Attacks against intrusion detection networks: evasion, reverse engineering and optimal countermeasures

Intrusion Detection Networks (IDNs) constitute a primary element in current cyberdefense systems. IDNs are composed of different nodes distributed among a network infrastructure, performing functions such as local detection --mostly by Intrusion Detection Systems (IDS) --, information sharing with other nodes in the IDN, and aggregation and correlation of data from different sources. Overall, they are able to detect distributed attacks taking place at large scale or in different parts of the network simultaneously. IDNs have become themselves target of advanced cyberattacks aimed at bypassing the security barrier they offer and thus gaining control of the protected system. In order to guarantee the security and privacy of the systems being protected and the IDN itself, it is required to design resilient architectures for IDNs capable of maintaining a minimum level of functionality even when certain IDN nodes are bypassed, compromised, or rendered unusable. Research in this field has traditionally focused on designing robust detection algorithms for IDS. However, almost no attention has been paid to analyzing the security of the overall IDN and designing robust architectures for them. This Thesis provides various contributions in the research of resilient IDNs grouped into two main blocks. The first two contributions analyze the security of current proposals for IDS nodes against specific attacks, while the third and fourth contributions provide mechanisms to design IDN architectures that remain resilient in the presence of adversaries. In the first contribution, we propose evasion and reverse engineering attacks to anomaly detectors that use classification algorithms at the core of the detection engine. These algorithms have been widely studied in the anomaly detection field, as they generally are claimed to be both effective and efficient. However, such anomaly detectors do not consider potential behaviors incurred by adversaries to decrease the effectiveness and efficiency of the detection process. We demonstrate that using well-known classification algorithms for intrusion detection is vulnerable to reverse engineering and evasion attacks, which makes these algorithms inappropriate for real systems. The second contribution discusses the security of randomization as a countermeasure to evasion attacks against anomaly detectors. Recent works have proposed the use of secret (random) information to hide the detection surface, thus making evasion harder for an adversary. We propose a reverse engineering attack using a query-response analysis showing that randomization does not provide such security. We demonstrate our attack on Anagram, a popular application-layer anomaly detector based on randomized n-gram analysis. We show how an adversary can _rst discover the secret information used by the detector by querying it with carefully constructed payloads and then use this information to evade the detector. The difficulties found to properly address the security of nodes in an IDN motivate our research to protect cyberdefense systems globally, assuming the possibility of attacks against some nodes and devising ways of allocating countermeasures optimally. In order to do so, it is essential to model both IDN nodes and adversarial capabilities. In the third contribution of this Thesis, we provide a conceptual model for IDNs viewed as a network of nodes whose connections and internal components determine the architecture and functionality of the global defense network. Such a model is based on the analysis and abstraction of a number of existing proposals for IDNs. Furthermore, we also develop an adversarial model for IDNs that builds on classical attack capabilities for communication networks and allow to specify complex attacks against IDN nodes. Finally, the fourth contribution of this Thesis presents DEFIDNET, a framework to assess the vulnerabilities of IDNs, the threats to which they are exposed, and optimal countermeasures to minimize risk considering possible economic and operational constraints. The framework uses the system and adversarial models developed earlier in this Thesis, together with a risk rating procedure that evaluates the propagation of attacks against particular nodes throughout the entire IDN and estimates the impacts of such actions according to different attack strategies. This assessment is then used to search for countermeasures that are both optimal in terms of involved cost and amount of mitigated risk. This is done using multi-objective optimization algorithms, thus offering the analyst sets of solutions that could be applied in different operational scenarios. -------------------------------------------------------------Las Redes de Detección de Intrusiones (IDNs, por sus siglas en inglés) constituyen un elemento primordial de los actuales sistemas de ciberdefensa. Una IDN está compuesta por diferentes nodos distribuidos a lo largo de una infraestructura de red que realizan funciones de detección de ataques --fundamentalmente a través de Sistemas de Detección de Intrusiones, o IDS--, intercambio de información con otros