Towards secure message systems

Message systems, which transfer information from sender to recipient via communication networks, are indispensable to our modern society. The enormous user base of message systems and their critical role in information delivery make it the top priority to secure message systems. This dissertation focuses on securing the two most representative and dominant messages systems---e-mail and instant messaging (IM)---from two complementary aspects: defending against unwanted messages and ensuring reliable delivery of wanted messages.;To curtail unwanted messages and protect e-mail and instant messaging users, this dissertation proposes two mechanisms DBSpam and HoneyIM, which can effectively thwart e-mail spam laundering and foil malicious instant message spreading, respectively. DBSpam exploits the distinct characteristics of connection correlation and packet symmetry embedded in the behavior of spam laundering and utilizes a simple statistical method, Sequential Probability Ratio Test, to detect and break spam laundering activities inside a customer network in a timely manner. The experimental results demonstrate that DBSpam is effective in quickly and accurately capturing and suppressing e-mail spam laundering activities and is capable of coping with high speed network traffic. HoneyIM leverages the inherent characteristic of spreading of IM malware and applies the honey-pot technology to the detection of malicious instant messages. More specifically, HoneyIM uses decoy accounts in normal users\u27 contact lists as honey-pots to capture malicious messages sent by IM malware and suppresses the spread of malicious instant messages by performing network-wide blocking. The efficacy of HoneyIM has been validated through both simulations and real experiments.;To improve e-mail reliability, that is, prevent losses of wanted e-mail, this dissertation proposes a collaboration-based autonomous e-mail reputation system called CARE. CARE introduces inter-domain collaboration without central authority or third party and enables each e-mail service provider to independently build its reputation database, including frequently contacted and unacquainted sending domains, based on the local e-mail history and the information exchanged with other collaborating domains. The effectiveness of CARE on improving e-mail reliability has been validated through a number of experiments, including a comparison of two large e-mail log traces from two universities, a real experiment of DNS snooping on more than 36,000 domains, and extensive simulation experiments in a large-scale environment

INSTANT MESSAGING SPAM DETECTION IN LONG TERM EVOLUTION NETWORKS

The lack of efficient spam detection modules for packet data communication is resulting to increased threat exposure for the telecommunication network users and the service providers. In this thesis, we propose a novel approach to classify spam at the server side by intercepting packet-data communication among instant messaging applications. Spam detection is performed using machine learning techniques on packet headers and contents (if unencrypted) in two different phases: offline training and online classification. The contribution of this study is threefold. First, it identifies the scope of deploying a spam detection module in a state-of-the-art telecommunication architecture. Secondly, it compares the usefulness of various existing machine learning algorithms in order to intercept and classify data packets in near real-time communication of the instant messengers. Finally, it evaluates the accuracy and classification time of spam detection using our approach in a simulated environment of continuous packet data communication. Our research results are mainly generated by executing instances of a peer-to-peer instant messaging application prototype within a simulated Long Term Evolution (LTE) telecommunication network environment. This prototype is modeled and executed using OPNET network modeling and simulation tools. The research produces considerable knowledge on addressing unsolicited packet monitoring in instant messaging and similar applications

Design of Multi-View Based Email Classification for IoT Systems via Semi-Supervised Learning

Suspicious emails are one big threat for Internet of Things (IoT) security, which aim to induce users to click and then redirect them to a phishing webpage. To protect IoT systems, email classification is an essential mechanism to classify spam and legitimate emails. In the literature, most email classification approaches adopt supervised learning algorithms that require a large number of labeled data for classifier training. However, data labeling is very time consuming and expensive, making only a very small set of data available in practice, which would greatly degrade the effectiveness of email classification. To mitigate this problem, in this work, we develop an email classification approach based on multi-view disagreement-based semi-supervised learning. The idea behind is that multi-view method can offer richer information for classification, which is often ignored by literature. The use of semi-supervised learning can help leverage both labeled and unlabeled data. In the evaluation, we investigate the performance of our proposed approach with datasets and in real network environments. Experimental results demonstrate that multi-view can achieve better classification performance than single view, and that our approach can achieve better performance as compared to the existing similar algorithms

Unsupervised Anomaly Detectors to Detect Intrusions in the Current Threat Landscape

