Biblio

Traditional privacy-preserving data disclosure solutions have focused on protecting the privacy of individual's information with the assumption that all aggregate (statistical) information about individuals is safe for disclosure. Such schemes fail to support group privacy where aggregate information about a group of individuals may also be sensitive and users of the published data may have different levels of access privileges entitled to them. We propose the notion ofεg-Group Differential Privacy that protects sensitive information of groups of individuals at various defined privacy levels, enabling data users to obtain the level of access entitled to them. We present a preliminary evaluation of the proposed notion of group privacy through experiments on real association graph data that demonstrate the guarantees on group privacy on the disclosed data.

Nowadays, Vehicular ad hoc Network as a special class of Mobile ad hoc Network(MANET), provides plenty of services. However, it also brings the privacy protection issues, and there are conflicts between the privacy protection and the services. In this paper, we will propose a privacy protection algorithm based on group signature including two parts, group signature based anonymous verification and batch verification. The anonymous verification is based on the network model we proposed, which can reduce the trust authority burden by dividing the roadside units into different levels, and the batch verification can reduce the time of message verification in one group. We also prove our algorithm can satisfy the demand of privacy protection. Finally, the simulation shows that the algorithm we proposed is better than the BBS on the length of the signature, time delay and packet loss rate.

In the last years, networking scenarios have been evolving, hand-in-hand with new and varied applications with heterogeneous Quality of Service (QoS) requirements. These requirements must be efficiently and effectively delivered. Given its static layered structure and almost complete lack of built-in QoS support, the current TCP/IP-based Internet hinders such an evolution. In contrast, the clean-slate Recursive InterNetwork Architecture (RINA) proposes a new recursive and programmable networking model capable of evolving with the network requirements, solving in this way most, if not all, TCP/IP protocol stack limitations. Network providers can better deliver communication services across their networks by taking advantage of the RINA architecture and its support for QoS. This support allows providing complete information of the QoS needs of the supported traffic flows, and thus, fulfilment of these needs becomes possible. In this work, we focus on the importance of path selection to better ensure QoS guarantees in long-haul RINA networks. We propose and evaluate a programmable strategy for path selection based on flow QoS parameters, such as the maximum allowed latency and packet losses, comparing its performance against simple shortest-path, fastest-path and connection-oriented solutions.

Though immutability has been long-proposed as a way to prevent bugs in software, little is known about how to make immutability support in programming languages effective for software engineers. We designed a new formalism that extends Java to support transitive class immutability, the form of immutability for which there is the strongest empirical support, and implemented that formalism in a tool called Glacier. We applied Glacier successfully to two real-world systems. We also compared Glacier to Java’s final in a user study of twenty participants. We found that even after being given instructions on how to express immutability with final, participants who used final were unable to express immutability correctly, whereas almost all participants who used Glacier succeeded. We also asked participants to make specific changes to immutable classes and found that participants who used final all incorrectly mutated immutable state, whereas almost all of the participants who used Glacier succeeded. Glacier represents a promising approach to enforcing immutability in Java and provides a model for enforcement in other languages.

Participatory sensing enables individuals, each with limited sensing capability, to share measurements and contribute towards developing a complete knowledge of their environment. The success of a participatory sensing application is often measured in terms of the number of users participating. In most cases, an individual’s eagerness to participate depends on the group of users who already participate. For instance, when users share data with their peers in a social network, the engagement of an individual depends on its peers. Such engagement rules have been studied in the context of social networks using the concept of k-core, which assumes that participation is determined solely by network topology. However, in participatory sensing, engagement rules must also consider user heterogeneity, such as differences in sensing capabilities and physical location. To account for heterogeneity, we introduce the concept of (r,s)-core to model the set of participating users. We formulate the problem of maximizing the size of the (r,s)-core using 1) anchor users, who are incentivized to participate regardless of their peers, and by 2) assigning capabilities to users. Since these problems are computationally challenging, we study heuristic algorithms for solving them. Based on real-world social networks as well as random graphs, we provide numerical results showing significant improvement compared to random selection of anchor nodes and label assignments.

