@article{Biblio24,

title = {Building a large, realistic and labeled HTTP URI dataset for anomaly-based intrusion detection systems: Biblio-US17 },

author = {Jesús E. {Díaz-Verdejo} and {Estepa Alonso}, Rafael and {Estepa Alonso}, Antonio and F. J. {Muñoz-Calle} and German {Madinabeitia}},

doi = {https://doi.org/10.1186/s42400‑024‑00336‑3},

issn = {2523-3246},

year  = {2025},

date = {2025-06-05},

urldate = {2025-06-05},

journal = {Cybersecurity},

volume = {8},

number = {35},

abstract = {This paper introduces Biblio-US17, a labeled dataset collected over 6 months from the log files of a popular public website at the University of Seville. It contains 47 million records, each including the method, uniform resource identifier (URI) and associated response code and size of every request received by the web server. Records have been classified as either normal or attack using a comprehensive semi-automated process, which involved signature-based detection, assisted inspection of URIs vocabulary, and substantial expert manual supervision. Unlike comparable datasets, this one offers a genuine real-world perspective on the normal operation of an active website, along with an unbiased proportion of actual attacks (i.e., non-synthetic). This makes it ideal for evaluating and comparing anomalybased approaches in a realistic environment. Its extensive size and duration also make it valuable for addressing challenges like data shift and insufficient training. This paper describes the collection and labeling processes, dataset structure, and most relevant properties. We also include an example of an application for assessing the performance of a simple anomaly detector. Biblio-US17, now available to the scientific community, can also be used to model the URIs used by current web servers.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{LARA2024101427,

title = {Anomaly-based Intrusion Detection System for smart lighting},

author = {Agustín Lara and Antonio Estepa and Rafael Estepa and Jesús E. Díaz-Verdejo and Vicente Mayor},

url = {https://www.sciencedirect.com/science/article/pii/S2542660524003688},

doi = {https://doi.org/10.1016/j.iot.2024.101427},

issn = {2542-6605},

year  = {2024},

date = {2024-01-01},

urldate = {2024-01-01},

journal = {Internet of Things},

volume = {28},

pages = {101427},

abstract = {Smart Lighting Systems (SLS) are essential to smart cities, offering enhanced energy efficiency and public safety. However, they are susceptible to security threats, potentially leading to safety risks and service disruptions, making the protection of this infrastructure critical. This paper presents an anomaly-based Intrusion Detection System (IDS) designed for a real-world operational SLS. As commercial deployments vary in components, protocols, and functionalities, IDSs must be tailored to the specific characteristics of each deployment to perform effectively. Our anomaly-based IDS has been defined based on the properties of the available data and the types of attacks we aim to detect, offering both explainability and low complexity. The proposed system identifies anomalies in seven features of network traffic and in the telemetry data received at the central control (O&M) server. For the latter, we designed three customized detectors to identify abnormal data points, persistent deviations in street lamp power consumption, and abnormal power value based on the time of day. Validation with real-world data and simulated attacks demonstrates the effectiveness of our approach. Network attacks (e.g., DoS, scanning) were detected by at least one of the seven flow-related anomaly detectors, while simulated data poisoning attacks and operational technology (OT) issues were detected with nearly 90% accuracy. The datasets used in this work are publicly available and may serve as reference for the design of future IDSs. While our detectors were designed specifically for our dataset, the variables examined and vulnerabilities addressed are common in most commercial SLSs.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{10.3897/jucs.131686,

title = {A Flexible Multilevel System for Mitre ATT&CK Model-driven Alerts and Events Correlation in Cyberattacks Detection},

author = {Javier Muñoz-Calle and Rafael Estepa Alonso and Antonio Estepa Alonso and Jesús E. Díaz-Verdejo and Elvira Castillo Fernández and Germán Madinabeitia},