Anomaly detection aims at identifying unexpected fluctuations in the expected behavior of a given system. It is acknowledged as a reliable answer to the identification of zero-day attacks to such extent, several ML algorithms that suit for binary classification have been proposed throughout years. However, the experimental comparison of a wide pool of unsupervised algorithms for anomaly-based intrusion detection against a comprehensive set of attacks datasets was not investigated yet. To fill such gap, we exercise seventeen unsupervised anomaly detection algorithms on eleven attack datasets. Results allow elaborating on a wide range of arguments, from the behavior of the individual algorithm to the suitability of the datasets to anomaly detection. We conclude that algorithms as Isolation Forests, One-Class Support Vector Machines and Self-Organizing Maps are more effective than their counterparts for intrusion detection, while clustering algorithms represent a good alternative due to their low computational complexity. Further, we detail how attacks with unstable, distributed or non-repeatable behavior as Fuzzing, Worms and Botnets are more difficult to detect. Ultimately, we digress on capabilities of algorithms in detecting anomalies generated by a wide pool of unknown attacks, showing that achieved metric scores do not vary with respect to identifying single attacks.Comment: Will be published on ACM Transactions Data Scienc

Cyber Infrastructure Protection: Vol. III

Despite leaps in technological advancements made in computing system hardware and software areas, we still hear about massive cyberattacks that result in enormous data losses. Cyberattacks in 2015 included: sophisticated attacks that targeted Ashley Madison, the U.S. Office of Personnel Management (OPM), the White House, and Anthem; and in 2014, cyberattacks were directed at Sony Pictures Entertainment, Home Depot, J.P. Morgan Chase, a German steel factory, a South Korean nuclear plant, eBay, and others. These attacks and many others highlight the continued vulnerability of various cyber infrastructures and the critical need for strong cyber infrastructure protection (CIP). This book addresses critical issues in cybersecurity. Topics discussed include: a cooperative international deterrence capability as an essential tool in cybersecurity; an estimation of the costs of cybercrime; the impact of prosecuting spammers on fraud and malware contained in email spam; cybersecurity and privacy in smart cities; smart cities demand smart security; and, a smart grid vulnerability assessment using national testbed networks.https://press.armywarcollege.edu/monographs/1412/thumbnail.jp

Securing large cellular networks via a data oriented approach: applications to SMS spam and voice fraud defenses

University of Minnesota Ph.D. dissertation. December 2013. Major: Computer Science. Advisor: Zhi-Li Zhang. 1 computer file (PDF); x, 103 pages.With widespread adoption and growing sophistication of mobile devices, fraudsters have turned their attention from landlines and wired networks to cellular networks. While security threats to wireless data channels and applications have attracted the most attention, attacks through mobile voice channels, such as Short Message Service (SMS) spam and voice-related fraud activities also represent a serious threat to mobile users. In particular, it has been reported that the number of spam messages in the US has risen 45% in 2011 to 4.5 billion messages, affecting more than 69% of mobile users globally. Meanwhile, we have seen increasing numbers of incidents where fraudsters deploy malicious apps, e.g., disguised as gaming apps to entice users to download; when invoked, these apps automatically - and without users' knowledge - dial certain (international) phone numbers which charge exorbitantly high fees. Fraudsters also frequently utilize social engineering (e.g., SMS or email spam, Facebook postings) to trick users into dialing these exorbitant fee-charging numbers. Unlike traditional attacks towards data channels, e.g., Email spam and malware, both SMS spam and voice fraud are not only annoying, but they also inflict financial loss to mobile users and cellular carriers as well as adverse impact on cellular network performance. Hence the objective of defense techniques is to restrict phone numbers initialized these activities quickly before they reach too many victims. However, due to the scalability issues and high false alarm rates, anomaly detection based approaches for securing wireless data channels, mobile devices, and applications/services cannot be readily applied here. In this thesis, we share our experience and approach in building operational defense systems against SMS spam and voice fraud in large-scale cellular networks. Our approach is data oriented, i.e., we collect real data from a large national cellular network and exert significant efforts in analyzing and making sense of the data, especially to understand the characteristics of fraudsters and the communication patterns between fraudsters and victims. On top of the data analysis results, we can identify the best predictive features that can alert us of emerging fraud activities. Usually, these features represent unwanted communication patterns which are derived from the original feature space. Using these features, we apply advanced machine learning techniques to train accurate detection models. To ensure the validity of the proposed approaches, we build and deploy the defense systems in operational cellular networks and carry out both extensive off-line evaluation and long-term online trial. To evaluate the system performance, we adopt both direct measurement using known fraudster blacklist provided by fraud agents and indirect measurement by monitoring the change of victim report rates. In both problems, the proposed approaches demonstrate promising results which outperform customer feedback based defenses that have been widely adopted by cellular carriers today.More specifically, using a year (June 2011 to May 2012) of user reported SMS spam messages together with SMS network records collected from a large US based cellular carrier, we carry out a comprehensive study of SMS spamming. Our analysis shows various characteristics of SMS spamming activities. and also reveals that spam numbers with similar content exhibit strong similarity in terms of their sending patterns, tenure, devices and geolocations. Using the insights we have learned from our analysis, we propose several novel spam defense solutions. For example, we devise a novel algorithm for detecting related spam numbers. The algorithm incorporates user spam reports and identifies additional (unreported) spam number candidates which exhibit similar sending patterns at the same network location of the reported spam number during the nearby time period. The algorithm yields a high accuracy of 99.4% on real network data. Moreover, 72% of these spam numbers are detected at least 10 hours before user reports.From a different angle, we present the design of Greystar, a defense solution against the growing SMS spam traffic in cellular networks. By exploiting the fact that most SMS spammers select targets randomly from the finite phone number space, Greystar monitors phone numbers from the gray phone space (which are associated with data only devices like data cards and modems and machine-to-machine communication devices like point-of-sale machines and electricity meters) to alert emerging spamming activities. Greystar employs a novel statistical model for detecting spam numbers based on their footprints on the gray phone space. Evaluation using five month SMS call detail records from a large US cellular carrier shows that Greystar can detect thousands of spam numbers each month with very few false alarms and 15% of the detected spam numbers have never been reported by spam recipients. Moreover, Greystar is much faster than victim spam reports. By deploying Greystar we can reduce 75% spam messages during peak hours. To defend against voice-related fraud activities, we develop a novel methodology for detecting voice-related fraud activities using only call records. More specifically, we advance the notion of voice call graphs to represent voice calls from domestic callers to foreign recipients and propose a Markov Clustering based method for isolating dominant fraud activities from these international calls. Using data collected over a two year period from one of the largest cellular networks in the US, we evaluate the efficacy of the proposed fraud detection algorithm and conduct systematic analysis of the identified fraud activities. Our work sheds light on the unique characteristics and trends of fraud activities in cellular networks, and provides guidance on improving and securing hardware/software architecture to prevent these fraud activities

