represented by a single vector. (2) Through controlled experiments with unseen ACEs, endpoint perturbation, and partial observation, we characterize how semantic matching behaves as exact overlap decreases. (3) Using real IoT traffic traces comprising more than 800,000 flows, we show that semantic matching provides stronger evidence during early and sparse-overlap observations, whereas exact matching becomes stronger as stable overlap accumulates. These results demonstrate that semantic ACE matching can complement exact MUD matching when runtime observations provide limited literal overlap with reference profiles. We release the code and research artifacts to support reproducibility.

cs.CR↗

arXiv2026

Assessing Resilience in Authoritative DNS Infrastructure Supporting Government Services

Online government services are increasingly regarded as critical national infrastructure. Because these services directly influence public trust, any disruption can have significant societal and political consequences. Yet their supporting infrastructures remain vulnerable to outages from natural disasters, geopolitical tensions, and targeted attacks. Central to their operation is the authoritative Domain Name System (DNS) infrastructure, the single source of truth that maps government domain names to service endpoints. While indispensable, this infrastructure also represents a potential and critical point of system failure. In this paper, we introduce a comprehensive assessment framework with purpose-designed mechanisms to systematically evaluate the operational resilience of authoritative DNS infrastructure supporting government services. Complementing prior studies on website hosting, recursive resolution, and DNS record integrity, our work provides a holistic view of authoritative DNS operation. Our first contribution develops a multi-sourced data schema that characterizes a (government) domain's authoritative DNS infrastructure across four hierarchical levels: physical hosting infrastructure, server functionality, name servers, and individual hosting instances. Using data collected from six representative countries, our second contribution identifies resilience attributes at their finest applicable hierarchy across three operational phases: infrastructure placement, service configuration, and DNS record dispatch. Our method assigns numerical scores to each attribute and aggregates them algorithmically to enable consistent and cross-domain comparisons. We apply our method to government domains in the six countries, highlighting their strengths and weaknesses in authoritative DNS resilience and pinpointing operational practices that require improvement.

cs.NI↗

arXiv2026

A Survey on Mapping Digital Systems with Bill of Materials: Development, Practices, and Challenges

Modern digital ecosystems, spanning software, hardware, learning models, datasets, and cryptographic products, continue to grow in complexity, making it difficult for organizations to understand and manage component dependencies. Bills of Materials (BOMs) have emerged as a structured way to document product components, their interrelationships, and key metadata, improving visibility and security across digital supply chains. This survey provides the first comprehensive cross-domain review of BOM developments and practices. We start by examining the evolution of BOM frameworks in three stages (i.e., pre-development, initial, and accelerated) and summarizing their core principles, key stakeholders, and standardization efforts for hardware, software, artificial intelligence (AI) models, datasets, and cryptographic assets. We then review industry practices for generating BOM data, evaluating its quality, and securely sharing it. Next, we review practical downstream uses of BOM data, including dependency modeling, compliance verification, operational risk assessment, and vulnerability tracking. We also discuss academic efforts to address limitations in current BOM frameworks through refinements, extensions, or new models tailored to emerging domains such as data ecosystems and AI supply chains. Finally, we identify four key gaps that limit the usability and reliability of today's BOM frameworks, motivating future research directions.

cs.CR↗

arXiv2025

From Flows to Functions: Macroscopic Behavioral Fingerprinting of IoT Devices via Network Services