url = {https://doi.org/10.3897/jucs.131686},

doi = {10.3897/jucs.131686},

issn = {0948-695X},

year  = {2024},

date = {2024-01-01},

urldate = {2024-01-01},

journal = {JUCS - Journal of Universal Computer Science},

volume = {30},

number = {9},

pages = {1184-1204},

publisher = {Journal of Universal Computer Science},

abstract = {Network monitoring systems can struggle to detect the full sequence of actions in a multi-step cyber attack, frequently resulting in multiple alerts (some of which are false positive (FP)) and missed actions. The challenge of easing the job of security analysts by triggering a single and accurate alert per attack requires developing and evaluating advanced event correlation techniques and models that have the potential to devise relationships between the different observed events/alerts.This work introduces a flexible architecture designed for hierarchical and iterative correlation of alerts and events. Its key feature is the sequential correlation of operations targeting specific attack episodes or aspects. This architecture utilizes IDS alerts or similar cybersecurity sensors, storing events and alerts in a non-relational database. Modules designed for knowledge creation then query these stored items to generate meta-alerts, also stored in the database. This approach facilitates creating a more refined knowledge that can be built on top of existing one by creating specialized modules. For illustrative purposes, we make a case study where we use this architectural approach to explore the feasibility of monitoring the progress of attacks of increased complexity by increasing the levels of the hyperalerts defined, including a case of a multi-step attack that adheres to the ATT&CK model. Although the mapping between the observations and the model components (i.e., techniques and tactics) is challenging, we could fully monitor the progress of two attacks and up to 5 out of 6 steps of the most complex attack by building up to three specialized modules. Despite some limitations due to the sensors and attack scenarios tested, the results indicate the architecture’s potential for enhancing the detection of complex cyber attacks, offering a promising direction for future cybersecurity research.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Lara2023,

title = {Smart home anomaly-based IDS: Architecture proposal and case study},

author = { {Walabonso Lara}, Agustín and Vicente Mayor and {Estepa Alonso}, Rafael and {Estepa Alonso} , Antonio and Jesús E. {Díaz-Verdejo}},

url = {https://linkinghub.elsevier.com/retrieve/pii/S2542660523000963},

doi = {10.1016/J.IOT.2023.100773},

issn = {2542-6605},

year  = {2023},

date = {2023-07-01},

urldate = {2023-07-01},

journal = {Internet of Things},

volume = {22},

pages = {100773},

publisher = {Elsevier},

abstract = {The complexity and diversity of the technologies involved in the Internet of Things (IoT) challenge the generalization of security solutions based on anomaly detection, which should fit the particularities of each context and deployment and allow for performance comparison. In this work, we provide a flexible architecture based on building blocks suited for detecting anomalies in the network traffic and the application-layer data exchanged by IoT devices in the context of Smart Home. Following this architecture, we have defined a particular Intrusion Detector System (IDS) for a case study that uses a public dataset with the electrical consumption of 21 home devices over one year. In particular, we have defined ten Indicators of Compromise (IoC) to detect network attacks and two anomaly detectors to detect false command or data injection attacks. We have also included a signature-based IDS (Snort) to extend the detection range to known attacks. We have reproduced eight network attacks (e.g., DoS, scanning) and four False Command or Data Injection attacks to test our IDS performance. The results show that all attacks were successfully detected by our IoCs and anomaly detectors with a false positive rate lower than 0.3%. Signature detection was able to detect only 4 out of 12 attacks. Our architecture and the IDS developed can be a reference for developing future IDS suited to different contexts or use cases. Given that we use a public dataset, our contribution can also serve as a baseline for comparison with new techniques that improve detection performance.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Diaz-Verdejo2023,

title = {A critical review of the techniques used for anomaly detection of HTTP-based attacks: taxonomy, limitations and open challenges},

author = {Jesús E. Díaz-Verdejo and {Estepa Alonso}, Rafael and {Estepa Alonso}, Antonio and German Madinabeitia},

