Daniele Sgandurra

A Survey on Security for Mobile Devices

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 significantly increased due to the different 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 field 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, by focusing on high-level attacks, such those to user applications. We group existing approaches aimed at protecting mobile devices against these classes of attacks into different 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.

Cloud security is not (just) virtualization security: a short paper

Cloud infrastructure commonly relies on virtualization. Customers provide their own VMs, and the cloud provider runs them often without knowledge of the guest OSes or their configurations. However, cloud customers also want effective and efficient security for their VMs. Cloud providers offering security-as-a-service based on VM introspection promise the best of both worlds: efficient centralization and effective protection. Since customers can move images from one cloud to another, an effective solution requires learning what guest OS runs in each VM and securing the guest OS without relying on the guest OS functionality or an initially secure guest VM state. We present a solution that is highly scalable in that it (i) centralizes guest protection into a security VM, (ii) supports Linux and Windows operating systems and can be easily extended to support new operating systems, (iii) does not assume any a-priori semantic knowledge of the guest, (iv) does not require any a-priori trust assumptions into any state of the guest VM. While other introspection monitoring solutions exist, to our knowledge none of them monitor guests on the semantic level required to effectively support both white- and black-listing of kernel functions, or allows to start monitoring VMs at any state during run-time, resumed from saved state, and cold-boot without the assumptions of a secure start state for monitoring.

MADAM: a multi-level anomaly detector for android malware

Currently, in the smartphone market, Android is the platform with the highest share. Due to this popularity and also to its open source nature, Android-based smartphones are now an ideal target for attackers. Since the number of malware designed for Android devices is increasing fast, Android users are looking for security solutions aimed at preventing malicious actions from damaging their smartphones. In this paper, we describe MADAM, a Multi-level Anomaly Detector for Android Malware. MADAM concurrently monitors Android at the kernel-level and user-level to detect real malware infections using machine learning techniques to distinguish between standard behaviors and malicious ones. The first prototype of MADAM is able to detect several real malware found in the wild. The device usability is not affected by MADAM due to the low number of false positives generated after the learning phase.

MADAM: Effective and Efficient Behavior-based Android Malware Detection and Prevention

Android users are constantly threatened by an increasing number of malicious applications (apps), generically called malware. Malware constitutes a serious threat to user privacy, money, device and file integrity. In this paper we note that, by studying their actions, we can classify malware into a small number of behavioral classes, each of which performs a limited set of misbehaviors that characterize them. These misbehaviors can be defined by monitoring features belonging to different Android levels. In this paper we present MADAM, a novel host-based malware detection system for Android devices which simultaneously analyzes and correlates features at four levels: kernel, application, user and package, to detect and stop malicious behaviors. MADAM has been specifically designed to take into account those behaviors that are characteristics of almost every real malware which can be found in the wild. MADAM detects and effectively blocks more than 96 percent of malicious apps, which come from three large datasets with about 2,800 apps, by exploiting the cooperation of two parallel classifiers and a behavioral signature-based detector. Extensive experiments, which also includes the analysis of a testbed of 9,804 genuine apps, have been conducted to show the low false alarm rate, the negligible performance overhead and limited battery consumption.

Building Trustworthy Intrusion Detection through VM Introspection

Psyco-Virt is a high assurance intrusion detection tool that merges host and network intrusion detection technologies with virtual machine introspection. Psyco-Virt architecture includes a cluster of virtual machines, the monitored VMs, which run the OS and applications of interest, and a further VM, the introspection one. Several agents distributed across the monitored VMs execute network and host IDS tools to discover attempted intrusions/attacks on the monitored VMs. The introspection VM makes the detection tools trustworthy by running an introspector and a director to discover any attempt to maliciously modify the kernel, the agents and the IDSes hosted on a monitored VM. On each monitored VM a collector gathers the alerts generated by the agents and forwards them to the director through a control network dedicated to data exchange among the agents and the introspection VM. The director on the introspection VM filters all the alerts and delegates the execution of a proper action to a notifier whenever an intrusion or an attempt to modify the IDSes is detected. In such cases, a monitored VM can either be stopped or frozen and its current state saved in a file for a later, deeper inspection. After describing Psyco-Virt, we discuss some examples of agents and functions using introspection and present preliminary results and performance figures of a first prototype.