The Internet of Things (IoT) is a new paradigm in which every-day objects are interconnected between each other and to the Internet. This paradigm is receiving much attention of the scientific community and it is applied in many fields. In some applications, it is useful to prove that a number of objects are simultaneously present in a group. For example, an individual might want to authorize NFC payment with his mobile only if k of his devices are present to ensure that he is the right person. This principle is known as Grouping-Proofs. However, existing Grouping-Proofs schemes are mostly designed for RFID systems and don't fulfill the IoT characteristics. In this paper, we propose a Threshold Grouping-Proofs for IoT applications. Our scheme uses the Key-Policy Attribute-Based Encryption (KP-ABE) protocol to encrypt a message so that it can be decrypted only if at least k objects are simultaneously present in the same location. A security analysis and performance evaluation is conducted to show the effectiveness of our proposal solution.

Security at virtualization level has always been a major issue in cloud computing environment. A large number of virtual machines that are hosted on a single server by various customers/client may face serious security threats due to internal/external network attacks. In this work, we have examined and evaluated these threats and their impact on OpenStack private cloud. We have also discussed the most popular DOS (Denial-of-Service) attack on DHCP server on this private cloud platform and evaluated the vulnerabilities in an OpenStack networking component, Neutron, due to which this attack can be performed through rogue DHCP server. Finally, a solution, a game-theory based cloud architecture, that helps to detect and prevent DOS attacks in OpenStack has been proposed.

In the age of Big Data, we are witnessing a huge proliferation of digital data capturing our lives and our surroundings. Data privacy is a critical barrier to data analytics and privacy-preserving data disclosure becomes a key aspect to leveraging large-scale data analytics due to serious privacy risks. Traditional privacy-preserving data publishing solutions have focused on protecting individual's private information while considering all aggregate information about individuals as safe for disclosure. This paper presents a new privacy-aware data disclosure scheme that considers group privacy requirements of individuals in bipartite association graph datasets (e.g., graphs that represent associations between entities such as customers and products bought from a pharmacy store) where even aggregate information about groups of individuals may be sensitive and need protection. We propose the notion of $ε$g-Group Differential Privacy that protects sensitive information of groups of individuals at various defined group protection levels, enabling data users to obtain the level of information entitled to them. Based on the notion of group privacy, we develop a suite of differentially private mechanisms that protect group privacy in bipartite association graphs at different group privacy levels based on specialization hierarchies. We evaluate our proposed techniques through extensive experiments on three real-world association graph datasets and our results demonstrate that the proposed techniques are effective, efficient and provide the required guarantees on group privacy.

We present a gradient-based attack against SVM-based forensic techniques relying on high-dimensional SPAM features. As opposed to prior work, the attack works directly in the pixel domain even if the relationship between pixel values and SPAM features can not be inverted. The proposed method relies on the estimation of the gradient of the SVM output with respect to pixel values, however it departs from gradient descent methodology due to the necessity of preserving the integer nature of pixels and to reduce the effect of the attack on image quality. A fast algorithm to estimate the gradient is also introduced to reduce the complexity of the attack. We tested the proposed attack against SVM detection of histogram stretching, adaptive histogram equalization and median filtering. In all cases the attack succeeded in inducing a decision error with a very limited distortion, the PSNR between the original and the attacked images ranging from 50 to 70 dBs. The attack is also effective in the case of attacks with Limited Knowledge (LK) when the SVM used by the attacker is trained on a different dataset with respect to that used by the analyst.