doi = {10.1016/j.cose.2022.102997},

issn = {01674048},

year  = {2023},

date = {2023-01-01},

urldate = {2023-01-01},

journal = {Computers and Security},

volume = {124},

pages = {102997},

abstract = {Intrusion Detection Systems (IDSs) and Web Application Firewalls (WAFs) offer a crucial layer of defense that allows organizations to detect cyberattacks on their web servers. Academic research overwhelmingly suggests using anomaly detection techniques to improve the performance of these defensive systems. However, analyzing and comparing the wide range of solutions in the scientific literature is challenging since they are typically presented as isolated (unrelated) contributions, and their results cannot be generalized. We believe that this impairs the industry's adoption of academic results and the advancement of research in this field. This paper aims to shed light on the literature on anomaly-based detection of attacks that use HTTP request messages. We define a novel framework for anomaly detection based on six data processing steps grouped into two sequential phases: preprocessing and classification. Based on this framework, we provide a taxonomy and critical review of the techniques surveyed, emphasizing their limitations and applicability. Future approaches should take advantage of the syntax and semantics of the Uniform Resource Locator (URL), be scalable, and address their obsolescence. These aspects are frequently overlooked in the literature and pose a significant challenge in the current era of web services. For better comparability, authors should use adequate public datasets, follow a thorough methodology, and use appropriate metrics that fully show the pros and cons of the approach.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Diaz-Verdejo2022,

title = {On the Detection Capabilities of Signature-Based Intrusion Detection Systems in the Context of Web Attacks},

author = {J. E. Díaz-Verdejo and F. J. Muñoz-Calle and {Estepa Alonso}, A. and {Estepa Alonso}, R. and G. Madinabeitia},

url = {https://www.mdpi.com/2076-3417/12/2/852/htm https://www.mdpi.com/2076-3417/12/2/852},

doi = {10.3390/app12020852},

issn = {20763417},

year  = {2022},

date = {2022-01-01},

urldate = {2022-01-01},

journal = {Applied Sciences},

volume = {12},

number = {2},

pages = {852},

publisher = {Multidisciplinary Digital Publishing Institute},

abstract = {Signature-based Intrusion Detection Systems (SIDS) play a crucial role within the arsenal of security components of most organizations. They can find traces of known attacks in the network traffic or host events for which patterns or signatures have been pre-established. SIDS include standard packages of detection rulesets, but only those rules suited to the operational environment should be activated for optimal performance. However, some organizations might skip this tuning process and instead activate default off-the-shelf rulesets without understanding its implications and trade-offs. In this work, we help gain insight into the consequences of using predefined rulesets in the performance of SIDS. We experimentally explore the performance of three SIDS in the context of web attacks. In particular, we gauge the detection rate obtained with predefined subsets of rules for Snort, ModSecurity and Nemesida using seven attack datasets. We also determine the precision and rate of alert generated by each detector in a real-life case using a large trace from a public webserver. Results show that the maximum detection rate achieved by the SIDS under test is insufficient to protect systems effectively and is lower than expected for known attacks. Our results also indicate that the choice of predefined settings activated on each detector strongly influences its detection capability and false alarm rate. Snort and ModSecurity scored either a very poor detection rate (activating the less-sensitive predefined ruleset) or a very poor precision (activating the full ruleset). We also found that using various SIDS for a cooperative decision can improve the precision or the detection rate, but not both. Consequently, it is necessary to reflect upon the role of these open-source SIDS with default configurations as core elements for protection in the context of web attacks. Finally, we provide an efficient method for systematically determining which rules deactivate from a ruleset to significantly reduce the false alarm rate for a target operational environment. We tested our approach using Snort’s ruleset in our real-life trace, increasing the precision from 0.015 to 1 in less than 16 h of work.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Diaz-Verdejo2020,

title = {A methodology for conducting efficient sanitization of HTTP training datasets},

author = {Jesús E. Díaz-Verdejo and Antonio Estepa and Rafael Estepa and German Madinabeitia and Fco Javier Muñoz-Calle},

