Energy efficiency of intrusion detection systems in wireless sensor networks

Security is a significant concern for many sensor network applications. Intrusion detection is one method of defending against attacks. However, standard intrusion detection is not suitable for sensor networks with limited battery power, memory and processing resources. This paper compares several approaches to intrusion detection in sensor networks. We investigate accuracy of detecting attacks, versus energy efficiency

The effectiveness of evasion techniques against intrusion prevention systems

Evaasioita ja evaasiokombinaatiota käytetään naamioimaan hyökkäyksiä, jotta tietoturvalaitteet eivät havaitsisi niitä. Diplomityössä tutkitaan näiden tekniikoiden tehokkuutta uusimpia tunkeutumisenestojärjestelmiä vastaan. Yhteensä 11 tunkeutumisenestojärjestelmää tutkittiin, joista 10 on kaupallista ja yksi ilmainen. Tutkimuksessa suoritettiin neljä koetta. Jokainen koe sisälsi miljoona hyökkäystä, jotka suoritettiin jokaista tunkeutumisenestojärjestelmää vastaan satunnaisin evaasioin ja evaasiokombinaatioin. Käytetty hyökkäys pysyi samana yksittäisen kokeen aikana, mutta jokainen hyökkäys oli naamioitu eri evaasiotekniikoin. Yhtenäistettyjä konfiguraatioita käytettiin, jotta saataisiin vertailukelpoisia tuloksia. Tulokset osoittavat, että evaasiotekniikat ovat toimivia suurinta osaa testattuja tunkeutumisenestojärjestelmiä vastaan. Vaikka osa evaasiotekniikoista on peräisin 1990-luvulta, ne voidaan saada hienosäädettyä huijaamaan suurinta osaa testatuista laitteista. Yksi evaasiotekniikka ei ole aina riittävä, jotta voitaisiin välttää hyökkäyksen havainnointi. Monen eri tekniikan yhdistäminen lisää kuitenkin todennäköisyyttä löytää tapa kiertää havainnointi.Evasions and evasion combinations are used to masquerade attacks in order to avoid detection by security appliances. This thesis evaluates the effectiveness of these techniques against the state of the art intrusion prevention systems. In total, 11 intrusion prevention systems were studied, 10 commercial and 1 free solution. Four experiments were conducted in this study. Each of the experiments contained a million attacks that were performed with randomized evasions and evasion combinations against each intrusion prevention system. The used attack stayed the same during a single experiment, but each attack was disguised with different evasion techniques. Standardized configurations were used in order to produce comparable results. The results indicate that evasion techniques are effective against the majority of tested intrusion prevention systems. Even though some of the techniques are from the 1990s, they can be fine-tuned to fool most of the tested appliances. One evasion technique is not always enough to avoid detection, but combining multiple techniques increases the possibility to find a way to evade detection

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

Incidence of accidental awareness during general anaesthesia in obstetrics: a multicentre, prospective cohort study

General anaesthesia for obstetric surgery has distinct characteristics that may contribute towards a higher risk of accidental awareness during general anaesthesia. The primary aim of this study was to investigate the incidence, experience and psychological implications of unintended conscious awareness during general anaesthesia in obstetric patients. From May 2017 to August 2018, 3115 consenting patients receiving general anaesthesia for obstetric surgery in 72 hospitals in England were recruited to the study. Patients received three repetitions of standardised questioning over 30 days, with responses indicating memories during general anaesthesia that were verified using interviews and record interrogation. A total of 12 patients had certain/probable or possible awareness, an incidence of 1 in 256 (95%CI 149–500) for all obstetric surgery. The incidence was 1 in 212 (95%CI 122–417) for caesarean section surgery. Distressing experiences were reported by seven (58.3%) patients, paralysis by five (41.7%) and paralysis with pain by two (16.7%). Accidental awareness occurred during induction and emergence in nine (75%) of the patients who reported awareness. Factors associated with accidental awareness during general anaesthesia were: high BMI (25–30 kg.m-2); low BMI (<18.5 kg.m-2); out-of-hours surgery; and use of ketamine or thiopental for induction. Standardised psychological impact scores at 30 days were significantly higher in awareness patients (median (IQR [range]) 15 (2.7–52.0 [2–56]) than in patients without awareness 3 (1–9 [0–64]), p = 0.010. Four patients had a provisional diagnosis of post-traumatic stress disorder. We conclude that direct postoperative questioning reveals high rates of accidental awareness during general anaesthesia for obstetric surgery, which has implications for anaesthetic practice, consent and follow-up

Incidence of accidental awareness during general anaesthesia in obstetrics: a multi-centre, prospective cohort study

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Misconfiguration Analysis of Network Access Control Policies

Network access control (NAC) systems have a very important role in network security. However, NAC policy configuration is an extremely complicated and error-prone task due to the semantic complexity of NAC policies and the large number of rules that could exist. This significantly increases the possibility of policy misconfigurations and network vulnerabilities. NAC policy misconfigurations jeopardize network security and can result in a severe consequence such as reachability and denial of service problems. In this thesis, we choose to study and analyze the NAC policy configuration of two significant network security devices, namely, firewall and IDS/IPS. In the first part of the thesis, a visualization technique is proposed to visualize firewall rules and policies to efficiently enhance the understanding and inspection of firewall configuration. This is implemented in a tool called PolicyVis. Our tool helps the user to answer general questions such as ‘‘Does this policy satisfy my connection/security requirements’’. If not, the user can detect all misconfigurations in the firewall policy. In the second part of the thesis, we study various policy misconfigurations of Snort, a very popular IDS/IPS. We focus on the misconfigurations of the flowbits option which is one of the most important features to offers a stateful signature-based NIDS. We particularly concentrate on a class of flowbits misconfiguration that makes Snort susceptible to false negatives. We propose a method to detect the flowbits misconfiguration, suggest practical solutions with controllable false positives to fix the misconfiguration and formally prove that the solutions are complete and sound

Aerospace Medicine and Biology: A continuing bibliography with indexes, supplement 158, September 1976

This bibliography lists 191 reports, articles, and other documents introduced into the NASA scientific and technical information system in August 1976