101 research outputs found

    Behaviour based anomaly detection system for smartphones using machine learning algorithm

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    In this research, we propose a novel, platform independent behaviour-based anomaly detection system for smartphones. The fundamental premise of this system is that every smartphone user has unique usage patterns. By modelling these patterns into a profile we can uniquely identify users. To evaluate this hypothesis, we conducted an experiment in which a data collection application was developed to accumulate real-life dataset consisting of application usage statistics, various system metrics and contextual information from smartphones. Descriptive statistical analysis was performed on our dataset to identify patterns of dissimilarity in smartphone usage of the participants of our experiment. Following this analysis, a Machine Learning algorithm was applied on the dataset to create a baseline usage profile for each participant. These profiles were compared to monitor deviations from baseline in a series of tests that we conducted, to determine the profiling accuracy. In the first test, seven day smartphone usage data consisting of eight features and an observation interval of one hour was used and an accuracy range of 73.41% to 100% was achieved. In this test, 8 out 10 user profiles were more than 95% accurate. The second test, utilised the entire dataset and achieved average accuracy of 44.50% to 95.48%. Not only these results are very promising in differentiating participants based on their usage, the implications of this research are far reaching as our system can also be extended to provide transparent, continuous user authentication on smartphones or work as a risk scoring engine for other Intrusion Detection System

    Solving SEI model using non-standard finite difference and high order extrapolation with variable step length

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    A high-level method was obtained to solve the SEI model problem involving Symmetrization measures in numerical calculations through the Implicit Midpoint Rule method (IMR). It is obtained using Non-Standard Finite Difference Schemes (NSFD) with Extrapolation techniques combined. In solving differential equation problems numerically, the Extrapolated SEI model method is able to generate more accurate results than the existing numerical method of SEI model. This study aims to investigate the accuracy and efficiency of computing between Extrapolated One-Step Active Symmetry Implicit Midpoint Rule method (1ASIMR), Extrapolated One-Step Active Symmetry Implicit Midpoint Rule method (2ASIMR), Extrapolated One-Step Passive Symmetry Midpoint Rule method (1PSIMR) and the extrapolated Two-Step Passive Symmetry Midpoint Rule method (2PSIMR). The results show that the 1ASIMR method is the most accurate method. For the determination of the efficiency of 2ASIMR and 2PSIMR methods have high efficiency. At the end of the study, the results from the numerical method obtained show that Extrapolation using Non-Standard Finite Difference has higher accuracy than the existing Implicit Midpoint Rule method

    A Survey on Security for Mobile Devices

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    Nowadays, mobile devices are an important part of our everyday lives since they enable us to access a large variety of ubiquitous services. In recent years, the availability of these ubiquitous and mobile services has signicantly increased due to the dierent form of connectivity provided by mobile devices, such as GSM, GPRS, Bluetooth and Wi-Fi. In the same trend, the number and typologies of vulnerabilities exploiting these services and communication channels have increased as well. Therefore, smartphones may now represent an ideal target for malware writers. As the number of vulnerabilities and, hence, of attacks increase, there has been a corresponding rise of security solutions proposed by researchers. Due to the fact that this research eld is immature and still unexplored in depth, with this paper we aim to provide a structured and comprehensive overview of the research on security solutions for mobile devices. This paper surveys the state of the art on threats, vulnerabilities and security solutions over the period 2004-2011. We focus on high-level attacks, such those to user applications, through SMS/MMS, denial-of-service, overcharging and privacy. We group existing approaches aimed at protecting mobile devices against these classes of attacks into dierent categories, based upon the detection principles, architectures, collected data and operating systems, especially focusing on IDS-based models and tools. With this categorization we aim to provide an easy and concise view of the underlying model adopted by each approach

    Data-Driven Approach for Automatic Telephony Threat Analysis and Campaign Detection