url = {https://linkinghub.elsevier.com/retrieve/pii/S0167739X19322629},

doi = {10.1016/j.future.2020.03.033},

issn = {0167739X},

year  = {2020},

date = {2020-08-01},

urldate = {2020-08-01},

journal = {Future Generation Computer Systems},

volume = {109},

pages = {67--82},

publisher = {Elsevier B.V.},

abstract = {The performance of anomaly-based intrusion detection systems depends on the quality of the datasets used to form normal activity profiles. Suitable datasets should include high volumes of real-life data free from attack instances. On account of this requirement, obtaining quality datasets from collected data requires a process of data sanitization that may be prohibitive if done manually, or uncertain if fully automated. In this work, we propose a sanitization approach for obtaining datasets from HTTP traces suited for training, testing, or validating anomaly-based attack detectors. Our methodology has two sequential phases. In the first phase, we clean known attacks from data using a pattern-based approach that relies on tools that detect URI-based known attacks. In the second phase, we complement the result of the first phase by conducting assisted manual labeling systematically and efficiently, setting the focus of expert examination not on the raw data (which would be millions of URIs), but on the set of words that compose the URIs. This dramatically downsizes the volume of data that requires expert discernment, making manual sanitization of large datasets feasible. We have applied our method to sanitize a trace that includes 45 million requests received by the library web server of the University of Seville. We were able to generate clean datasets in less than 84 h with only 33 h of manual supervision. We have also applied our method to some public benchmark datasets, confirming that attacks unnoticed by signature-based detectors can be discovered in a reduced time span.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{2020-howmuch,

title = {How Much Training Data is Enough? A Case Study for HTTP Anomaly-Based Intrusion Detection},

author = {R. Estepa and J. E. Díaz-Verdejo and A. Estepa and G. Madinabeitia},

doi = {10.1109/ACCESS.2020.2977591},

issn = {2169-3536},

year  = {2020},

date = {2020-03-02},

journal = {IEEE Access},

volume = {8},

pages = {44410-44425},

abstract = {Most anomaly-based intrusion detectors rely on models that learn from training datasets whose quality is crucial in their performance. Albeit the properties of suitable datasets have been formulated, the influence of the dataset size on the performance of the anomaly-based detector has received scarce attention so far. In this work, we investigate the optimal size of a training dataset. This size should be large enough so that training data is representative of normal behavior, but after that point, collecting more data may result in unnecessary waste of time and computational resources, not to mention an increased risk of overtraining. In this spirit, we provide a method to find out when the amount of data collected at the production environment is representative of normal behavior in the context of a detector of HTTP URI attacks based on 1-grammar. Our approach is founded on a set of indicators related to the statistical properties of the data. These indicators are periodically calculated during data collection, producing time series that stabilize when more training data is not expected to translate to better system performance, which indicates that data collection can be stopped. We present a case study with real-life datasets collected at the University of Seville (Spain) and a public dataset from the University of Saskatchewan. The application of our method to these datasets showed that more than 42% of one trace, and almost 20% of another were unnecessarily collected, thereby showing that our proposed method can be an efficient approach for collecting training data at the production environment.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Salah2019,

title = {Fusing information from tickets and alerts to improve the incident resolution process},

author = {S. Salah and G. Maciá-Fernández and J. E. Díaz-Verdejo},

doi = {10.1016/j.inffus.2018.01.011},

issn = {15662535},

year  = {2019},

date = {2019-01-01},

journal = {Information Fusion},

volume = {45},

abstract = {textcopyright 2018 Elsevier B.V. In the context of network incident monitoring, alerts are useful notifications that provide IT management staff with information about incidents. They are usually triggered in an automatic manner by network equipment and monitoring systems, thus containing only technical information available to the systems that are generating them. On the other hand, ticketing systems play a different role in this context. Tickets represent the business point of view of incidents. They are usually generated by human intervention and contain enriched semantic information about ongoing and past incidents. In this article, our main hypothesis is that incorporating tickets information into the alert correlation process will be beneficial to the incident resolution life-cycle in terms of accuracy, timing, and overall incident's description. We propose a methodology to validate this hypothesis and suggest a solution to the main challenges that appear. The proposed correlation approach is based on the time alignment of the events (alerts and tickets) that affect common elements in the network. For this we use real alert and ticket datasets obtained from a large telecommunications network. The results have shown that using ticket information enhances the incident resolution process, mainly by reducing and aggregating a higher percentage of alerts compared with standard alert correlation systems that only use alerts as the main source of information. Finally, we also show the applicability and usability of this model by applying it to a case study where we analyze the performance of the management staff.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Khalife2017,