Identifying devices such as cameras, printers, voice assistants, or health monitoring sensors, collectively known as the Internet of Things (IoT), within a network is a critical operational task, particularly to manage the cyber risks they introduce. While behavioral fingerprinting based on network traffic analysis has shown promise, most existing approaches rely on machine learning (ML) techniques applied to fine-grained features of short-lived traffic units (packets and/or flows). These methods tend to be computationally expensive, sensitive to traffic measurement errors, and often produce opaque inferences. In this paper, we propose a macroscopic, lightweight, and explainable alternative to behavioral fingerprinting focusing on the network services (e.g., TCP/80, UDP/53) that IoT devices use to perform their intended functions over extended periods. Our contributions are threefold. (1) We demonstrate that IoT devices exhibit stable and distinguishable patterns in their use of network services over a period of time. We formalize the notion of service-level fingerprints and derive a generalized method to represent network behaviors using a configurable granularity parameter. (2) We develop a procedure to extract service-level fingerprints, apply it to traffic from 13 consumer IoT device types in a lab testbed, and evaluate the resulting representations in terms of their convergence and recurrence properties. (3) We validate the efficacy of service-level fingerprints for device identification in closed-set and open-set scenarios. Our findings are based on a large dataset comprising about 10 million IPFIX flow records collected over a 1.5-year period.

cs.IR↗

arXiv2024

Unveiling Behavioral Transparency of Protocols Communicated by IoT Networked Assets (Full Version)

Behavioral transparency for Internet-of-Things (IoT) networked assets involves two distinct yet interconnected tasks: (a) characterizing device types by discerning the patterns exhibited in their network traffic, and (b) assessing vulnerabilities they introduce to the network. While identifying communication protocols, particularly at the application layer, plays a vital role in effective network management, current methods are, at best, ad-hoc. Accurate protocol identification and attribute extraction from packet payloads are crucial for distinguishing devices and discovering vulnerabilities. This paper makes three contributions: (1) We process a public dataset to construct specific packet traces pertinent to six standard protocols (TLS, HTTP, DNS, NTP, DHCP, and SSDP) of ten commercial IoT devices. We manually analyze TLS and HTTP flows, highlighting their characteristics, parameters, and adherence to best practices-we make our data publicly available; (2) We develop a common model to describe protocol signatures that help with the systematic analysis of protocols even when communicated through non-standard port numbers; and, (3) We evaluate the efficacy of our data models for the six protocols, which constitute approximately 97% of our dataset. Our data models, except for SSDP in 0.3% of Amazon Echo's flows, produce no false positives for protocol detection. We draw insights into how various IoT devices behave across those protocols by applying these models to our IoT traces.

cs.NI↗

arXiv2023

Realizing Open and Decentralized Marketplace for Exchanging Data of Expected IoT Behaviors

With rising concerns about the security of IoT devices, network operators need better ways to handle potential risks. Luckily, IoT devices show consistent patterns in how they communicate. But despite previous efforts, it remains unclear how knowledge of these patterns can be made available. As data marketplaces become popular in different domains, this paper1 proposes creating a special marketplace focused on IoT cybersecurity. The goal is to openly share knowledge about IoT devices' behavior, using structured data formats like Manufacturer Usage Description (MUD) files. To make this work, we employ technologies like blockchain and smart contracts to build a practical and secure foundation for sharing and accessing important information about how IoT devices should behave on the network. Our contributions are two-fold. (1) We identify the essential features of an effective marketplace for sharing data related to the expected behaviors of IoT devices. We develop a smart contract on the Ethereum blockchain with five concrete functions; and, (2) We implement a prototype of our marketplace in a private chain environment-our codes are publicly released. We demonstrate how effectively our marketplace functions through experiments involving MUD files from consumer IoT devices. Our marketplace enables suppliers and consumers to share MUD data on the Ethereum blockchain for under a hundred dollars, promoting accessibility and participation.

cs.CR↗

arXiv2023

A Survey on Enterprise Network Security: Asset Behavioral Monitoring and Distributed Attack Detection