The quantity of information that is collected and stored in computer systems continues to grow rapidly. At the same time, the sensitivity of such information (e.g., detailed medical records) often makes such information valuable to both external attackers, who may obtain information by compromising a system, and malicious insiders, who may misuse information by exercising their authorization. To mitigate compromises and deter misuse, the security administrators of these resources often deploy various types of intrusion and misuse detection systems, which provide alerts of suspicious events that are worthy of follow-up review. However, in practice, these systems may generate a large number of false alerts, wasting the time of investigators. Given that security administrators have limited budget for investigating alerts, they must prioritize certain types of alerts over others. An important challenge in alert prioritization is that adversaries may take advantage of such behavior to evade detection - specifically by mounting attacks that trigger alerts that are less likely to be investigated. In this paper, we model alert prioritization with adaptive adversaries using a Stackelberg game and introduce an approach to compute the optimal prioritization of alert types. We evaluate our approach using both synthetic data and a real-world dataset of alerts generated from the audit logs of an electronic medical record system in use at a large academic medical center.

Tactical networks are generally simple ad-hoc networks in design, however, this simple design often gets complicated, when heterogeneous wireless technologies have to work together to enable seamless multi-hop communications across multiple sessions. In recent years, there has been some significant advances in computational, radio, localization, and networking te, and session's rate i.e., aggregate capacity averaged over a 4-time-slot frame)chnologies, which motivate a clean slate design of the control plane for multi-hop tactical wireless networks. In this paper, we develop a global network optimization framework, which characterizes the control plane for multi-hop wireless tactical networks. This framework abstracts the underlying complexity of tactical wireless networks and orchestrates the the control plane functions. Specifically, we develop a cross-layer optimization framework, which characterizes the interaction between the physical, link, and network layers. By applying the framework to a throughput maximization problem, we show how the proposed framework can be utilized to solve a broad range of wireless multi-hop tactical networking problems.

Assuring communication integrity is a central problem in security. However, overhead costs associated with cryptographic primitives used towards this end introduce significant practical implementation challenges for resource-bounded systems, such as cyberphysical systems. For example, many control systems are built on legacy components which are computationally limited but have strict timing constraints. If integrity protection is a binary decision, it may simply be infeasible to introduce into such systems; without it, however, an adversary can forge malicious messages, which can cause significant physical or financial harm. To bridge the gap between such binary decisions, we propose a stochastic message authentication approach that can explicitly trade computational cost off for security. We introduce a formal game-theoretic framework for optimal stochastic message authentication, providing provable guarantees for resource-bounded systems based on an existing message authentication scheme. We use our framework to investigate attacker deterrence, as well as optimal stochastic message authentication when deterrence is impossible, in both short-term and long-term equilibria. Additionally, we propose two schemes for implementing stochastic message authentication in practice, one for saving computation only at the receiver and one for saving computation at both ends, and demonstrate the associated computational savings using an actual implementation.

Large-scale infrastructures are critical to economic and social development, and hence their continued performance and security are of high national importance. Such an infrastructure often is a system of systems, and its functionality critically depends on the inherent robustness of its constituent systems and its defense strategy for countering attacks. Additionally, interdependencies between the systems play another critical role in determining the infrastructure robustness specified by its survival probability. In this paper, we develop game-theoretic models between a defender and an attacker for a generic system of systems using inherent parameters and conditional survival probabilities that characterize the interdependencies. We derive Nash Equilibrium conditions for the cases of interdependent and independent systems of systems under sum-form utility functions. We derive expressions for the infrastructure survival probability that capture its dependence on cost and system parameters, and also on dependencies that are specified by conditional probabilities. We apply the results to cyber-physical systems which show the effects on system survival probability due to defense and attack intensities, inherent robustness, unit cost, target valuation, and interdependencies.

Due to the unavailability of signatures for previously unknown malware, non-signature malware detection schemes typically rely on analyzing program behavior. Prior behavior based non-signature malware detection schemes are either easily evadable by obfuscation or are very inefficient in terms of storage space and detection time. In this paper, we propose GZero, a graph theoretic approach fast and accurate non-signature malware detection at end hosts. GZero it is effective while being efficient in terms of both storage space and detection time. We conducted experiments on a large set of both benign software and malware. Our results show that GZero achieves more than 99% detection rate and a false positive rate of less than 1%, with less than 1 second of average scan time per program and is relatively robust to obfuscation attacks. Due to its low overheads, GZero can complement existing malware detection solutions at end hosts.