title = {A sampling methodology for DPI classifiers},

author = {Jawad Khalife and Amjad Hajjar and Jesús Díaz-Verdejo},

doi = {10.6138/JIT.2017.18.4.20130525},

issn = {20794029},

year  = {2017},

date = {2017-01-01},

journal = {Journal of Internet Technology},

volume = {18},

number = {4},

pages = {787--800},

abstract = {In this paper we provide a general methodology for customizing sampling schemes used with DPI (Deep Packet inspection) based traffic classifiers. Sampling is supposed to optimize DPI classification by reducing the disclosed payload size for inspection and the associated computational overhead while providing better protection of the users' privacy. As a real case scenario, we choose a real traffic dataset captured on a campus network link on which we conduct a series of classification experiments joint with sampling using OpenDPI, as the DPI tool of choice. First, we attempt to statistically localize payload sections within a flow stream where application signatures are mostly matched by OpenDPI. Then, we specify the minimum required payload to be disclosed for inspection, on a per protocol basis. Finally, we recommend a methodology for generalizing one DPI sampling scheme.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Salah2016,

title = {A Model for Incident Tickets Correlation in Network Management},

author = {Saeed Salah and Gabriel Maciá-Fernández and Jesús E. Díaz-Verdejo and Leovigildo Sánchez-Casado},

doi = {10.1007/s10922-014-9340-6},

issn = {10647570},

year  = {2016},

date = {2016-01-01},

journal = {Journal of Network and Systems Management},

volume = {24},

number = {1},

pages = {57--91},

abstract = {textcopyright 2015, Springer Science+Business Media New York. In Information Technology Service Management (ITSM), network management teams typically use an Incident Ticket System (ITS) as a tool to track, troubleshoot, and coordinate the resolution of network incidents that occur during the daily operation of the network. A well organized ITS may positively impact on the efficiency of the incident management process. Nevertheless, in many cases the handling of tickets by the management team is not completely systematic and may be incoherent and inefficient. This way, irrelevant or redundant tickets for the same incident may be issued, thus creating a redundancy in the system that leads to inefficiencies. In this paper, we suggest a model aimed to correlate redundant tickets in order to reduce the information to a single ticket per incident. We validate the proposed correlation model by evaluating it with two datasets taken from a real ticketing system of a telecommunications network company. Using this model as a basis, we also develop and evaluate a methodology that assesses the efficiency of the management team during the process of tickets creation and management. Based on it, we also get some insights on the performance of the different management groups involved in the ticket creation process. These analyses can be leveraged for improving both the management groups functioning and the policies for the tickets' creation.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Hajjar2015,

title = {Network traffic application identification based on message size analysis},

author = {Amjad Hajjar and Jawad Khalife and Jesús Díaz-Verdejo},

doi = {10.1016/J.JNCA.2015.10.003},

issn = {1084-8045},

year  = {2015},

date = {2015-12-01},

journal = {Journal of Network and Computer Applications},

volume = {58},

number = {2010},

pages = {130--143},

publisher = {Academic Press},

abstract = {Identifying network applications is centric to many network management and security tasks. A large number of approaches exist in the literature, most of which are based on statistical and machine learning techniques. For protecting the user privacy, the majority of the existing methods rely on discriminative traffic attributes at the network and transport layers, such as interaction schemes, packet sizes and inter-arrival times. In this work, we propose a novel blind, quintuple centric approach by exploring traffic attributes at the application level without inspecting the payloads. The identification model is based on the analysis of the first application-layer messages in a flow (quintuple), based on their sizes, directions and positions in the flow. The underlying idea is that the first messages of a flow usually carry some application level signaling and data transfer units (command, request, response, etc.) that can be discriminative through their patterns of size and direction. A Gaussian mixture model is proposed to characterize the applications, based on a study of the common characteristics of application-level protocols. The blind classifier is based on Markov models with low complexity and reasonable computational requirements, where the training procedure consists of profiling the target applications separately. Promising results were obtained for some popular protocols including many peer-to-peer applications.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Garcia-Teodoro2015,

title = {Automatic generation of HTTP intrusion signatures by selective identification of anomalies},