Hierarchical, model-based risk management of critical infrastructures

Risk management is a process that includes several steps, from vulnerability analysis to the formulation of a risk mitigation plan that selects countermeasures to be adopted. With reference to an information infrastructure, we present a risk management strategy that considers a sequence of hierarchical models, each describing dependencies among infrastructure components. A dependency exists anytime a security-related attribute of a component depends upon the attributes of other components. We discuss how this notion supports the formal definition of risk mitigation plan and the evaluation of the infrastructure robustness. A hierarchical relation exists among models that are analyzed because each model increases the level of details of some components in a previous one. Since components and dependencies are modeled through a hypergraph, to increase the model detail level, some hypergraph nodes are replaced by more and more detailed hypergraphs. We show how critical information for the assessment can be automatically deduced from the hypergraph and define conditions that determine cases where a hierarchical decomposition simplifies the assessment. In these cases, the assessment has to analyze the hypergraph that replaces the component rather than applying again all the analyses to a more detailed, and hence larger, hypergraph. We also show how the proposed framework supports the definition of a risk mitigation plan and discuss some indicators of the overall infrastructure robustness. Lastly, the development of tools to support the assessment is discussed.

Evolution of Attacks, Threat Models, and Solutions for Virtualized Systems

Virtualization technology enables Cloud providers to efficiently use their computing services and resources. Even if the benefits in terms of performance, maintenance, and cost are evident, however, virtualization has also been exploited by attackers to devise new ways to compromise a system. To address these problems, research security solutions have evolved considerably over the years to cope with new attacks and threat models. In this work, we review the protection strategies proposed in the literature and show how some of the solutions have been invalidated by new attacks, or threat models, that were previously not considered. The goal is to show the evolution of the threats, and of the related security and trust assumptions, in virtualized systems that have given rise to complex threat models and the corresponding sophistication of protection strategies to deal with such attacks. We also categorize threat models, security and trust assumptions, and attacks against a virtualized system at the different layers—in particular, hardware, virtualization, OS, and application.

Measuring Semantic Integrity for Remote Attestation

We propose a framework for the attestation of the integrity of a remote system that considers not only the configuration of the system to be attested but also its current behaviour. The resulting architecture, called Virtual machine Integrity Measurement System (VIMS), is based upon virtualization technology and it runs two virtual machines on a system to be attested, i.e. the Client (C-VM) and the Assurance VM (A-VM). A generic remote server (REM-S) accepts incoming connections and cooperates with the A-VM to authenticate and attest the integrity of the C-VM and of the software it runs. The A-VM is a shadow machine that exploits virtual machine introspection to apply a set of consistency checks on the configuration of the C-VM and on the software it currently runs. The checks depend upon the security policies that the REM-S establishes in the initial connection handshake. The REM-S defines both the complexity of checks to be applied and the frequency of their execution and it communicates the security policy to the A-VM through a control channel. Policies that can be applied range from the one that simply checks the integrity of the binaries loaded by the C-VM to those that continuously monitor the dynamic behaviour of applications to discover attacks that alter their expected behaviour. The control channel also transmits the results of the checks from the A-VM to the REM-S. As an example, remote attestation can be adopted when a client software on the C-VM tries to establish a secure channel to a REM-S on an Intranet. After describing the overall VIMS architecture, we present and discuss the implementation and the performance of a first prototype.

Assessing ICT risk through a Monte Carlo method

To assess and manage the risk due to an information and communication system before its deployment, data of interest can be produced by a Monte Carlo method. This paper presents Haruspex, a software tool that applies a Monte Carlo method to simulate intelligent and adaptive threat agents that reach predefined goals through plan with several attacks. The samples that Haruspex collects are used to compute statistics on the agent’s impacts and their plans as well as to select cost-effective countermeasures. We describe the rationale and the implementation of Haruspex, the inputs it requires and the simulation of how the agents select and implement their plans. After discussing the validation and the performance of the first version of Haruspex, we present a case study and the first set of experimental results.

Securing a Community Cloud

Virtual Interacting Network CommunIty (Vinci) is a software architecture that exploits virtualization to secure a community cloud, i.e. a cloud system shared among communities with distinct security levels and reliability requirements. A community consists of a set of users, their applications, a set of services and of shared resources. Users with distinct privileges and applications with distinct trust levels belong to distinct communities. Rather than acquiring and managing its own physical infrastructure, a community defines a virtual ICT infrastructure, i.e. an overlay, by instantiating and interconnecting virtual machines (VMs) defined from a small set of templates. Vinci includes templates to run user applications, protect shared resources and control traffic among communities to filter out malware or distributed attacks. The adoption of alternative VM templates minimizes the complexity of each VM and increases the robustness of both the VMs and of the overall infrastructure. The resulting overlays are mapped onto the cloud infrastructure or, from another perspective, they access an infrastructure service. The cloud provider defines a further overlay that interconnects VMs to manage the infrastructure resources and configure the VMs at start-up.