Prävention, Detektion und Reaktion gegen drei Ausprägungsformen automotiver Malware : eine methodische Analyse im Spektrum von Manipulationen und Schutzkonzepten

Magdeburg, Univ., Fak. für Informatik, Diss., 2014

Plataforma automatizada de detección de malware

Es descriu el disseny i posterior implementació de la nova plataforma d’automatització del servei ofert per Internet Security Auditors, S.L. destinada a l’anàlisi de dominis d’Internet amb la finalitat de detectar possibles infeccions que afectin a usuaris de la web. El sistema actual conté algunes deficiències, de manera que aquest text presenta una nova versió, la qual aporta millores molt significatives com ara una gestió més òptima, o un disseny renovat i escalable de la informació i els diferents processos. Així mateix es dota al sistema d’un control d’errors centralitzat, amb enviament d’alàrmes en temps real, i una agrupació i centralització dels resultats.

Malware al context del programari lliure

Aquest document mostra els resultats d'una recerca basada en un cas d'estudi on s'avalua la fortalesa de dos comunitats de programari lliure. L'avaluació d'aquesta fortalesa es du a terme amb una exploració que té com a objectiu esbrinar si aquestes comunitats acompleixen una sèrie de procediments que les ajuden a protegir-se davant d'atacs.

IRP Poster - The Evolution of Malware

IRP poster for "The Evolution of Malware"

Malware distributed collection and pre-classification system using honeypot technology

Malware has become a major threat in the last years due to the ease of spread through the Internet. Malware detection has become difficult with the use of compression, polymorphic methods and techniques to detect and disable security software. Those and other obfuscation techniques pose a problem for detection and classification schemes that analyze malware behavior. In this paper we propose a distributed architecture to improve malware collection using different honeypot technologies to increase the variety of malware collected. We also present a daemon tool developed to grab malware distributed through spam and a pre-classification technique that uses antivirus technology to separate malware in generic classes. © 2009 SPIE.

A malware detection system inspired on the human immune system

Malicious programs (malware) can cause severe damage on computer systems and data. The mechanism that the human immune system uses to detect and protect from organisms that threaten the human body is efficient and can be adapted to detect malware attacks. In this paper we propose a system to perform malware distributed collection, analysis and detection, this last inspired by the human immune system. After collecting malware samples from Internet, they are dynamically analyzed so as to provide execution traces at the operating system level and network flows that are used to create a behavioral model and to generate a detection signature. Those signatures serve as input to a malware detector, acting as the antibodies in the antigen detection process. This allows us to understand the malware attack and aids in the infection removal procedures. © 2012 Springer-Verlag.

Cryptolocker, trick or threat: phishing e malware alla ricerca dell'anello debole

Il Cryptolocker è un malware diffuso su scala globale appartenente alla categoria ransomware. La mia analisi consiste nel ripercorrere le origini dei software maligni alla ricerca di rappresentanti del genere con caratteristiche simili al virus che senza tregua persevera a partire dal 2013: il Cryptolocker. Per imparare di più sul comportamento di questa minaccia vengono esposte delle analisi del malware, quella statica e quella dinamica, eseguite sul Cryptolocker (2013), CryptoWall (2014) e TeslaCrypt (2015). In breve viene descritta la parte operativa per la concezione e la configurazione di un laboratorio virtuale per la successiva raccolta di tracce lasciate dal malware sul sistema e in rete. In seguito all’analisi pratica e alla concentrazione sui punti deboli di queste minacce, oltre che sugli aspetti tecnici alla base del funzionamento dei crypto, vengono presi in considerazione gli aspetti sociali e psicologici che caratterizzano un complesso background da cui il virus prolifica. Vengono confrontate fonti autorevoli e testimonianze per chiarire i dubbi rimasti dopo i test. Saranno questi ultimi a confermare la veridicità dei dati emersi dai miei esperimenti, ma anche a formare un quadro più completo sottolineando quanto la morfologia del malware sia in simbiosi con la tipologia di utente che va a colpire. Capito il funzionamento generale del crypto sono proprio le sue funzionalità e le sue particolarità a permettermi di stilare, anche con l’aiuto di fonti esterne al mio operato, una lista esauriente di mezzi e comportamenti difensivi per contrastarlo ed attenuare il rischio d’infezione. Vengono citati anche le possibili procedure di recupero per i dati compromessi, per i casi “fortunati”, in quanto il recupero non è sempre materialmente possibile. La mia relazione si conclude con una considerazione da parte mia inaspettata: il potenziale dei crypto, in tutte le loro forme, risiede per la maggior parte nel social engineering, senza il quale (se non per certe categorie del ransomware) l’infezione avrebbe percentuali di fallimento decisamente più elevate.