author = {P. Garcia-Teodoro and J. E. Diaz-Verdejo and J. E. Tapiador and R. Salazar-Hernandez},

doi = {10.1016/j.cose.2015.09.007},

issn = {01674048},

year  = {2015},

date = {2015-01-01},

journal = {Computers and Security},

volume = {55},

pages = {159--174},

abstract = {In this paper, we introduce a novel methodology to automatically generate HTTP intrusion signatures for Network Intrusion Detection Systems (NIDS). Our approach relies on the use of a service-specific, semantic-aware anomaly detection scheme that combines stochastic learning with a model structure based on the protocol specification. Each incoming payload for the target service is tagged with an anomaly score obtained from probabilistically matching it against the corresponding learned model of normal usage. For those payloads whose anomaly score exceeds a given threshold, a more detailed analysis is performed to extract the portions that contribute the most to the anomaly score. Such portions are then used to build up candidate intrusion signatures, using a merging process that combines them with already existing patterns in order to keep the signature database as simple as possible by avoiding redundancies. We report results obtained with a specific implementation of our proposal for web traffic. During our evaluation, we used a well-known signature-based NIDS that sits behind the anomaly detection system and is fed with the signatures automatically generated by the latter. Our results indicate that functioning in such a way translates into an improvement of the often tedious signature generation process. Furthermore, a visual inspection of the signatures reveals that the generation procedure is quite reliable, mimicking (and, in some cases, even improving) attack patterns manually generated by security analysts. This results in an increase of the overall detection performance of the composite signature- plus anomaly-based system.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Khalife2014,

title = {A multilevel taxonomy and requirements for an optimal traffic- classification model},

author = {Jawad Khalife and Amjad Hajjar and Jesus Diaz-Verdejo},

doi = {10.1002/nem.1855},

issn = {10991190},

year  = {2014},

date = {2014-01-01},

journal = {International Journal of Network Management},

volume = {24},

number = {2},

pages = {101--120},

abstract = {Identifying Internet traffic applications is essential for network security and management. The steady emergence of new Internet applications, together with the use of encryption and obfuscation techniques, ensures that traffic classification remains a hot research topic. Much research has been devoted to this topic by the research community in the last decade. However, an optimal traffic classification model has yet to be defined. Many techniques and formats have been described, with the current literature therefore lacking appropriate benchmarks expressed in a consistent terminology. Moreover, existing surveys are outdated and do not include many recent advances in the field. In this article, we present a systematic multilevel taxonomy that covers a broad range of existing and recently proposed methods, together with examples of vendor classification techniques. Our taxonomy assists in defining a consistent terminology. It could be useful in future benchmarking contexts by characterizing and comparing methods at three different levels. From this perspective, we describe key features and provide design hints for future classification models, while emphasizing the main requirements for promoting future research efforts. To motivate researchers and other interested parties, we collect and share data captured from real traffic, using two models to protect data privacy. Copyright textcopyright 2014 John Wiley & Sons, Ltd. With the huge amount of recently emergent papers in traffic classification, existing surveys cannot reflect current advances and trends in the field. In this article, we propose a multilevel taxonomy categorising and characterizing most existing methods at three different levels, which is vital for future benchmarks. We show comparisons, highlight on current research trends and describe the optimal future classifier's features. From the perspective of our taxonomy, we illuminate on research requirements both on the policy and technical levels. Copyright textcopyright 2014 John Wiley & Sons, Ltd.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Padilla2013,

title = {Erratum: On the influence of the propagation channel in the performance of energy-efficient geographic routing algorithms for wireless sensor networks (WSN) (Wireless Personal Communications DOI: 10.1007/s11277-012-0676-5)},

author = {P. Padilla and J. Camacho and G. Maciá-Fernández and J. E. Díaz-Verdejo and P. García-Teodoro and C. Gómez-Calero},

doi = {10.1007/s11277-012-0719-y},

issn = {09296212},

year  = {2013},

date = {2013-01-01},

journal = {Wireless Personal Communications},

volume = {70},

number = {1},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Padilla2013a,

title = {On the influence of the propagation channel in the performance of energy-efficient geographic routing algorithms for Wireless Sensor Networks (WSN)},

