The paradigm of Privacy by Design aims to integrate privacy early in the product development life cycle. One element of this is to conduct threat modeling with developers to identify privacy threats that engender from the architecture design of the product. In this paper, we propose a systematic lightweight privacy threat modeling framework (MAP) based on attacker personas that is both easy to operationalize and scale. MAP leverages existing privacy threat frameworks to provide an operational roadmap based on relevant threat actors, associated threats, and resulting harm to individuals as well as organizations. We implement MAP as a persona picker tool that threat modelers can use as a menu select to identify, investigate, and remediate relevant threats based on product developer’s scope of privacy risk. We conclude by testing the framework using a repository of 207 privacy breaches extracted from the VERIS Community Database.

Intrusion detection in modern enterprise networks faces challenges due to the increasing large volume of data and insufficient training data for anomaly detections. In this work, we propose a novel network topology for improved intrusion detection through multi-phase data monitoring system. Rather than the all-or-nothing approach to terminate all sessions identified as suspicious, the topology route traffic to different servers replicas with different monitoring intensity level based on their likelihood of attacks. This topology leverages recent advances in software-defined networking (SDN) to dynamically route such sessions into risk-appropriate computing environments. These environments offer enhanced training opportunities intrusion detection systems (IDSes) by exposing data streams that would not have been observable had the session merely been terminated at the first sign of maliciousness. They also afford defenders finer- grained risk management by supporting a continuum of endpoint environments, ranging from fully trusted, to semi-trusted, to fully untrusted, for example.

Distributed Denial of Service (DDoS) attacks remain one of the top threats to enterprise networks and ISPs nowadays. It can cause tremendous damage by bringing down online websites or services. Existing DDoS defense solutions either brings high cost such as upgrading existing firewall or IPS, or bring excessive traffic delay by using third-party cloud-based DDoS filtering services. In this work, we propose a DDoS defense framework that utilizes Network Function Virtualization (NFV) architecture to provide low cost and highly flexible solutions for enterprises. In particular, the system uses virtual network agents to perform attack traffic filtering before they are forwarded to the target server. Agents are created on demand to verify the authenticity of the source of packets, and drop spoofed packets in order protect the target server. Furthermore, we design a scalable and flexible dispatcher to forward packets to corresponding agents for processing. A bucket-based forwarding mechanism is used to improve the scalability of the dispatcher through batching forwarding. The dispatcher can also adapt to agent addition and removal. Our simulation results demonstrate that the dispatcher can effectively serve a large volume of traffic with low dropping rate. The system can successfully mitigate SYN flood attack by introducing minimal performance degradation to legitimate traffic.

In current Android architecture, users have to decide whether an app is safe to use or not. Expert users can make savvy decisions to avoid unnecessary privacy breach. However, the majority of normal users are not technically capable or do not care to consider privacy implications to make safe decisions. To assist the technically incapable crowd, we propose DroidNet, an Android permission control framework based on crowdsourcing. At its core, DroidNet runs new apps under probation mode without granting their permission requests up-front. It provides recommendations on whether to accept or reject the permission requests based on decisions from peer expert users. To seek expert users, we propose an expertise ranking algorithm using a transitional Bayesian inference model. The recommendation is based on the aggregated expert responses and its confidence level. Our simulation and real user experimental results demonstrate that DroidNet provides accurate recommendations and cover the majority of app requests given a small coverage from a small set of initial experts.

The increasing popularity of Android phones and its open app market system have caused the proliferation of malicious Android apps. The increasing sophistication and diversity of the malicious Android apps render the conventional malware detection techniques ineffective, which results in a large number of malicious applications remaining undetected. This calls for more effective techniques for detection and classification of Android malware. Hence, in this paper, we present an Android malicious application detection framework based on the Support Vector Machine (SVM) and Active Learning technologies. In our approach, we extract applications' activities while in execution and map them into a feature set, we then attach timestamps to some features in the set. We show that our novel use of time-dependent behavior tracking can significantly improve the malware detection accuracy. In particular, we build an active learning model using Expected error reduction query strategy to integrate new informative instances of Android malware and retrain the model to be able to do adaptive online learning. We evaluate our model through a set of experiments on the DREBIN benchmark malware dataset. Our evaluation results show that the proposed approach can accurately detect malicious applications and improve updatability against new malware.