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    The growth of the telephone network and the availability of Voice over Internet Protocol (VoIP) have both contributed to the availability of a flexible and easy to use artifact for users, but also to a significant increase in cyber-criminal activity. These criminals use emergent technologies to conduct illegal and suspicious activities. For instance, they use VoIP’s flexibility to abuse and scam victims. A lot of interest has been expressed into the analysis and assessment of telephony cyber-threats. A better understanding of these types of abuse is required in order to detect, mitigate, and attribute these attacks. The purpose of this research work is to generate relevant and timely telephony abuse intelligence that can support the mitigation and/or the investigation of such activities. To achieve this objective, we present, in this thesis, the design and implementation of a Telephony Abuse Intelligence Framework (TAINT) that automatically aggregates, analyzes and reports on telephony abuse activities. Such a framework monitors and analyzes, in near-real-time, crowd-sourced telephony complaints data from various sources. We deploy our framework on a large dataset of telephony complaints, spanning over seven years, to provide in-depth insights and intelligence about merging telephony threats. The framework presented in this thesis is of paramount importance when it comes to the mitigation, the prevention and the attribution of telephony abuse incidents. We analyze the data and report on the complaint distribution, the used numbers and the spoofed callers’ identifiers. In addition, we identify and geo-locate the sources of the phone calls, and further investigate the underlying telephony threats. Moreover, we quantify the similarity between reported phone numbers to unveil potential groups that are behind specific telephony abuse activities that are actually launched as telephony abuse campaigns

    Community-Based Intrusion Detection

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    Today, virtually every company world-wide is connected to the Internet. This wide-spread connectivity has given rise to sophisticated, targeted, Internet-based attacks. For example, between 2012 and 2013 security researchers counted an average of about 74 targeted attacks per day. These attacks are motivated by economical, financial, or political interests and commonly referred to as “Advanced Persistent Threat (APT)” attacks. Unfortunately, many of these attacks are successful and the adversaries manage to steal important data or disrupt vital services. Victims are preferably companies from vital industries, such as banks, defense contractors, or power plants. Given that these industries are well-protected, often employing a team of security specialists, the question is: How can these attacks be so successful? Researchers have identified several properties of APT attacks which make them so efficient. First, they are adaptable. This means that they can change the way they attack and the tools they use for this purpose at any given moment in time. Second, they conceal their actions and communication by using encryption, for example. This renders many defense systems useless as they assume complete access to the actual communication content. Third, their actions are stealthy — either by keeping communication to the bare minimum or by mimicking legitimate users. This makes them “fly below the radar” of defense systems which check for anomalous communication. And finally, with the goal to increase their impact or monetisation prospects, their attacks are targeted against several companies from the same industry. Since months can pass between the first attack, its detection, and comprehensive analysis, it is often too late to deploy appropriate counter-measures at businesses peers. Instead, it is much more likely that they have already been attacked successfully. This thesis tries to answer the question whether the last property (industry-wide attacks) can be used to detect such attacks. It presents the design, implementation and evaluation of a community-based intrusion detection system, capable of protecting businesses at industry-scale. The contributions of this thesis are as follows. First, it presents a novel algorithm for community detection which can detect an industry (e.g., energy, financial, or defense industries) in Internet communication. Second, it demonstrates the design, implementation, and evaluation of a distributed graph mining engine that is able to scale with the throughput of the input data while maintaining an end-to-end latency for updates in the range of a few milliseconds. Third, it illustrates the usage of this engine to detect APT attacks against industries by analyzing IP flow information from an Internet service provider. Finally, it introduces a detection algorithm- and input-agnostic intrusion detection engine which supports not only intrusion detection on IP flow but any other intrusion detection algorithm and data-source as well

    Darknet as a Source of Cyber Threat Intelligence: Investigating Distributed and Reflection Denial of Service Attacks