Enterprise networks that host valuable assets and services are popular and frequent targets of distributed network attacks. In order to cope with the ever-increasing threats, industrial and research communities develop systems and methods to monitor the behaviors of their assets and protect them from critical attacks. In this paper, we systematically survey related research articles and industrial systems to highlight the current status of this arms race in enterprise network security. First, we discuss the taxonomy of distributed network attacks on enterprise assets, including distributed denial-of-service (DDoS) and reconnaissance attacks. Second, we review existing methods in monitoring and classifying network behavior of enterprise hosts to verify their benign activities and isolate potential anomalies. Third, state-of-the-art detection methods for distributed network attacks sourced from external attackers are elaborated, highlighting their merits and bottlenecks. Fourth, as programmable networks and machine learning (ML) techniques are increasingly becoming adopted by the community, their current applications in network security are discussed. Finally, we highlight several research gaps on enterprise network security to inspire future research.

cs.CR↗

arXiv2023

Detecting Anomalous Microflows in IoT Volumetric Attacks via Dynamic Monitoring of MUD Activity

IoT networks are increasingly becoming target of sophisticated new cyber-attacks. Anomaly-based detection methods are promising in finding new attacks, but there are certain practical challenges like false-positive alarms, hard to explain, and difficult to scale cost-effectively. The IETF recent standard called Manufacturer Usage Description (MUD) seems promising to limit the attack surface on IoT devices by formally specifying their intended network behavior. In this paper, we use SDN to enforce and monitor the expected behaviors of each IoT device, and train one-class classifier models to detect volumetric attacks. Our specific contributions are fourfold. (1) We develop a multi-level inferencing model to dynamically detect anomalous patterns in network activity of MUD-compliant traffic flows via SDN telemetry, followed by packet inspection of anomalous flows. This provides enhanced fine-grained visibility into distributed and direct attacks, allowing us to precisely isolate volumetric attacks with microflow (5-tuple) resolution. (2) We collect traffic traces (benign and a variety of volumetric attacks) from network behavior of IoT devices in our lab, generate labeled datasets, and make them available to the public. (3) We prototype a full working system (modules are released as open-source), demonstrates its efficacy in detecting volumetric attacks on several consumer IoT devices with high accuracy while maintaining low false positives, and provides insights into cost and performance of our system. (4) We demonstrate how our models scale in environments with a large number of connected IoTs (with datasets collected from a network of IP cameras in our university campus) by considering various training strategies (per device unit versus per device type), and balancing the accuracy of prediction against the cost of models in terms of size and training time.

cs.CR↗

arXiv2023

Quantifying and Managing Impacts of Concept Drifts on IoT Traffic Inference in Residential ISP Networks

Millions of vulnerable consumer IoT devices in home networks are the enabler for cyber crimes putting user privacy and Internet security at risk. Internet service providers (ISPs) are best poised to play key roles in mitigating risks by automatically inferring active IoT devices per household and notifying users of vulnerable ones. Developing a scalable inference method that can perform robustly across thousands of home networks is a non-trivial task. This paper focuses on the challenges of developing and applying data-driven inference models when labeled data of device behaviors is limited and the distribution of data changes (concept drift) across time and space domains. Our contributions are three-fold: (1) We collect and analyze network traffic of 24 types of consumer IoT devices from 12 real homes over six weeks to highlight the challenge of temporal and spatial concept drifts in network behavior of IoT devices; (2) We analyze the performance of two inference strategies, namely "global inference" (a model trained on a combined set of all labeled data from training homes) and "contextualized inference" (several models each trained on the labeled data from a training home) in the presence of concept drifts; and (3) To manage concept drifts, we develop a method that dynamically applies the ``closest'' model (from a set) to network traffic of unseen homes during the testing phase, yielding better performance in 20% of scenarios.

cs.LG↗

arXiv2022

A Survey on DNS Encryption: Current Development, Malware Misuse, and Inference Techniques

