36 research outputs found

    Emotions behind drive-by download propagation on Twitter

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    Twitter has emerged as one of the most popular platforms to get updates on entertainment and current events. However, due to its 280 character restriction and automatic shortening of URLs, it is continuously targeted by cybercriminals to carry out drive-by download attacks, where a user’s system is infected by merely visiting a Web page. Popular events that attract a large number of users are used by cybercriminals to infect and propagate malware by using popular hashtags and creating misleading tweets to lure users to malicious Web pages. A drive-by download attack is carried out by obfuscating a malicious URL in an enticing tweet and used as clickbait to lure users to a malicious Web page. In this paper we answer the following two questions: Why are certain malicious tweets retweeted more than others? Do emotions reflecting in a tweet drive virality? We gathered tweets from seven different sporting events over three years and identified those tweets that used to carry to out a drive-by download attack. From the malicious (N=105,642) and benign (N=169,178) data sample identified, we built models to predict information flow size and survival. We define size as the number of retweets of an original tweet, and survival as the duration of the original tweet’s presence in the study window. We selected the zero-truncated negative binomial (ZTNB) regression method for our analysis based on the distribution exhibited by our dependent size measure and the comparison of results with other predictive models. We used the Cox regression technique to model the survival of information flows as it estimates proportional hazard rates for independent measures. Our results show that both social and content factors are statistically significant for the size and survival of information flows for both malicious and benign tweets. In the benign data sample, positive emotions and positive sentiment reflected in the tweet significantly predict size and survival. In contrast, for the malicious data sample, negative emotions, especially fear, are associated with both size and survival of information flows

    Resilience Strategies for Network Challenge Detection, Identification and Remediation

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    The enormous growth of the Internet and its use in everyday life make it an attractive target for malicious users. As the network becomes more complex and sophisticated it becomes more vulnerable to attack. There is a pressing need for the future internet to be resilient, manageable and secure. Our research is on distributed challenge detection and is part of the EU Resumenet Project (Resilience and Survivability for Future Networking: Framework, Mechanisms and Experimental Evaluation). It aims to make networks more resilient to a wide range of challenges including malicious attacks, misconfiguration, faults, and operational overloads. Resilience means the ability of the network to provide an acceptable level of service in the face of significant challenges; it is a superset of commonly used definitions for survivability, dependability, and fault tolerance. Our proposed resilience strategy could detect a challenge situation by identifying an occurrence and impact in real time, then initiating appropriate remedial action. Action is autonomously taken to continue operations as much as possible and to mitigate the damage, and allowing an acceptable level of service to be maintained. The contribution of our work is the ability to mitigate a challenge as early as possible and rapidly detect its root cause. Also our proposed multi-stage policy based challenge detection system identifies both the existing and unforeseen challenges. This has been studied and demonstrated with an unknown worm attack. Our multi stage approach reduces the computation complexity compared to the traditional single stage, where one particular managed object is responsible for all the functions. The approach we propose in this thesis has the flexibility, scalability, adaptability, reproducibility and extensibility needed to assist in the identification and remediation of many future network challenges

    Intrusion detection and management over the world wide web

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    As the Internet and society become ever more integrated so the number of Internet users continues to grow. Today there are 1.6 billion Internet users. They use its services to work from home, shop for gifts, socialise with friends, research the family holiday and manage their finances. Through generating both wealth and employment the Internet and our economies have also become interwoven. The growth of the Internet has attracted hackers and organised criminals. Users are targeted for financial gain through malware and social engineering attacks. Industry has responded to the growing threat by developing a range defences: antivirus software, firewalls and intrusion detection systems are all readily available. Yet the Internet security problem continues to grow and Internet crime continues to thrive. Warnings on the latest application vulnerabilities, phishing scams and malware epidemics are announced regularly and serve to heighten user anxiety. Not only are users targeted for attack but so too are businesses, corporations, public utilities and even states. Implementing network security remains an error prone task for the modern Internet user. In response this thesis explores whether intrusion detection and management can be effectively offered as a web service to users in order to better protect them and heighten their awareness of the Internet security threat

    Optimal Control of Epidemics in the Presence of Heterogeneity

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    We seek to identify and address how different types of heterogeneity affect the optimal control of epidemic processes in social, biological, and computer networks. Epidemic processes encompass a variety of models of propagation that are based on contact between agents. Assumptions of homogeneity of communication rates, resources, and epidemics themselves in prior literature gloss over the heterogeneities inherent to such networks and lead to the design of sub-optimal control policies. However, the added complexity that comes with a more nuanced view of such networks complicates the generalizing of most prior work and necessitates the use of new analytical methods. We first create a taxonomy of heterogeneity in the spread of epidemics. We then model the evolution of heterogeneous epidemics in the realms of biology and sociology, as well as those arising from practice in the fields of communication networks (e.g., DTN message routing) and security (e.g., malware spread and patching). In each case, we obtain computational frameworks using Pontryagin’s Maximum Principle that will lead to the derivation of dynamic controls that optimize general, context-specific objectives. We then prove structures for each of these vectors of optimal controls that can simplify the derivation, storage, and implementation of optimal policies. Finally, using simulations and real-world traces, we examine the benefits achieved by including heterogeneity in the control decision, as well as the sensitivity of the models and the controls to model parameters in each case