Blockchain has been applied to study data privacy and network security recently. In this paper, we propose a punishment scheme based on the action record on the blockchain to suppress the attack motivation of the edge servers and the mobile devices in the edge network. The interactions between a mobile device and an edge server are formulated as a blockchain security game, in which the mobile device sends a request to the server to obtain real-time service or launches attacks against the server for illegal security gains, and the server chooses to perform the request from the device or attack it. The Nash equilibria (NEs) of the game are derived and the conditions that each NE exists are provided to disclose how the punishment scheme impacts the adversary behaviors of the mobile device and the edge server.

Today's network is distributed and heterogeneous in nature and has numerous applications which affect day to day life, such as e-Banking, e-Booking of tickets, on line shopping etc. Hence the security of the network is crucial. Threats in the network can be due to intrusions. Such threats can be observed and handled using Intrusion Detection System. The security can be achieved using intrusion detection system, which observes the data traffic and identifies it as an intrusion or not. The objective of this paper is to design a model using game theoretic approach for intrusion detection. Game model is designed by defining players, strategies and utility functions to identify the Probe attacks. This model is tested with NSLKDD data set. The model is the Probe attacks are identified by dominated strategies elimination method. Experimental results shows that game model identifies the attacks with good detection rate.

Web applications are the core enabler for most Internet services today. Their standard interfaces allow them to be composed together in different ways in order to support different service workflows. While the modular composition of applications has considerably simplified the provisioning of new Internet services, it has also added new security challenges; the impact of a security breach propagating through the chain far beyond the vulnerable application. To secure web applications, two distinct approaches have been commonly used in the literature. First, white-box approaches leverage the source code in order to detect and fix unintended flaws. Although they cover well the intrinsic flaws within each application, they can barely leverage logic flaws that arise when connecting multiple applications within the same service. On the other hand, black-box approaches analyze the workflow of a service through a set of user interactions, while assuming only little information about its embedded applications. These approaches may have a better coverage, but suffer from a high false positives rate. So far, to the best of our knowledge, there is not yet a single solution that combines both approaches into a common framework. In this paper, we present a new grey-box approach that leverages the advantages of both white-box and black-box. The core component of our system is a semi-supervised learning framework that first learns the nominal behavior of the service using a set of elementary user interactions, and then prune this nominal behavior from attacks that may have occurred during the learning phase. To do so, we leverage a graph-based representation of known attack scenarios that is built using a white-box approach. We demonstrate in this paper the use of our system through a practical use case, including real world attack scenarios that we were able to detect and qualify using our approach.

With the advent of the Internet of Things (IoT) and big data, high fidelity localization and tracking systems that employ cameras, RFIDs, and attached sensors intrude on personal privacy. However, the benefit of localization information sharing enables trend forecasting and automation. To address this challenge, we introduce Wobly, an attribute based signature (ABS) that measures gait. Wobly passively receives Wi-Fi beacons and produces human signatures based on the Doppler Effect and multipath signals without attached devices and out of direct line-of-sight. Because signatures are specific to antenna placement and room configuration and do not require sensor attachments, the identities of the individuals can remain anonymous. However, the gait based signatures are still unique, and thus Wobly is able to track individuals in a building or home. Wobly uses the physical layer channel and the unique human gait as a means of encoding a person's identity. We implemented Wobly on a National Instruments Radio Frequency (RF) test bed. Using a simple naive Bayes classifier, the correct identification rate was 87% with line-of-sight (LoS) and 77% with non-line-of-sight (NLoS).