Análisis de características estáticas de ficheros ejecutables para la clasificación de Malware

El Malware es una grave amenaza para la seguridad de los sistemas. Con el uso generalizado de la World Wide Web, ha habido un enorme aumento en los ataques de virus, haciendo que la seguridad informática sea esencial para todas las computadoras y se expandan las áreas de investigación sobre los nuevos incidentes que se generan, siendo una de éstas la clasificación del malware. Los “desarrolladores de malware” utilizan nuevas técnicas para generar malware polimórfico reutilizando los malware existentes, por lo cual es necesario agruparlos en familias para estudiar sus características y poder detectar nuevas variantes de los mismos. Este trabajo, además de presentar un detallado estado de la cuestión de la clasificación del malware de ficheros ejecutables PE, presenta un enfoque en el que se mejora el índice de la clasificación de la base de datos de Malware MALICIA utilizando las características estáticas de ficheros ejecutables Imphash y Pehash, utilizando dichas características se realiza un clustering con el algoritmo clustering agresivo el cual se cambia con la clasificación actual mediante el algoritmo de majority voting y la característica icon_label, obteniendo un Precision de 99,15% y un Recall de 99,32% mejorando la clasificación de MALICIA con un F-measure de 99,23%.---ABSTRACT---Malware is a serious threat to the security of systems. With the widespread use of the World Wide Web, there has been a huge increase in virus attacks, making the computer security essential for all computers. Near areas of research have append in this area including classifying malware into families, Malware developers use polymorphism to generate new variants of existing malware. Thus it is crucial to group variants of the same family, to study their characteristics and to detect new variants. This work, in addition to presenting a detailed analysis of the problem of classifying malware PE executable files, presents an approach in which the classification in the Malware database MALICIA is improved by using static characteristics of executable files, namely Imphash and Pehash. Both features are evaluated through clustering real malware with family labels with aggressive clustering algorithm and combining this with the current classification by Majority voting algorithm, obtaining a Precision of 99.15% and a Recall of 99.32%, improving the classification of MALICIA with an F-measure of 99,23%.

Técnicas criptográficas utilizadas en "MALWARE"

La finalidad de este trabajo es estudiar los tipos de malware existentes así como las técnicas criptográficas utilizadas en ellos para ocultar y ofuscar sus actividades, con el fin de impedir su análisis a las empresas de seguridad. En este trabajo se propone un criptosistema conveniente que, además de en malware, pueda ser empleado en otros ámbitos. El análisis comienza proponiendo una breve definición del término Criptografía y explicando mediante ejemplos los distintos tipos de sistemas criptográficos y algunos posibles usos. A continuación se lleva a cabo un estudio de los distintos tipos de malware existentes, dando una visión histórica de los tipos de Criptografía utilizados hasta el momento en cada caso. El estudio profundiza en las técnicas criptográficas utilizadas actualmente y su seguridad criptográfica. Además, se realiza una propuesta de posibles soluciones criptográficas, analizando la seguridad computacional resultante de aplicar dichas soluciones y calculando la carga computacional adicional precisa. Finalmente se ofrecen una serie de conclusiones y unas líneas de desarrollo futuras.