nodos de la IDN, y agregación y correlación de eventos procedentes de distintas fuentes. En conjunto, una IDN es capaz de detectar ataques distribuidos y de gran escala que se manifiestan en diferentes partes de la red simultáneamente. Las IDNs se han convertido en objeto de ataques avanzados cuyo fin es evadir las funciones de seguridad que ofrecen y ganar así control sobre los sistemas protegidos. Con objeto de garantizar la seguridad y privacidad de la infraestructura de red y de la IDN, es necesario diseñar arquitecturas resilientes para IDNs que sean capaces de mantener un nivel mínimo de funcionalidad incluso cuando ciertos nodos son evadidos, comprometidos o inutilizados. La investigación en este campo se ha centrado tradicionalmente en el diseño de algoritmos de detección robustos para IDS. Sin embargo, la seguridad global de la IDN ha recibido considerablemente menos atención, lo que ha resultado en una carencia de principios de diseño para arquitecturas de IDN resilientes. Esta Tesis Doctoral proporciona varias contribuciones en la investigación de IDN resilientes. La investigación aquí presentada se agrupa en dos grandes bloques. Por un lado, las dos primeras contribuciones proporcionan técnicas de análisis de la seguridad de nodos IDS contra ataques deliberados. Por otro lado, las contribuciones tres y cuatro presentan mecanismos de diseño de arquitecturas IDS robustas frente a adversarios. En la primera contribución se proponen ataques de evasión e ingeniería inversa sobre detectores de anomalíaas que utilizan algoritmos de clasificación en el motor de detección. Estos algoritmos han sido ampliamente estudiados en el campo de la detección de anomalías y son generalmente considerados efectivos y eficientes. A pesar de esto, los detectores de anomalías no consideran el papel que un adversario puede desempeñar si persigue activamente decrementar la efectividad o la eficiencia del proceso de detección. En esta Tesis se demuestra que el uso de algoritmos de clasificación simples para la detección de anomalías es, en general, vulnerable a ataques de ingeniería inversa y evasión, lo que convierte a estos algoritmos en inapropiados para sistemas reales. La segunda contribución analiza la seguridad de la aleatorización como contramedida frente a los ataques de evasión contra detectores de anomalías. Esta contramedida ha sido propuesta recientemente como mecanismo de ocultación de la superficie de decisión, lo que supuestamente dificulta la tarea del adversario. En esta Tesis se propone un ataque de ingeniería inversa basado en un análisis consulta-respuesta que demuestra que, en general, la aleatorización no proporciona un nivel de seguridad sustancialmente superior. El ataque se demuestra contra Anagram, un detector de anomalías muy popular basado en el análisis de n-gramas que opera en la capa de aplicación. El ataque permite a un adversario descubrir la información secreta utilizada durante la aleatorización mediante la construcción de paquetes cuidadosamente diseñados. Tras la finalización de este proceso, el adversario se encuentra en disposición de lanzar un ataque de evasión. Los trabajos descritos anteriormente motivan la investigación de técnicas que permitan proteger sistemas de ciberdefensa tales como una IDN incluso cuando la seguridad de algunos de sus nodos se ve comprometida, así como soluciones para la asignación óptima de contramedidas. Para ello, resulta esencial disponer de modelos tanto de los nodos de una IDN como de las capacidades del adversario. En la tercera contribución de esta Tesis se proporcionan modelos conceptuales para ambos elementos. El modelo de sistema permite representar una IDN como una red de nodos cuyas conexiones y componentes internos determinan la arquitectura y funcionalidad de la red global de defensa. Este modelo se basa en el análisis y abstracción de diferentes arquitecturas para IDNs propuestas en los últimos años. Asimismo, se desarrolla un modelo de adversario para IDNs basado en las capacidades clásicas de un atacante en redes de comunicaciones que permite especificar ataques complejos contra nodos de una IDN. Finalmente, la cuarta y última contribución de esta Tesis Doctoral describe DEFIDNET, un marco que permite evaluar las vulnerabilidades de una IDN, las amenazas a las que están expuestas y las contramedidas que permiten minimizar el riesgo de manera óptima considerando restricciones de naturaleza económica u operacional. DEFIDNET se basa en los modelos de sistema y adversario desarrollados anteriormente en esta Tesis, junto con un procedimiento de evaluación de riesgos que permite calcular la propagación a lo largo de la IDN de ataques contra nodos individuales y estimar el impacto de acuerdo a diversas estrategias de ataque. El resultado del análisis de riesgos es utilizado para determinar contramedidas óptimas tanto en términos de coste involucrado como de cantidad de riesgo mitigado. Este proceso hace uso de algoritmos de optimización multiobjetivo y ofrece al analista varios conjuntos de soluciones que podrían aplicarse en distintos escenarios operacionales.Programa en Ciencia y Tecnología InformáticaPresidente: Andrés Marín López; Vocal: Sevil Sen; Secretario: David Camacho Fernánde