The domain name system (DNS) that maps alphabetic names to numeric Internet Protocol (IP) addresses plays a foundational role for Internet communications. By default, DNS queries and responses are exchanged in unencrypted plaintext, and hence, can be read and/or hijacked by third parties. To protect user privacy, the networking community has proposed standard encryption technologies such as DNS over TLS (DoT), DNS over HTTPS (DoH), and DNS over QUIC (DoQ) for DNS communications, enabling clients to perform secure and private domain name lookups. We survey the DNS encryption literature published since 2016, focusing on its current landscape and how it is misused by malware, and highlighting the existing techniques developed to make inferences from encrypted DNS traffic. First, we provide an overview of various standards developed in the space of DNS encryption and their adoption status, performance, benefits, and security issues. Second, we highlight ways that various malware families can exploit DNS encryption to their advantage for botnet communications and/or data exfiltration. Third, we discuss existing inference methods for profiling normal patterns and/or detecting malicious encrypted DNS traffic. Several directions are presented to motivate future research in enhancing the performance and security of DNS encryption.

cs.CR↗

arXiv2022

AdIoTack: Quantifying and Refining Resilience of Decision Tree Ensemble Inference Models against Adversarial Volumetric Attacks on IoT Networks

Machine Learning-based techniques have shown success in cyber intelligence. However, they are increasingly becoming targets of sophisticated data-driven adversarial attacks resulting in misprediction, eroding their ability to detect threats on network devices. In this paper, we present AdIoTack, a system that highlights vulnerabilities of decision trees against adversarial attacks, helping cybersecurity teams quantify and refine the resilience of their trained models for monitoring IoT networks. To assess the model for the worst-case scenario, AdIoTack performs white-box adversarial learning to launch successful volumetric attacks that decision tree ensemble models cannot flag. Our first contribution is to develop a white-box algorithm that takes a trained decision tree ensemble model and the profile of an intended network-based attack on a victim class as inputs. It then automatically generates recipes that specify certain packets on top of the indented attack packets (less than 15% overhead) that together can bypass the inference model unnoticed. We ensure that the generated attack instances are feasible for launching on IP networks and effective in their volumetric impact. Our second contribution develops a method to monitor the network behavior of connected devices actively, inject adversarial traffic (when feasible) on behalf of a victim IoT device, and successfully launch the intended attack. Our third contribution prototypes AdIoTack and validates its efficacy on a testbed consisting of a handful of real IoT devices monitored by a trained inference model. We demonstrate how the model detects all non-adversarial volumetric attacks on IoT devices while missing many adversarial ones. The fourth contribution develops systematic methods for applying patches to trained decision tree ensemble models, improving their resilience against adversarial volumetric attacks.

cs.LG↗

arXiv2022

Analyzing Enterprise DNS Traffic to Classify Assets and Track Cyber-Health

The Domain Name System (DNS) is a critical service that enables domain names to be converted to IP addresses (or vice versa); consequently, it is generally permitted through enterprise security systems (e.g., firewalls) with little restriction. This has exposed organizational networks to DDoS, exfiltration, and reflection attacks, inflicting significant financial and reputational damage. Large organizations with loosely federated IT departments (e.g., Universities and Research Institutes) often do not even fully aware of all their DNS assets and vulnerabilities, let alone the attack surface they expose to the outside world. In this paper, we address the "DNS blind spot" by developing methods to passively analyze live DNS traffic, identify organizational DNS assets, and monitor their health on a continuous basis. Our contributions are threefold. First, we perform a comprehensive analysis of all DNS traffic in two large organizations (a University Campus and a Government Research Institute) for over a month, and identify key behavioral profiles for various asset types such as recursive resolvers, authoritative name servers, and mixed DNS servers. Second, we develop an unsupervised clustering method that classifies enterprise DNS assets using the behavioral attributes identified, and demonstrate that our method successfully classifies over 100 DNS assets across the two organizations. Third, our method continuously tracks various health metrics across the organizational DNS assets and identifies several instances of improper configuration, data exfiltration, DDoS, and reflection attacks. We believe the passive analysis methods in this paper can help enterprises monitor organizational DNS health in an automated and risk-free manner.

cs.NI↗

arXiv2021

Modeling Live Video Streaming: Real-Time Classification, QoE Inference, and Field Evaluation