author = {P. Padilla and J. Camacho and G. Maciá-Fernández and J. E. Díaz-Verdejo and P. García-Teodoro and C. Gómez-Calero},

doi = {10.1007/s11277-012-0676-5},

issn = {09296212},

year  = {2013},

date = {2013-01-01},

journal = {Wireless Personal Communications},

volume = {70},

number = {1},

abstract = {In this paper, the influence of the features of the propagation channel in the performance of energy-efficient routing algorithms for wireless sensor networks is studied. Although there are a lot of works regarding energy-efficient routing protocols, almost no reference to realistic propagation channel models and influence is made in the literature. Considering that the propagation channel may affect the efficiency of the different energy-efficient routing algorithms, different propagation scenarios are proposed in this work, from the most simplistic free-space propagation model to more complex ones. The latter includes the effects of multipath propagation, shadowing, fading, etc. In addition, spatial diversity transmission/reception models are considered to mitigate the effects of hard propagation fading. Some results are provided comparing the performance of several energy-efficient routing algorithms in different scenarios. textcopyright 2012 Springer Science+Business Media, LLC.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Camacho2013,

title = {A generalizable dynamic flow pairing method for traffic classification},

author = {J. Camacho and P. Padilla and P. García-Teodoro and J. Díaz-Verdejo},

doi = {10.1016/j.comnet.2013.06.006},

issn = {13891286},

year  = {2013},

date = {2013-01-01},

journal = {Computer Networks},

volume = {57},

number = {14},

abstract = {The goal of network traffic classification is to identify the protocols or types of protocols in the network traffic. In particular, the identification of network traffic with high resource consumption, such as peer-to-peer (P2P) traffic, represents a great concern for Internet Service Providers (ISP) and network managers. Most current flow-based classification approaches report high accuracy without paying attention to the generalization ability of the classifier. However, without this ability, a classifier may not be suitable for on-line classification. In this paper, a number of experiments on real traffic help to elucidate the reason for this lack of generalization. It is also shown that one way to attain the generalization ability is by using dynamic classifiers. From these results, a dynamic classification approach based on the pairing of flows according to a similarity criterion is proposed. The pairing method is not a classifier by itself. Rather, its goal is to determine in a fast way that two given flows are similar enough to conclude they correspond to the same protocol. Combining this method with a classifier, most of the flows do not need to be explicitly evaluated by the later, so that the computational overhead is reduced without a significant reduction in accuracy. In this paper, as a case study, we explore complementing the pairing method with payload inspection. In the experiments performed, the pairing approach generalizes well to traffic obtained in different conditions and scenarios than that used for calibration. Moreover, a high portion of the traffic unclassified by payload inspection is categorized with the pairing method. textcopyright 2013 Elsevier B.V. All rights reserved.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Salah2013,

title = {A model-based survey of alert correlation techniques},

author = {S. Salah and G. Maciá-Fernández and J. E. Díaz-Verdejo},

doi = {10.1016/j.comnet.2012.10.022},

issn = {13891286},

year  = {2013},

date = {2013-01-01},

journal = {Computer Networks},

volume = {57},

number = {5},

abstract = {As telecommunication networks evolve rapidly in terms of scalability, complexity, and heterogeneity, the efficiency of fault localization procedures and the accuracy in the detection of anomalous behaviors are becoming important factors that largely influence the decision making process in large management companies. For this reason, telecommunication companies are doing a big effort investing in new technologies and projects aimed at finding efficient management solutions. One of the challenging issues for network and system management operators is that of dealing with the huge amount of alerts generated by the managed systems and networks. In order to discover anomalous behaviors and speed up fault localization processes, alert correlation is one of the most popular resources. Although many different alert correlation techniques have been investigated, it is still an active research field. In this paper, a survey of the state of the art in alert correlation techniques is presented. Unlike other authors, we consider that the correlation process is a common problem for different fields in the industry. Thus, we focus on showing the broad influence of this problem. Additionally, we suggest an alert correlation architecture capable of modeling current and prospective proposals. Finally, we also review some of the most important commercial products currently available. textcopyright 2012 Elsevier B.V. All rights reserved.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Khalife2013,