    Novel Analytical Modelling-based Simulation of Worm Propagation in Unstructured Peer-to-Peer Networks

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    Millions of users world-wide are sharing content using Peer-to-Peer (P2P) networks, such as Skype and Bit Torrent. While such new innovations undoubtedly bring benefits, there are nevertheless some associated threats. One of the main hazards is that P2P worms can penetrate the network, even from a single node and then spread rapidly. Understanding the propagation process of such worms has always been a challenge for researchers. Different techniques, such as simulations and analytical models, have been adopted in the literature. While simulations provide results for specific input parameter values, analytical models are rather more general and potentially cover the whole spectrum of given parameter values. Many attempts have been made to model the worm propagation process in P2P networks. However, the reported analytical models to-date have failed to cover the whole spectrum of all relevant parameters and have therefore resulted in high false-positives. This consequently affects the immunization and mitigation strategies that are adopted to cope with an outbreak of worms. The first key contribution of this thesis is the development of a susceptible, exposed, infectious, and Recovered (SEIR) analytical model for the worm propagation process in a P2P network, taking into account different factors such as the configuration diversity of nodes, user behaviour and the infection time-lag. These factors have not been considered in an integrated form previously and have been either ignored or partially addressed in state-of-the-art analytical models. Our proposed SEIR analytical model holistically integrates, for the first time, these key factors in order to capture a more realistic representation of the whole worm propagation process. The second key contribution is the extension of the proposed SEIR model to the mobile M-SEIR model by investigating and incorporating the role of node mobility, the size of the worm and the bandwidth of wireless links in the worm propagation process in mobile P2P networks. The model was designed to be flexible and applicable to both wired and wireless nodes. The third contribution is the exploitation of a promising modelling paradigm, Agent-based Modelling (ABM), in the P2P worm modelling context. Specifically, to exploit the synergies between ABM and P2P, an integrated ABM-Based worm propagation model has been built and trialled in this research for the first time. The introduced model combines the implementation of common, complex P2P protocols, such as Gnutella and GIA, along with the aforementioned analytical models. Moreover, a comparative evaluation between ABM and conventional modelling tools has been carried out, to demonstrate the key benefits of ease of real-time analysis and visualisation. As a fourth contribution, the research was further extended by utilizing the proposed SEIR model to examine and evaluate a real-world data set on one of the most recent worms, namely, the Conficker worm. Verification of the model was achieved using ABM and conventional tools and by then comparing the results on the same data set with those derived from developed benchmark models. Finally, the research concludes that the worm propagation process is to a great extent affected by different factors such as configuration diversity, user-behaviour, the infection time lag and the mobility of nodes. It was found that the infection propagation values derived from state-of-the-art mathematical models are hypothetical and do not actually reflect real-world values. In summary, our comparative research study has shown that infection propagation can be reduced due to the natural immunity against worms that can be provided by a holistic exploitation of the range of factors proposed in this work

    Information dissemination in mobile networks

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    This thesis proposes some solutions to relieve, using Wi-Fi wireless networks, the data consumption of cellular networks using cooperation between nodes, studies how to make a good deployment of access points to optimize the dissemination of contents, analyzes some mechanisms to reduce the nodes' power consumption during data dissemination in opportunistic networks, as well as explores some of the risks that arise in these networks. Among the applications that are being discussed for data off-loading from cellular networks, we can find Information Dissemination in Mobile Networks. In particular, for this thesis, the Mobile Networks will consist of Vehicular Ad-hoc Networks and Pedestrian Ad-Hoc Networks. In both scenarios we will find applications with the purpose of vehicle-to-vehicle or pedestrian-to-pedestrian Information dissemination, as well as vehicle-to-infrastructure or pedestrian-to-infrastructure Information dissemination. We will see how both scenarios (vehicular and pedestrian) share many characteristics, while on the other hand some differences make them unique, and therefore requiring of specific solutions. For example, large car batteries relegate power saving techniques to a second place, while power-saving techniques and its effects to network performance is a really relevant issue in Pedestrian networks. While Cellular Networks offer geographically full-coverage, in opportunistic Wi-Fi wireless solutions the short-range non-fullcoverage paradigm as well as the high mobility of the nodes requires different network abstractions like opportunistic networking, Disruptive/Delay Tolerant Networks (DTN) and Network Coding to analyze them. And as a particular application of Dissemination in Mobile Networks, we will study the malware spread in Mobile Networks. Even though it relies on similar spreading mechanisms, we will see how it entails a different perspective on Dissemination