Simulación de la propagación del malware: modelos continuos vs. modelos discretos

La gran mayoría de modelos matemáticos propuestos hasta la fecha para simular la propagación del malware están basados en el uso de ecuaciones diferenciales. Dichos modelos son analizados de manera crítica en este trabajo, determinando las principales deficiencias que presentan y planteando distintas alternativas para su subsanación. En este sentido, se estudia el uso de los autómatas celulares como nuevo paradigma en el que basar los modelos epidemiológicos, proponiendo una alternativa explícita basada en ellos a un reciente modelo continuo.

Integrity-based kernel malware detection

Kernel-level malware is one of the most dangerous threats to the security of users on the Internet, so there is an urgent need for its detection. The most popular detection approach is misuse-based detection. However, it cannot catch up with today's advanced malware that increasingly apply polymorphism and obfuscation. In this thesis, we present our integrity-based detection for kernel-level malware, which does not rely on the specific features of malware. ^ We have developed an integrity analysis system that can derive and monitor integrity properties for commodity operating systems kernels. In our system, we focus on two classes of integrity properties: data invariants and integrity of Kernel Queue (KQ) requests. ^ We adopt static analysis for data invariant detection and overcome several technical challenges: field-sensitivity, array-sensitivity, and pointer analysis. We identify data invariants that are critical to system runtime integrity from Linux kernel 2.4.32 and Windows Research Kernel (WRK) with very low false positive rate and very low false negative rate. We then develop an Invariant Monitor to guard these data invariants against real-world malware. In our experiment, we are able to use Invariant Monitor to detect ten real-world Linux rootkits and nine real-world Windows malware and one synthetic Windows malware. ^ We leverage static and dynamic analysis of kernel and device drivers to learn the legitimate KQ requests. Based on the learned KQ requests, we build KQguard to protect KQs. At runtime, KQguard rejects all the unknown KQ requests that cannot be validated. We apply KQguard on WRK and Linux kernel, and extensive experimental evaluation shows that KQguard is efficient (up to 5.6% overhead) and effective (capable of achieving zero false positives against representative benign workloads after appropriate training and very low false negatives against 125 real-world malware and nine synthetic attacks). ^ In our system, Invariant Monitor and KQguard cooperate together to protect data invariants and KQs in the target kernel. By monitoring these integrity properties, we can detect malware by its violation of these integrity properties during execution.^

Integrity-Based Kernel Malware Detection

Kernel-level malware is one of the most dangerous threats to the security of users on the Internet, so there is an urgent need for its detection. The most popular detection approach is misuse-based detection. However, it cannot catch up with today's advanced malware that increasingly apply polymorphism and obfuscation. In this thesis, we present our integrity-based detection for kernel-level malware, which does not rely on the specific features of malware. We have developed an integrity analysis system that can derive and monitor integrity properties for commodity operating systems kernels. In our system, we focus on two classes of integrity properties: data invariants and integrity of Kernel Queue (KQ) requests. We adopt static analysis for data invariant detection and overcome several technical challenges: field-sensitivity, array-sensitivity, and pointer analysis. We identify data invariants that are critical to system runtime integrity from Linux kernel 2.4.32 and Windows Research Kernel (WRK) with very low false positive rate and very low false negative rate. We then develop an Invariant Monitor to guard these data invariants against real-world malware. In our experiment, we are able to use Invariant Monitor to detect ten real-world Linux rootkits and nine real-world Windows malware and one synthetic Windows malware. We leverage static and dynamic analysis of kernel and device drivers to learn the legitimate KQ requests. Based on the learned KQ requests, we build KQguard to protect KQs. At runtime, KQguard rejects all the unknown KQ requests that cannot be validated. We apply KQguard on WRK and Linux kernel, and extensive experimental evaluation shows that KQguard is efficient (up to 5.6% overhead) and effective (capable of achieving zero false positives against representative benign workloads after appropriate training and very low false negatives against 125 real-world malware and nine synthetic attacks). In our system, Invariant Monitor and KQguard cooperate together to protect data invariants and KQs in the target kernel. By monitoring these integrity properties, we can detect malware by its violation of these integrity properties during execution.