Decoy routing is a promising new approach for censorship circumvention that relies on traffic re-direction by volunteer autonomous systems. Decoy routing is subject to a fundamental censorship attack, called routing around decoy (RAD), in which the censors re-route their clients' Internet traffic in order to evade decoy routing autonomous systems. Recently, there has been a heated debate in the community on the real-world feasibility of decoy routing in the presence of the RAD attack. Unfortunately, previous studies rely their analysis on heuristic-based mechanisms for decoy placement strategies as well as ad hoc strategies for the implementation of the RAD attack by the censors. In this paper, we perform the first systematic analysis of decoy routing in the presence of the RAD attack. We use game theory to model the interactions between decoy router deployers and the censors in various settings. Our game-theoretic analysis finds the optimal decoy placement strategies–-as opposed to heuristic-based placements–-in the presence of RAD censors who take their optimal censorship actions–-as opposed to some ad hoc implementation of RAD. That is, we investigate the best decoy placement given the best RAD censorship. We consider two business models for the real-world deployment of decoy routers: a central deployment that resembles that of Tor and a distributed deployment where autonomous systems individually decide on decoy deployment based on their economic interests. Through extensive simulation of Internet routes, we derive the optimal strategies in the two models for various censoring countries and under different assumptions about the budget and preferences of the censors and decoy deployers. We believe that our study is a significant step forward in understanding the practicality of the decoy routing circumvention approach.

Cyber-Physical Embedded Systems (CPESs) are distributed embedded systems integrated with various actuators and sensors. When it comes to the issue of CPES security, the most significant problem is the security of Embedded Sensor Networks (ESNs). With the continuous growth of ESNs, the security of transferring data from sensors to their destinations has become an important research area. Due to the limitations in power, storage, and processing capabilities, existing security mechanisms for wired or wireless networks cannot apply directly to ESNs. Meanwhile, ESNs are likely to be attacked by different kinds of attacks in industrial scenarios. Therefore, there is a need to develop new techniques or modify the current security mechanisms to overcome these problems. In this article, we focus on Intrusion Detection (ID) techniques and propose a new attack-defense game model to detect malicious nodes using a repeated game approach. As a direct consequence of the game model, attackers and defenders make different strategies to achieve optimal payoffs. Importantly, error detection and missing detection are taken into consideration in Intrusion Detection Systems (IDSs), where a game tree model is introduced to solve this problem. In addition, we analyze and prove the existence of pure Nash equilibrium and mixed Nash equilibrium. Simulations show that the proposed model can both reduce energy consumption by up to 50% compared with the existing All Monitor (AM) model and improve the detection rate by up to 10% to 15% compared with the existing Cluster Head (CH) monitor model.

Authenticating a user based on her unique behavioral bio-metric traits has been extensively researched over the past few years. The most researched behavioral biometrics techniques are based on keystroke and mouse dynamics. These schemes, however, have been shown to be vulnerable to human-based and robotic attacks that attempt to mimic the user's behavioral pattern to impersonate the user. In this paper, we aim to verify the user's identity through the use of active, cognition-based user interaction in the authentication process. Such interaction boasts to provide two key advantages. First, it may enhance the security of the authentication process as multiple rounds of active interaction would serve as a mechanism to prevent against several types of attacks, including zero-effort attack, expert trained attackers, and automated attacks. Second, it may enhance the usability of the authentication process by actively engaging the user in the process. We explore the cognitive authentication paradigm through very simplistic interactive challenges, called Dynamic Cognitive Games, which involve objects floating around within the images, where the user's task is to match the objects with their respective target(s) and drag/drop them to the target location(s). Specifically, we introduce, build and study Gametrics ("Game-based biometrics"), an authentication mechanism based on the unique way the user solves such simple challenges captured by multiple features related to her cognitive abilities and mouse dynamics. Based on a comprehensive data set collected in both online and lab settings, we show that Gametrics can identify the users with a high accuracy (false negative rates, FNR, as low as 0.02) while rejecting zero-effort attackers (false positive rates, FPR, as low as 0.02). Moreover, Gametrics shows promising results in defending against expert attackers that try to learn and later mimic the user's pattern of solving the challenges (FPR for expert human attacker as low as 0.03). Furthermore, we argue that the proposed biometrics is hard to be replayed or spoofed by automated means, such as robots or malware attacks.