CHORUS Deliverable 2.2: Second report - identification of multi-disciplinary key issues for gap analysis toward EU multimedia search engines roadmap

After addressing the state-of-the-art during the first year of Chorus and establishing the existing landscape in multimedia search engines, we have identified and analyzed gaps within European research effort during our second year. In this period we focused on three directions, notably technological issues, user-centred issues and use-cases and socio- economic and legal aspects. These were assessed by two central studies: firstly, a concerted vision of functional breakdown of generic multimedia search engine, and secondly, a representative use-cases descriptions with the related discussion on requirement for technological challenges. Both studies have been carried out in cooperation and consultation with the community at large through EC concertation meetings (multimedia search engines cluster), several meetings with our Think-Tank, presentations in international conferences, and surveys addressed to EU projects coordinators as well as National initiatives coordinators. Based on the obtained feedback we identified two types of gaps, namely core technological gaps that involve research challenges, and “enablers”, which are not necessarily technical research challenges, but have impact on innovation progress. New socio-economic trends are presented as well as emerging legal challenges

Advanced persistent threats

Tese de mestrado, Segurança Informática, Universidade de Lisboa, Faculdade de Ciências, 2015Os sistemas computacionais tornaram-se uma parte importante da nossa sociedade, para além de estarmos intrinsecamente ligados a eles, a maioria da informação que utilizamos no nosso dia-a-dia está no seu formato digital. Ao contrário de um documento físico, um documento digital está exposto a uma maior variedade de ameaças, principalmente se estiver de alguma forma disponível `a Internet. Informação é poder, por isso não é de admirar que alguém, algures esteja a tentar roubá-la, assim, é facto que os adversários já operam neste novo mundo. Ladrões, terroristas e mesmo a máfia começaram a utilizar a internet como um meio para alcançar os seus fins. A cibersegurança tenta proteger a informação e os sistemas contra estes e outros tipos de ameaças, utilizando anti-vírus, firewalls ou detetores de intrusões, entre outros. Infelizmente as notícias continuam a sair, milhões de euros roubados a bancos por via informática, empresas saqueadas da sua propriedade intelectual e governos envergonhados por os seus segredos serem expostos ao mundo. A questão coloca-se, porque é que os sistemas de segurança estão a falhar? Como está o adversário a ultrapassá-los? A verdade hoje em dia é que os atacantes não só adquiriram talentos avançados na área como também têm acesso a ferramentas extremamente sofisticadas e vão fazer uso delas para serem bem-sucedidos nos seus objetivos, sejam estes o roubo de informação, o objetivo mais comum e por isso o mais abordado neste trabalho, seja o ataque a infraestruturas críticas. Advanced Persistent Threat(APT), ou ameaça avançada persistente, é um termo utilizado para caracterizar atacantes sofisticados, organizados e com recursos para concretizar ataques informáticos. Inventado pela força aérea Americana em 2006, o termo era uma forma de discutir intrusões informáticas com pessoal não militar. Nas suas origens, a palavra Ameaça indica que o adversário não é um pedaço de código automático, ou seja, o adversário ´e humano e ´e este humano que controla parte do ataque e contribui para o seu sucesso, avançada porque este humano é treinado e especializado na utilização de todo o espectro informático de forma a melhor conseguir atingir o seu objectivo e persistente, pois esse objectivo é formalmente definido, ou seja, o ataque só está concluído quando atingir o alvo em pleno. Infelizmente, o termo passou a ser utilizado para descrever qualquer ataque informático e a ter uma conotação extremamente comercial devido aos sistemas anti-APT que invadiram o mercado pouco tempo depois do ataque sofrido pela Google em 2010. Neste trabalho abordamos estes pressupostos, e explica-se o verdadeiro significado do termo juntamente com uma forma mais científica, claramente mais útil do ponto das abordagens da engenharia. Nomeadamente, sugere-se uma visão mais abrangente da campanha de ataque, não se focando apenas no software utilizado pelo adversário, mas tentando olhar para a campanha como um todo; equipas, organização, manutenção e orçamento, entre outros. Mostramos também porque estes ataques são diferentes, relativamente às suas tácticas, técnicas e procedimentos, e porque merecem ser distinguidos com a sua própria designação e o seu próprio ciclo de vida. Para além de identificarmos vários ciclos de vida associados às APTs, o ciclo de vida mais utilizado para caracterizar estas campanhas de ataque foi analisado em detalhe, desde as primeiras etapas de reconhecimento até à conclusão dos objectivos. Discute-se também a essência de cada passo e porque são, ou não, importantes. De seguida realiza-se uma análise ao tipo de atacante por trás destas campanhas, quem são, quais as suas histórias e objectivos. Avalia-se também porque é que os mecanismos de defesa tradicionais continuam a ser ultrapassados e n˜ao conseguem acompanhar o passo rápido dos atacantes. Isto acontece principalmente devido à utilização de listas do que é malicioso e o bloqueio apenas do que se encontra nessa lista, chamado de black listing. Ainda que se tenha já realizado trabalho na área de