title = {Performance of OpenDPI in identifying sampled network traffic},

author = {Jawad Khalife and Amjad Hajjar and Jesús Díaz-Verdejo},

doi = {10.4304/jnw.8.1.71-81},

issn = {17962056},

year  = {2013},

date = {2013-01-01},

journal = {Journal of Networks},

volume = {8},

number = {1},

pages = {71--81},

abstract = {The identification of the nature of the traffic flowing through a TCP/IP network is a relevant target for traffic engineering and security related tasks. Despite the privacy concerns it arises, Deep Packet Inspection (DPI) is one of the most successful current techniques. Nevertheless, the performance of DPI is strongly limited by computational issues related to the huge amount of data it needs to handle, both in terms of number of packets and the length of the packets. One way to reduce the computational overhead with identification techniques is to sample the traffic being monitored. This paper addresses the sensitivity of OpenDPI, one of the most powerful freely available DPI systems, with sampled network traffic. Two sampling techniques are applied and compared: the per-packet payload sampling, and the per-flow packet sampling. Based on the obtained results, some conclusions are drawn to show how far DPI methods could be optimised through traffic sampling. textcopyright2013 ACADEMY PUBLISHER.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Salcedo-Campos2012,

title = {Segmental parameterisation and statistical modelling of e-mail headers for spam detection},

author = {Francisco Salcedo-Campos and Jesús Díaz-Verdejo and Pedro García-Teodoro},

doi = {10.1016/j.ins.2012.01.022},

issn = {0020-0255},

year  = {2012},

date = {2012-07-01},

journal = {Information Sciences},

volume = {195},

pages = {45--61},

publisher = {Elsevier},

abstract = {'Spammers exploit the popularity and low cost of e-mail services to send unsolicited messages (spam), which fill users' accounts and waste valuable resources. To combat this problem, many different spam filtering techniques have been proposed in the literature. Nevertheless, most current anti-spamming filtering schemes are based on detecting relevant terms or tokens in the entire message or in only the body, which implies an invasion of users' privacy. In this paper, a novel spam-filtering technique based solely on the information present in headers is introduced. In this approach, headers are considered as the result of a dynamic process that generates characters. The observed characters are treated as signals and parameterised in accordance with standard signal pre-processing techniques by extracting relevant parameters from the header. From this, Hidden Markov Models (HMMs) are considered for a spam detection system. The performance achieved by our proposal is evaluated and compared with that of other pattern classification paradigms used for spam filtering. The experimental results for SpamAssassin, TREC05 and CEAS 2008 Lab Evaluation improve on those results obtained with other widely used techniques, achieving up to 98.42% of spam detection while keeping the false positive rate below 0.4% and with the added advantages of using only information from the headers and being independent of the language in which the e-mail is written. textcopyright 2012 Elsevier Inc. All rights reserved.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Camacho2011c,

title = {Least-squares approximation of a space distribution for a given covariance and latent sub-space},

author = {José Camacho and Pablo Padilla and Jesús Díaz-Verdejo and Keith Smith and David Lovett},

doi = {10.1016/j.chemolab.2010.12.005},

issn = {0169-7439},

year  = {2011},

date = {2011-02-01},

journal = {Chemometrics and Intelligent Laboratory Systems},

volume = {105},

number = {2},

pages = {171--180},

publisher = {Elsevier},

abstract = {In this paper, a new method to approximate a data set by another data set with constrained covariance matrix is proposed. The method is termed Approximation of a DIstribution for a given COVariance (ADICOV). The approximation is solved in any projection subspace, including that of Principal Component Analysis (PCA) and Partial Least Squares (PLS). Given the direct relationship between covariance matrices and projection models, ADICOV is useful to test whether a data set satisfies the covariance structure in a projection model. This idea is broadly applicable in chemometrics. Also, ADICOV can be used to simulate data with a specific covariance structure and data distribution. Some applications are illustrated in an industrial case of study. textcopyright 2010 Elsevier B.V.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}