A methodology for large-scale identification of related accounts in underground forums

Underground forums allow users to interact with communities focused on illicit activities. They serve as an entry point for actors interested in deviant and criminal topics. Due to the pseudo-anonymity provided, they have become improvised marketplaces for trading illegal products and services, including those used to conduct cyberattacks. Thus, these forums are an important data source for threat intelligence analysts and law enforcement. The use of multiple accounts is forbidden in most forums since these are mostly used for malicious purposes. Still, this is a common practice. Being able to identify an actor or gang behind multiple accounts allows for proper attribution in online investigations, and also to design intervention mechanisms for illegal activities. Existing solutions for multi-account detection either require ground truth data to conduct supervised classification or use manual approaches. In this work, we propose a methodology for the large-scale identification of related accounts in underground forums. These accounts are similar according to the distinctive content posted, and thus are likely to belong to the same actor or group. The methodology applies to various domains and leverages distinctive artefacts and personal information left online by the users. We provide experimental results on a large dataset comprising more than 1.1M user accounts from 15 different forums. We show how this methodology, combined with existing approaches commonly used in social media forensics, can assist with and improve online investigations.This work was partially supported by CERN openlab, the CERN Doctoral Student Programme, the Spanish grants ODIO (PID2019-111429RB-C21 and PID2019-111429RB) and the Region of Madrid grant CYNAMON-CM (P2018/TCS-4566), co-financed by European Structural Funds ESF and FEDER, and Excellence Program EPUC3M1

Displacing big data: How criminals cheat the system

Abstract: Many technical approaches for detecting and preventing cy-bercrime utilise big data and machine learning, drawing upon knowledgeabout the behaviour of legitimate customers and indicators of cyber-crime. These include fraud detection systems, behavioural analysis, spamdetection, intrusion detection systems, anti-virus software, and denial ofservice attack protection. However, criminals have adapted their meth-ods in response to big data systems. We present case studies for a numberof different cybercrime types to highlight the methods used for cheatingsuch systems. We argue that big data solutions are not a silver bulletapproach to disrupting cybercrime, but rather represent a Red Queen'srace, requiring constant running to stay in one spot

An analysis of fake social media engagement services

Fake engagement services allow users of online social media and other web platforms to illegitimately increase their online reach and boost their perceived popularity. Driven by socio-economic and even political motivations, the demand for fake engagement services has increased in the last years, which has incentivized the rise of a vast underground market and support infrastructure. Prior research in this area has been limited to the study of the infrastructure used to provide these services (e.g., botnets) and to the development of algorithms to detect and remove fake activity in online targeted platforms. Yet, the platforms in which these services are sold (known as panels) and the underground markets offering these services have not received much research attention. To fill this knowledge gap, this paper studies Social Media Management (SMM) panels, i.e., reselling platforms¿often found in underground forums¿in which a large variety of fake engagement services are offered. By daily crawling 86 representative SMM panels for 4 months, we harvest a dataset with 2.8 M forum entries grouped into 61k different services. This dataset allows us to build a detailed catalog of the services for sale, the platforms they target, and to derive new insights on fake social engagement services and its market. We then perform an economic analysis of fake engagement services and their trading activities by automatically analyzing 7k threads in underground forums. Our analysis reveals a broad range of offered services and levels of customization, where buyers can acquire fake engagement services by selecting features such as the quality of the service, the speed of delivery, the country of origin, and even personal attributes of the fake account (e.g., gender). The price analysis also yields interesting empirical results, showing significant disparities between prices of the same product across different markets. These observations suggest that the market is still undeveloped and sellers do not know the real market value of the services that they offer, leading them to underprice or overprice their services.This work was supported by the EU Horizon 2020 Research and Innovation Program under Grant agreement no. 101021377 (TRUST aWARE ); the Spanish grants ODIO (PID2019-111429RB-C21 and PID2019-111429RB-C22), and the Region of Madrid grant CYNAMON-CM (P2018/TCS-4566), co-financed by European Structural Funds ESF and FEDER

Randomized Anagram Revisited

When compared to signature-based Intrusion Detection Systems (IDS), anomaly detectors present the potential advantage of detecting previously unseen attacks, which makes them an attractive solution against zero-day exploits and other attacks for which a signature is unavailable. Most anomaly detectors rely on machine learning algorithms to derive a model of normality that is later used to detect suspicious events. Such algorithms, however, are generally susceptible to evasion by means of carefully constructed attacks that are not recognized as anomalous. Different strategies to thwart evasion have been proposed over the last years, including the use of randomization to make somewhat uncertain how each packet will be processed. In this paper we analyze the strength of the randomization strategy suggested for Anagram, a well-known anomaly detector based on n-gram models. We show that an adversary who can interact with the system for a short period of time with inputs of his choosing will be able to recover the secret mask used to process packets. We describe and discuss an efficient algorithm to do this and report our experiences with a prototype implementation. Furthermore, we show that the specific form of randomization suggested for Anagram is a double-edged sword, as knowledge of the mask makes evasion easier than in the non-randomized case. We finally discuss a simple countermeasure to prevent our attacks.Publicad