deteccão de anomalias, mostra-se também o porquê de esses sistemas continuarem a não ser suficientes, nomeadamente devido ao facto de definirem os seus pressupostos base erroneamente. Durante a realização deste trabalho percebeu-se a falta de estatísticas que pudessem responder a algumas questões. E por isso foi realizado um estudo aos relatórios disponíveis relativos a este tipo de ataques e apresentados os resultados de uma forma simples, organizada e resumida. Este estudo veio ajudar a perceber quais os maiores objectivos neste tipo de ataque, nomeadamente a espionagem e o roubo de informação confidencial; quais os maiores vectores de ataque (sendo o e-mail o grande vencedor devido à facilidade de explorar o vector humano); quais as aplicações alvo e a utilização, ou não, de vulnerabilidades desconhecidas. Esperamos que esta recolha de informação seja útil para trabalhos futuros ou para interessados no tema. Só depois de realizado este estudo foi possível pensar em formas de contribuir para a solução do problema imposto pelas APTs. Uma distinção ficou clara, existe não só a necessidade de detectar APTs, mas também a criticalidade da sua prevenção. A melhor forma de não ser vítima de infeção é a aplicação de boas práticas de segurança e, neste caso, a formação de todo o pessoal relativamente ao seu papel na segurança geral da organização. Aborda-se também a importância da preparação; segurança não é apenas proteger-se dos atacantes, mas principalmente saber como recuperar. Relativamente à deteção, foi realizado trabalho em duas vertentes, primeiramente e visto o trabalho ter sido realizado em ambiente de empresa, foi elaborado um plano para um sistema capaz de detectar campanhas de ataque que utilizassem o vetor de infeção do e-mail, fazendo uso dos sistemas já desenvolvidos pela AnubisNetworks que, sendo uma empresa de segurança informática com fortes ligações ao e-mail, tinha o conhecimento e as ferramentas necessárias para a concretização do sistema. O sistema faz uso de uma caracterização de pessoas, chamado de people mapping, que visa a identificar os principais alvos dentro da empresa e quem exibe maiores comportamentos de risco. Esta caracterização possibilita a criação de uma lista de pessoal prioritário, que teria o seu e-mail (caso tivesse anexos ou endereços) analisado em ambiente de sandbox. Este sistema acabou por não ser construído e é apenas deixada aqui a sua esquematização, sendo que fica lançado o desafio para a sua realização. De forma a contribuir não só para a empresa, mas também para a comunidade científica de segurança, foi de seguida realizado trabalho de deteção em vários pontos de qualquer rede informática seguindo os quatro principais passos na execução de uma campanha APT. Decidimos então utilizar um ciclo de vida composto por quatro etapas, sendo elas, a fase de reconhecimento, a infeção inicial, o controlo e o roubo de informação. Neste modelo, procuraram-se possíveis sistemas para a deteção de eventos relacionados com APTs nos três principais pontos de qualquer rede: a Internet, a Intranet e a máquina cliente. Ao analisar cada fase em cada ponto da rede, foi possível perceber realmente quais as principais áreas de estudo e desenvolvimento para melhor detectar APTs. Mais concretamente, concluiu-se que a internet seria o ponto ideal de deteção das fases de reconhecimento, a intranet para detetar controlo e roubo de informação e a máquina cliente para detetar infeção inicial. Conclui-se o trabalho apresentando o nosso ponto de vista relativamente ao futuro, isto é, quem vai fazer uso das táticas utilizadas nas campanhas APT visto serem extremamente bem sucedidas, como vão os atacantes adaptar-se aos novos mecanismos de defesa e quais os novos possíveis vetores de infeção.Computer systems have become a very important part of our society, most of the information we use in our everyday lives is in its digital form, and since information is power it only makes sense that someone, somewhere will try to steal it. Attackers are adapting and now have access to highly sophisticated tools and expertise to conduct highly targeted and very complex attack campaigns. Advanced Persistent Threat, or APT, is a term coined by the United States Air Force around 2006 as a way to talk about classified intrusions with uncleared personnel. It wrongly and quickly became the standard acronym to describe every sort of attack. This work tries to demystify the problem of APTs, why they are called as such, and what are the most common tactics, techniques and procedures. It also discusses previously proposed life-cycles, profile the most common adversaries and takes a look at why traditional defences will not stop them. A big problem encountered while developing this work was the lack of statistics regarding APT attacks. One of the big contributions here consists on the search for publicly available reports, its analysis, and presentation of relevant information gathered in a summarised fashion. From the most targeted applications to the most typical infection vector, insight is given on how and why the adversaries conduct these attacks. Only after a clear understanding of the problem is reached, prevention and detection schemes were discussed. Specifically, blueprints for a system to be used by AnubisNetworks are presented, capable of detecting these attacks at the e-mail level. It is based on sandboxing and people mapping, which is a way to better understand people, one of the weakest links in security. The work is concluded by trying to understand how the threat landscape will shape itself in upcoming years

