Abstract. Smartphone users increasingly download and install third-party applications from official application repositories. Attackers may use this centralized application delivery architecture as a security and privacy attack vector. This risk increases since application vetting mechanisms are often not in place and the user is delegated to authorize which functionality and protected resources are accessible by third-party applications. In this paper, we mount a survey to explore the security awareness of smartphone users who download applications from official application repositories (e.g. Google Play, Apple's App Store, etc.). The survey findings suggest a security complacency, as the majority of users trust the app repository, security controls are not enabled or not added, and users disregard security during application selection and installation. As a response to this security complacency, we built a prediction model to indentify users who trust the app repository. Then, the model is assessed, evaluated, and proved to be statistically significant and efficient.

Over the last five years there has been an increase in the frequency and diversity of network attacks. This holds true, as more and more organisations admit compromises on a daily basis. Many misuse and anomaly based Intrusion Detection Systems (IDSs) that rely on either signatures, supervised or statistical methods have been proposed in the literature, but their trustworthiness is debatable. Moreover, as this work uncovers, the current IDSs are based on obsolete attack classes that do not reflect the current attack trends. For these reasons, this paper provides a comprehensive overview of unsupervised and hybrid methods for intrusion detection, discussing their potential in the domain. We also present and highlight the importance of feature engineering techniques that have been proposed for intrusion detection. Furthermore, we discuss that current IDSs should evolve from simple detection to correlation and attribution. We descant how IDS data could be used to reconstruct and correlate attacks to identify attackers, with the use of advanced data analytics techniques. Finally, we argue how the present IDS attack classes can be extended to match the modern attacks and propose three new classes regarding the outgoing network communication.

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Abstract. Smartphones are multi-purpose ubiquitous devices, which face both, smartphone-specific and typical security threats. This paper describes a method for risk assessment that is tailored for smartphones. The method does not treat this kind of device as a single entity. Instead, it identifies smartphone assets and provides a detailed list of specific applicable threats. For threats that use application permissions as the attack vector, risk triplets are facilitated. The triplets associate assets to threats and permission combinations. Then, risk is assessed as a combination of asset impact and threat likelihood. The method utilizes user input, with respect to impact valuation, coupled with statistics for threat likelihood calculation. Finally, the paper provides a case study, which demonstrates the risk assessment method in the Android platform.

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