A nested decision tree for event detection in smart grids

Procedings of: 20th International Conference on Renewable Energies and Power Quality (ICREPQ'22), 27-29 July 2022, Vigo, Spain.Digitalization process experienced by traditional power networks towards smart grids extend the challenges faced by power grid operators to the field of cybersecurity. False data injection attacks, one of the most common cyberattacks in smart grids, could lead the power grid to sabotage itself. In this paper, an event detection algorithm for cyberattack in smart grids is developed based on a decision tree. In order to find the most accurate algorithm, two different decision trees with two different goals have been trained: one classifies the status of the network, corresponding to an event, and the other will classify the location where the event is detected. To train the decision trees, a dataset made by co-simulating a power network and a communication network has been used. The decision trees are going to be compared in different settings by changing the division criteria, the dataset used to train them and the misclassification cost. After looking at their performance independently, the best way to combine them into a single algorithm is presented.This research was funded by Fundación Iberdrola España, within the 2020 research support scholarship program

Probabilistic yoking proofs for large scale IoT systems

Yoking (or grouping) proofs were introduced in 2004 as a security construction for RFID applications in which it is needed to build an evidence that several objects have been scanned simultaneously or, at least, within a short time. Such protocols were designed for scenarios where only a few tags (typically just two) are involved, so issues such as preventing an object from abandoning the proof right after being interrogated simply do not make sense. The idea, however, is very interesting for many Internet of Things (IoT) applications where a potentially large population of objects must be grouped together. In this paper we address this issue by presenting the notion of Probabilistic Yoking Proofs (PYP) and introducing three main criteria to assess their performance: cost, security, and fairness. Our proposal combines the message structure found in classical grouping proof constructions with an iterative Poisson sampling process where the probability of each object being sampled varies over time. We introduce a number of mechanisms to apply fluctuations to each object's sampling probability and present different sampling strategies. Our experimental results confirm that most strategies achieve good security and fairness levels while keeping the overall protocol cost down. (C) 2015 Elsevier B.V. All rights reserved.This work was supported by the MINECO Grant TIN2013 46469 R (SPINY: Security and Privacy in the Internet of You)

PAgIoT - Privacy-preserving aggregation protocol for internet of things

Modern society highly relies on the use of cyberspace to perform a huge variety of activities, such as social networking or e-commerce, and new technologies are continuously emerging. As such, computer systems may store a huge amount of information, which makes data analysis and storage a challenge. Information aggregation and correlation are two basic mechanisms to reduce the problem size, for example by filtering out redundant data or grouping similar one. These processes require high processing capabilities, and thus their application in Internet of Things (IoT) scenarios is not straightforward due to resource constraints. Furthermore, privacy issues may arise when the data at stake is personal. In this paper we propose PAgIoT, a Privacy-preserving Aggregation protocol suitable for IoT settings. It enables multi-attribute aggregation for groups of entities while allowing for privacy-preserving value correlation. Results show that PAgIoT is resistant to security attacks, it outperforms existing proposals that provide with the same security features, and it is feasible in resource-constrained devices and for aggregation of up to 10 attributes in big networks.This work was partially supported by the MINECO grant TIN2013-46469-R (SPINY: Security and Privacy in the Internet of You) and the CAM grant S2013/ICE-3095 CIBERDINE-CM (CIBERDINE: Cybersecurity, Data, and Risks) funded by the Autonomous Community of Madrid and co-funded by European funds

Retos en materia de ciberseguridad en smart grids

Proceedings of: VII Congreso Smart Grids 2020 [Congreso Online]En el proceso de digitalización de las redes eléctricas hacia las smart grids, el aumento de comunicación entre los dispositivos que la componen extiende los retos a los que se enfrentan los operadores de las redes eléctricas hasta el campo de la ciberseguridad. En el presente trabajo se muestran los principales retos en materia de ciberseguridad de las smart grids en tres ámbitos: (i) los sistemas de protección de las smart grids, atendiendo al impacto social derivado de la pérdida de una línea y el desabastecimiento de cargas, así como la posibilidad de provocar un colapso de tensión; (ii) los protocolos de comunicación entre dispositivos, dada la necesidad de salvaguardar la confidencialidad, veracidad y disponibilidad de la información intercambiada; y (iii) el marco legal, siendo necesario el desarrollo normativo ligado a las infraestructuras críticas