Development of an M-commerce security framework

Research shows how M-Commerce has managed to find its way to previously inaccessible parts of the world as a major Information and Communication Technologies (ICT) tool for development due to widespread introduction of mobile phones in remote areas. M-Commerce has offered valuable advantages: anytime, anywhere, more personal, more location-aware, more context-aware, more age aware, always online and instant connectivity. But this is not without its problems, of which security is high on the list. The security issues span the whole M-Commerce spectrum, from the top to the bottom layer of the OSI network protocol stack, from machines to humans. This research proposes a threat-mitigation modular framework to help address the security issues lurking in M-Commerce systems being used by marginalised rural community members. The research commences with a literature survey carried out to establish security aspects related to M-Commerce and to determine requirements for a security framework. The framework classifies M-Commerce security threat-vulnerability-risks into four levels: human behaviour and mobile device interaction security, mobile device security, M-Commerce access channel security, wireless network access security. This is followed by a review of the supporting structures or related frameworks that the proposed framework could leverage to address security issues on M-Commerce systems as ICT4D initiatives. The proposed security framework based on the requirements discovered is then presented. As a proof-of-concept, a case study was undertaken at the Siyakhula Living Lab at Dwesa in the Eastern Cape province of South Africa in order to validate the components of the proposed framework. Following the application of the framework in a case study, it can be argued that the proposed security framework allows for secure transacting by marginalised users using M-Commerce initiatives. The security framework is therefore useful in addressing the identified security requirements of M-Commerce in ICT4D contexts