Social media, professional sports, and video games are driving rapid growth in live video streaming, on platforms such as Twitch and YouTube Live. Live streaming experience is very susceptible to short-time-scale network congestion since client playback buffers are often no more than a few seconds. Unfortunately, identifying such streams and measuring their QoE for network management is challenging, since content providers largely use the same delivery infrastructure for live and video-on-demand (VoD) streaming, and packet inspection techniques (including SNI/DNS query monitoring) cannot always distinguish between the two. In this paper, we design, build, and deploy ReCLive: a machine learning method for live video detection and QoE measurement based on network-level behavioral characteristics. Our contributions are four-fold: (1) We analyze about 23,000 video streams from Twitch and YouTube, and identify key features in their traffic profile that differentiate live and on-demand streaming. We release our traffic traces as open data to the public; (2) We develop an LSTM-based binary classifier model that distinguishes live from on-demand streams in real-time with over 95% accuracy across providers; (3) We develop a method that estimates QoE metrics of live streaming flows in terms of resolution and buffer stall events with overall accuracies of 93% and 90%, respectively; and (4) Finally, we prototype our solution, train it in the lab, and deploy it in a live ISP network serving more than 7,000 subscribers. Our method provides ISPs with fine-grained visibility into live video streams, enabling them to measure and improve user experience.

cs.NI↗

arXiv2021

Modeling Classroom Occupancy using Data of WiFi Infrastructure in a University Campus

Universities worldwide are experiencing a surge in enrollments, therefore campus estate managers are seeking continuous data on attendance patterns to optimize the usage of classroom space. As a result, there is an increasing trend to measure classrooms attendance by employing various sensing technologies, among which pervasive WiFi infrastructure is seen as a low cost method. In a dense campus environment, the number of connected WiFi users does not well estimate room occupancy since connection counts are polluted by adjoining rooms, outdoor walkways, and network load balancing. In this paper, we develop machine learning based models to infer classroom occupancy from WiFi sensing infrastructure. Our contributions are three-fold: (1) We analyze metadata from a dense and dynamic wireless network comprising of thousands of access points (APs) to draw insights into coverage of APs, behavior of WiFi connected users, and challenges of estimating room occupancy; (2) We propose a method to automatically map APs to classrooms using unsupervised clustering algorithms; and (3) We model classroom occupancy using a combination of classification and regression methods of varying algorithms. We achieve 84.6% accuracy in mapping APs to classrooms while the accuracy of our estimation for room occupancy is comparable to beam counter sensors with a symmetric Mean Absolute Percentage Error (sMAPE) of 13.10%.

eess.SP↗

arXiv2021

Comparing Broadband ISP Performance using Big Data from M-Lab

Comparing ISPs on broadband speed is challenging, since measurements can vary due to subscriber attributes such as operation system and test conditions such as access capacity, server distance, TCP window size, time-of-day, and network segment size. In this paper, we draw inspiration from observational studies in medicine, which face a similar challenge in comparing the effect of treatments on patients with diverse characteristics, and have successfully tackled this using "causal inference" techniques for {\em post facto} analysis of medical records. Our first contribution is to develop a tool to pre-process and visualize the millions of data points in M-Lab at various time- and space-granularities to get preliminary insights on factors affecting broadband performance. Next, we analyze 24 months of data pertaining to twelve ISPs across three countries, and demonstrate that there is observational bias in the data due to disparities amongst ISPs in their attribute distributions. For our third contribution, we apply a multi-variate matching method to identify suitable cohorts that can be compared without bias, which reveals that ISPs are closer in performance than thought before. Our final contribution is to refine our model by developing a method for estimating speed-tier and re-apply matching for comparison of ISP performance. Our results challenge conventional rankings of ISPs, and pave the way towards data-driven approaches for unbiased comparisons of ISPs world-wide.