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    Cyberspace has become a massive battlefield between computer criminals and computer security experts. In addition, large-scale cyber attacks have enormously matured and became capable to generate, in a prompt manner, significant interruptions and damage to Internet resources and infrastructure. Denial of Service (DoS) attacks are perhaps the most prominent and severe types of such large-scale cyber attacks. Furthermore, the existence of widely available encryption and anonymity techniques greatly increases the difficulty of the surveillance and investigation of cyber attacks. In this context, the availability of relevant cyber monitoring is of paramount importance. An effective approach to gather DoS cyber intelligence is to collect and analyze traffic destined to allocated, routable, yet unused Internet address space known as darknet. In this thesis, we leverage big darknet data to generate insights on various DoS events, namely, Distributed DoS (DDoS) and Distributed Reflection DoS (DRDoS) activities. First, we present a comprehensive survey of darknet. We primarily define and characterize darknet and indicate its alternative names. We further list other trap-based monitoring systems and compare them to darknet. In addition, we provide a taxonomy in relation to darknet technologies and identify research gaps that are related to three main darknet categories: deployment, traffic analysis, and visualization. Second, we characterize darknet data. Such information could generate indicators of cyber threat activity as well as provide in-depth understanding of the nature of its traffic. Particularly, we analyze darknet packets distribution, its used transport, network and application layer protocols and pinpoint its resolved domain names. Furthermore, we identify its IP classes and destination ports as well as geo-locate its source countries. We further investigate darknet-triggered threats. The aim is to explore darknet inferred threats and categorize their severities. Finally, we contribute by exploring the inter-correlation of such threats, by applying association rule mining techniques, to build threat association rules. Specifically, we generate clusters of threats that co-occur targeting a specific victim. Third, we propose a DDoS inference and forecasting model that aims at providing insights to organizations, security operators and emergency response teams during and after a DDoS attack. Specifically, this work strives to predict, within minutes, the attacks’ features, namely, intensity/rate (packets/sec) and size (estimated number of compromised machines/bots). The goal is to understand the future short-term trend of the ongoing DDoS attacks in terms of those features and thus provide the capability to recognize the current as well as future similar situations and hence appropriately respond to the threat. Further, our work aims at investigating DDoS campaigns by proposing a clustering approach to infer various victims targeted by the same campaign and predicting related features. To achieve our goal, our proposed approach leverages a number of time series and fluctuation analysis techniques, statistical methods and forecasting approaches. Fourth, we propose a novel approach to infer and characterize Internet-scale DRDoS attacks by leveraging the darknet space. Complementary to the pioneer work on inferring DDoS activities using darknet, this work shows that we can extract DoS activities without relying on backscattered analysis. The aim of this work is to extract cyber security intelligence related to DRDoS activities such as intensity, rate and geographic location in addition to various network-layer and flow-based insights. To achieve this task, the proposed approach exploits certain DDoS parameters to detect the attacks and the expectation maximization and k-means clustering techniques in an attempt to identify campaigns of DRDoS attacks. Finally, we conclude this work by providing some discussions and pinpointing some future work

    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

    Analysis of mobile banking malware on the Android operating system

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    The Android platform is the fastest growing smartphone operating system to date. Consequently, malware on Android OS has been increasing at an alarming rate. Similar to Windows-based malware, Android malware also have different families which are responsible for different malicious activities. In this thesis, we focused on one particular group of Android malware which is designed to target banks and financial institutions. These banking malware use different techniques to attack bank clients and banking servers. A coherent framework to analyse the behaviour of these malware needs to be developed, so the impact of theirs attacks could be minimised. This thesis investigates a systematic analysis to understand these malware’s behaviour and distribution method. From public and private sources, 37 samples of banking malware have been collected which represent eight major Android Banking malware families. In addition, we also analysed malware source code by reverse engineering all malware samples. As a result of analysis, a clear overview and better understanding of mobile banking malware on Android OS was established. The results indicated that Android banking malware is evolving in technique and will become more difficult to analyse in the future.Masters by Researc

    Enabling NATO’s Collective Defense: Critical Infrastructure Security and Resiliency (NATO COE-DAT Handbook 1)

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    In 2014 NATO’s Center of Excellence-Defence Against Terrorism (COE-DAT) launched the inaugural course on “Critical Infrastructure Protection Against Terrorist Attacks.” As this course garnered increased attendance and interest, the core lecturer team felt the need to update the course in critical infrastructure (CI) taking into account the shift from an emphasis on “protection” of CI assets to “security and resiliency.” What was lacking in the fields of academe, emergency management, and the industry practitioner community was a handbook that leveraged the collective subject matter expertise of the core lecturer team, a handbook that could serve to educate government leaders, state and private-sector owners and operators of critical infrastructure, academicians, and policymakers in NATO and partner countries. Enabling NATO’s Collective Defense: Critical Infrastructure Security and Resiliency is the culmination of such an effort, the first major collaborative research project under a Memorandum of Understanding between the US Army War College Strategic Studies Institute (SSI), and NATO COE-DAT. The research project began in October 2020 with a series of four workshops hosted by SSI. The draft chapters for the book were completed in late January 2022. Little did the research team envision the Russian invasion of Ukraine in February this year. The Russian occupation of the Zaporizhzhya nuclear power plant, successive missile attacks against Ukraine’s electric generation and distribution facilities, rail transport, and cyberattacks against almost every sector of the country’s critical infrastructure have been on world display. Russian use of its gas supplies as a means of economic warfare against Europe—designed to undermine NATO unity and support for Ukraine—is another timely example of why adversaries, nation-states, and terrorists alike target critical infrastructure. Hence, the need for public-private sector partnerships to secure that infrastructure and build the resiliency to sustain it when attacked. Ukraine also highlights the need for NATO allies to understand where vulnerabilities exist in host nation infrastructure that will undermine collective defense and give more urgency to redressing and mitigating those fissures.https://press.armywarcollege.edu/monographs/1951/thumbnail.jp
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