    Cellular, Wide-Area, and Non-Terrestrial IoT: A Survey on 5G Advances and the Road Towards 6G

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    The next wave of wireless technologies is proliferating in connecting things among themselves as well as to humans. In the era of the Internet of things (IoT), billions of sensors, machines, vehicles, drones, and robots will be connected, making the world around us smarter. The IoT will encompass devices that must wirelessly communicate a diverse set of data gathered from the environment for myriad new applications. The ultimate goal is to extract insights from this data and develop solutions that improve quality of life and generate new revenue. Providing large-scale, long-lasting, reliable, and near real-time connectivity is the major challenge in enabling a smart connected world. This paper provides a comprehensive survey on existing and emerging communication solutions for serving IoT applications in the context of cellular, wide-area, as well as non-terrestrial networks. Specifically, wireless technology enhancements for providing IoT access in fifth-generation (5G) and beyond cellular networks, and communication networks over the unlicensed spectrum are presented. Aligned with the main key performance indicators of 5G and beyond 5G networks, we investigate solutions and standards that enable energy efficiency, reliability, low latency, and scalability (connection density) of current and future IoT networks. The solutions include grant-free access and channel coding for short-packet communications, non-orthogonal multiple access, and on-device intelligence. Further, a vision of new paradigm shifts in communication networks in the 2030s is provided, and the integration of the associated new technologies like artificial intelligence, non-terrestrial networks, and new spectra is elaborated. Finally, future research directions toward beyond 5G IoT networks are pointed out.Comment: Submitted for review to IEEE CS&

    Malware Propagation in Online Social Networks: Modeling, Analysis and Real-world Implementations

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    The popularity and wide spread usage of online social networks (OSNs) have attracted hackers and cyber criminals to use OSNs as an attack platform to spread malware. Over the last few years, Facebook users have experienced hundreds of malware attacks. A successful attack can lead to tens of millions of OSN accounts being compromised and computers being infected. Cyber criminals can mount massive denial of service attacks against Internet infrastructures or systems using compromised accounts and computers. Malware infecting a user's computer have the ability to steal login credentials and other confidential information stored on the computer, install ransomware and infect other computers on the same network. Therefore, it is important to understand propagation dynamics of malware in OSNs in order to detect, contain and remove them as early as possible. The objective of this dissertation is thus to model and study propagation dynamics of various types of malware in social networks such as Facebook, LinkedIn and Orkut. In particular, - we propose analytical models that characterize propagation dynamics of cross-site scripting and Trojan malware, the two major types of malware propagating in OSNs. Our models assume the topological characteristics of real-world social networks, namely, low average shortest distance, power-law distribution of node degrees and high clustering coefficient. The proposed models were validated using a real-world social network graph. - we present the design and implementation of a cellular botnet named SoCellBot that uses the OSN platform as a means to recruit and control cellular bots on smartphones. SoCellBot utilizes OSN messaging systems as communication channels between bots. We then present a simulation-based analysis of the botnet's strategies to maximize the number of infected victims within a short amount of time and, at the same time, minimize the risk of being detected. - we describe and analyze emerging malware threats in OSNs, namely, clickjacking, extension-based and Magnet malware. We discuss their implementations and working mechanics, and analyze their propagation dynamics via simulations. - we evaluate the performance of several selective monitoring schemes used for malware detection in OSNs. With selective monitoring, we select a set of important users in the network and monitor their and their friends activities and posts for malware threats. These schemes differ in how the set of important users is selected. We evaluate and compare the effectiveness of several selective monitoring schemes in terms of malware detection in OSNs

    High-Performance Modelling and Simulation for Big Data Applications

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    This open access book was prepared as a Final Publication of the COST Action IC1406 “High-Performance Modelling and Simulation for Big Data Applications (cHiPSet)“ project. Long considered important pillars of the scientific method, Modelling and Simulation have evolved from traditional discrete numerical methods to complex data-intensive continuous analytical optimisations. Resolution, scale, and accuracy have become essential to predict and analyse natural and complex systems in science and engineering. When their level of abstraction raises to have a better discernment of the domain at hand, their representation gets increasingly demanding for computational and data resources. On the other hand, High Performance Computing typically entails the effective use of parallel and distributed processing units coupled with efficient storage, communication and visualisation systems to underpin complex data-intensive applications in distinct scientific and technical domains. It is then arguably required to have a seamless interaction of High Performance Computing with Modelling and Simulation in order to store, compute, analyse, and visualise large data sets in science and engineering. Funded by the European Commission, cHiPSet has provided a dynamic trans-European forum for their members and distinguished guests to openly discuss novel perspectives and topics of interests for these two communities. This cHiPSet compendium presents a set of selected case studies related to healthcare, biological data, computational advertising, multimedia, finance, bioinformatics, and telecommunications
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