cs.PF↗

arXiv2020

IoT Network Security: Requirements, Threats, and Countermeasures

IoT devices are increasingly utilized in critical infrastructure, enterprises, and households. There are several sophisticated cyber-attacks that have been reported and many networks have proven vulnerable to both active and passive attacks by leaking private information, allowing unauthorized access, and being open to denial of service attacks. This paper aims firstly, to assist network operators to understand the need for an IoT network security solution, and then secondly, to survey IoT network attack vectors, cyber threats, and countermeasures with a focus on improving the robustness of existing security solutions. Our first contribution highlights viewpoints on IoT security from the perspective of stakeholders such as manufacturers, service providers, consumers, and authorities. We discuss the differences between IoT and IT systems, the need for IoT security solutions, and we highlight the key components required for IoT network security system architecture. For our second contribution, we survey the types of IoT attacks by grouping them based on their impact. We discuss various attack techniques, threats, and shortfalls of existing countermeasures with an intention to enable future research into improving IoT network security.

cs.CR↗

arXiv2020

Optimal Witnessing of Healthcare IoT Data Using Blockchain Logging Contract

Verification of data generated by wearable sensors is increasingly becoming of concern to health service providers and insurance companies. There is a need for a verification framework that various authorities can request a verification service for the local network data of a target IoT device. In this paper, we leverage blockchain as a distributed platform to realize an on-demand verification scheme. This allows authorities to automatically transact with connected devices for witnessing services. A public request is made for witness statements on the data of a target IoT that is transmitted on its local network, and subsequently, devices (in close vicinity of the target IoT) offer witnessing service. Our contributions are threefold: (1) We develop a system architecture based on blockchain and smart contract that enables authorities to dynamically avail a verification service for data of a subject device from a distributed set of witnesses which are willing to provide (in a privacy-preserving manner) their local wireless measurement in exchange of monetary return; (2) We then develop a method to optimally select witnesses in such a way that the verification error is minimized subject to monetary cost constraints; (3) Lastly, we evaluate the efficacy of our scheme using real Wi-Fi session traces collected from a five-storeyed building with more than thirty access points, representative of a hospital. According to the current pricing schedule of the Ethereum public blockchain, our scheme enables healthcare authorities to verify data transmitted from a typical wearable device with the verification error of the order 0.01% at cost of less than two dollars for one-hour witnessing service.

cs.CR↗

arXiv2020

HazeDose: Design and Analysis of a Personal Air Pollution Inhaled Dose Estimation System using Wearable Sensors

Nowadays air pollution becomes one of the biggest world issues in both developing and developed countries. Helping individuals understand their air pollution exposure and health risks, the traditional way is to utilize data from static monitoring stations and estimate air pollution qualities in a large area by government agencies. Data from such sensing system is very sparse and cannot reflect real personal exposure. In recent years, several research groups have developed participatory air pollution sensing systems which use wearable or portable units coupled with smartphones to crowd-source urban air pollution data. These systems have shown remarkable improvement in spatial granularity over government-operated fixed monitoring systems. In this paper, we extend the paradigm to HazeDose system, which can personalize the individuals' air pollution exposure. Specifically, we combine the pollution concentrations obtained from an air pollution estimation system with the activity data from the individual's on-body activity monitors to estimate the personal inhalation dosage of air pollution. Users can visualize their personalized air pollution exposure information via a mobile application. We show that different activities, such as walking, cycling, or driving, impact their dosage, and commuting patterns contribute to a significant proportion of an individual's daily air pollution dosage. Moreover, we propose a dosage minimization algorithm, with the trial results showing that up to 14.1% of a biker's daily exposure can be reduced while using alternative routes the driver can inhale 25.9% less than usual. One heuristic algorithm is also introduced to balance the execution time and dosage reduction for alternative routes scenarios. The results show that up to 20.3% dosage reduction can be achieved when the execution time is almost one seventieth of the original one.

cs.CY↗