This survey provides a structured and comprehensive overview of research on security and privacy in computer and communication networks that use game-theoretic approaches. We present a selected set of works to highlight the application of game theory in addressing different forms of security and privacy problems in computer networks and mobile applications. We organize the presented works in six main categories: security of the physical and MAC layers, security of self-organizing networks, intrusion detection systems, anonymity and privacy, economics of network security, and cryptography. In each category, we identify security problems, players, and game models. We summarize the main results of selected works, such as equilibrium analysis and security mechanism designs. In addition, we provide a discussion on the advantages, drawbacks, and future direction of using game theory in this field. In this survey, our goal is to instill in the reader an enhanced understanding of different research approaches in applying game-theoretic methods to network security. This survey can also help researchers from various fields develop game-theoretic solutions to current and emerging security problems in computer networking.

/pdf/game-theory-meets-network-security-and-privacy-1r8g3nc948.pdf

Game theory meets network security and privacy

Cloud computing is expected to become a common solution for deploying applications thanks to its capacity to leverage developers from infrastructure management tasks, thus reducing the overall costs and services’ time to market. Several concerns prevent players’ entry in the cloud; security is arguably the most relevant one. Many factors have an impact on cloud security, but it is its multitenant nature that brings the newest and more challenging problems to cloud settings. Here, we analyze the security risks that multitenancy induces to the most established clouds, Infrastructure as a service clouds, and review the literature available to present the most relevant threats, state of the art of solutions that address some of the associated risks. A major conclusion of our analysis is that most reported systems employ access control and encryption techniques to secure the different elements present in a virtualized (multitenant) datacenter. Also, we analyze which are the open issues and challenges to be addressed by cloud systems in the security field.

http://msjavan.persiangig.com/document/Researches/Locking%20the%20sky-%20a%20survey%20on%20IaaS%20cloud%20security.pdf

Locking the sky: a survey on IaaS cloud security

It is generally believed to be a tedious, time consuming, and error-prone process to develop a virtual machine introspection (VMI) tool manually because of the semantic gap. Recent advances in Virtuoso show that we can largely narrow the semantic gap. But it still cannot completely automate the VMI tool generation. In this paper, we present VMST, an entirely new technique that can automatically bridge the semantic gap and generate the VMI tools. The key idea is that, through system wide instruction monitoring, we can automatically identify the introspection related data and redirect these data accesses to the in-guest kernel memory. VMST offers a number of new features and capabilities. Particularly, it automatically enables an in-guest inspection program to become an introspection program. We have tested VMST over 15 commonly used utilities on top of 20 different Linux kernels. The experimental results show that our technique is general (largely OS-agnostic), and it introduces 9.3X overhead on average for the introspected program compared to the native non-redirected one.

http://www.cse.psu.edu/~trj1/cse597-s13/docs/vmi_redirect_oakland_12.pdf

Space Traveling across VM: Automatically Bridging the Semantic Gap in Virtual Machine Introspection via Online Kernel Data Redirection

Security countermeasures help ensure the confidentiality, availability, and integrity of information systems by preventing or mitigating asset losses from Cybersecurity attacks. Due to uncertainty, the financial impact of threats attacking assets is often difficult to measure quantitatively, and thus it is difficult to prescribe which countermeasures to employ. In this research, we describe a decision support system for calculating the uncertain risk faced by an organization under cyber attack as a function of uncertain threat rates, countermeasure costs, and impacts on its assets. The system uses a genetic algorithm to search for the best combination of countermeasures, allowing the user to determine the preferred tradeoff between the cost of the portfolio and resulting risk. Data collected from manufacturing firms provide an example of results under realistic input conditions.

Decision support for Cybersecurity risk planning

Implementations for providing a persistent secure execution environment with a hosted computer are described. A host operating system of a computing system provides an encrypted checkpoint to a persistence module that executes in a secure execution environment of a hardware-protected memory area initialized by a security-enabled processor. The encrypted checkpoint is derived at least partly from another secure execution environment that is cryptographically certifiable as including another hardware-protected memory area established in an activation state to refrain from executing software not trusted by the client system.

Cryptographic certification of secure hosted execution environments

This paper considers a real-time risk assessment method for information systems and networks based on observations from networks sensors such as intrusion detection systems. The system risk is dynamically evaluated using hidden Markov models, providing a mechanism for handling data from sensors with different trustworthiness in terms of false positives and negatives. The method provides a higher level of abstraction for monitoring network security, suitable for risk management and intrusion response applications.

Real-time risk assessment with network sensors and intrusion detection systems

This paper proposes a Distributed Intrusion Prevention System (DIPS), which consists of several IPS over a large network (s), all of which communicate with each other or with a central server, that facilitates advanced network monitoring. A Hidden Markov Model is proposed for sensing intrusions in a distributed environment and to make a one step ahead prediction against possible serious intrusions. DIPS is activated based on the predicted threat level and risk assessment of the protected assets. Intrusions attempts are blocked based on (1) a serious attack that has already occurred (2) rate of packet flow (3) prediction of possible serious intrusions and (4) online risk assessment of the assets possibly available to the intruder. The focus of this paper is on the distributed monitoring of intrusion attempts, the one step ahead prediction of such attempts and online risk assessment using fuzzy inference systems. Preliminary experiment results indicate that the proposed framework is efficient for real time distributed intrusion monitoring and prevention.

/pdf/dips-a-framework-for-distributed-intrusion-prediction-and-3eimspex7r.pdf

DIPS: A Framework for Distributed Intrusion Prediction and Prevention Using Hidden Markov Models and Online Fuzzy Risk Assessment

The use of tools for monitoring the security state of assets in a network is an essential part of network management. Traditional risk assessment methodologies provide a framework for manually determining the risks of assets, and intrusion detection systems can provide alerts regarding security incidents, but these approaches do not provide a real-time high level overview of the risk level of assets. In this paper we further extend a previously proposed real-time risk assessment method to facilitate more flexible modeling with support for a wide range of sensors. Specifically, the paper develops a method for handling continuous-time sensor data and for determining a weighted aggregate of multisensor input.

Multisensor Real-time Risk Assessment using Continuous-time Hidden Markov Models

A Distributed Intrusion Prediction and Prevention Systems (DIPPS) not only detects and prevents possible intrusions but also possesses the capability to predict possible intrusions in a distributed network. Based on the DIPS sensors, instead of merely preventing the attackers or blocking traffic, we propose a fuzzy logic based online risk assessment scheme. The key idea of DIPPS is to protect the network(s) linked to assets, which are considered to be very risky. To implement DIPPS we used a Distributed Intrusion Detection System (DIDS) with extended real time traffic surveillance and online risk assessment. To model and predict the next step of an attacker, we used a Hidden Markov Model (HMM) that captures the interaction between the attacker and the network. The interaction between various DIDS and integration of their output are achieved through a HMM. The novelty of this paper is the detailed development of Fuzzy Logic Controllers to estimate the various risk(s) that are dependent on several other variables based on the inputs from HMM modules and the DIDS agents. To develop the fuzzy risk expert system, if-then fuzzy rules were formulated based on interviews with security experts and network administrators. Preliminary results indicate that such a system is very practical for protecting assets which are prone to attacks or misuse, i.e. highly at risk.

/pdf/fuzzy-online-risk-assessment-for-distributed-intrusion-2utzzj1pyz.pdf

Fuzzy Online Risk Assessment for Distributed Intrusion Prediction and Prevention Systems

In this paper we propose to use a hidden Markov model (HMM) to model sensors for an intrusion prevention system (IPS). Observations from different sensors are aggregated in the HMM and the intrusion frequency security metric is estimated. We use a Markov model that captures the interaction between the attacker and the network to model and predict the next step of an attacker. A new HMM is created and used for updating the estimated system state for each observation, based on the sensor trustworthiness and the time since last observation processed. Our objective is to calculate and maintain a state probability distribution that can be used for intrusion prediction and prevention. We show how our sensor model can be applied to an IPS architecture based on intrusion detection system (IDS) sensors, real-time traffic surveillance and online risk assessment. Our approach is illustrated by a small case study.

Kjetil Haslum

Papers

Real-time risk assessment with network sensors and intrusion detection systems

DIPS: A Framework for Distributed Intrusion Prediction and Prevention Using Hidden Markov Models and Online Fuzzy Risk Assessment

Multisensor Real-time Risk Assessment using Continuous-time Hidden Markov Models

Fuzzy Online Risk Assessment for Distributed Intrusion Prediction and Prevention Systems

Real-time intrusion prevention and security analysis of networks using HMMs