In current Android systems, the application recommendation function is an important feature that users can use to find a similar application to replace a targeted one. The current recommendation system provided through Google and the Google Play store presumably recommends applications similar to a target application while accounting for the popularity of each application. However, it does not take the security features of each application or users preferences into consideration when doing so. In this paper, we propose DroidVisor, an Android tool that provides users with fine-grained and customizable application recommendations. Compared to the Google store recommendation function, DroidVisor does not only use the similarity to a preselected target application, but also considers other metrics such as popularity, security, and usability. More specifically, DroidVisor provides an interface for users to configure the weight of each metric and a recommendation algorithm that generates a list of recommended applications based on the combined scores. We evaluate our proposed criteria and the quality of recommendation through use case studies. Finally, we present our findings through a discussion of accuracy as well as possible ways to improve our recommendation results.

High-profile and often destructive distributed denial of service (DDoS) attacks continue to be one of the top security concerns as the DDoS attacks volumes are increasing constantly. Among them, the SYN Flood attack is the most common type. Conventional DDoS defense solutions may not be preferable, since they demand highly capable hardware resources, which induce high cost and long deployment cycle. The emerging of network function virtualization (NFV) technology introduces new opportunities to decrease the amount of proprietary hardware that is needed to launch and operate network services. In this paper, we propose a DDoS defense mechanism named CoFence, which facilitates a “domain-helps-domain” collaboration network among NFV-based domain networks. CoFence allows domain networks to help each other in handling large volume of DDoS attacks through resource sharing. Specifically, we design a dynamic resource allocation mechanism for domains so that the resource allocation is fair, efficient, and incentive-compatible. The resource sharing mechanism is modeled as a multi-leader-follower Stackelberg game. In this game, all domains have a degree of control to maximize their own utility. The resource supplier domains determine the amount of resource to each requesting peer based on optimizing a reciprocal-based utility function. On the other hand, the resource requesting domains decide the level of demand to send to the resource supplier domains in order to reach sufficient support. Our simulation results demonstrate that the designed resource allocation game is effective, incentive compatible, fair, and reciprocal under its Nash equilibrium.

With the exponential growth of the Internet use, the impact of cyber attacks are growing rapidly. Distributed Denial of Service (DDoS) attacks are the most common but damaging type of cyber attacks. Among them SYN Flood attack is the most common type. Existing DDoS defense strategies are encountering obstacles due to their high cost and low flexibility. The emerging of Network Function Virtualization (NFV) technology introduces new opportunities for low-cost and flexible DDoS defense solutions. In this work, we propose CoFence - a DDoS defense mechanism which facilitates a collaboration framework among NFV-based peer domain networks. CoFence allows domain networks help each others handle large volumes of DDoS attacks through resource sharing. Specifically, we focus on the resource allocation problem in the collaboration framework. Through CoFence a domain network decides the amount of resource to share with other peers based on a reciprocal-based utility function. Our simulation results demonstrate the designed resource allocation system is effective, incentive compatible, fair, and reciprocal.

RecDroid is a smartphone permission management system which provides users with a fine-grained real-time app permission control and a recommendation system regarding whether to grant the permission or not based on expert users' responses in the network. However, in such a system, malware owners may create multiple bot users to misguide the recommendation system by providing untruthful responses on the malicious app. Threshold-based detection method can detect malicious users which are dishonest on many apps, but it cannot detect malicious users that target on some specific apps. In this work, we present a clustering-based method called BotTracer to finding groups of bot users controlled by the same masters, which can be used to detect bot users with high reputation scores. The key part of the proposed method is to map the users into a graph based on their similarity and apply a clustering algorithm to group users together. We evaluate our method using a set of simulated users' profiles, including malicious users and regular ones. Our experimental results demonstrate high accuracy in terms of detecting malicious users. Finally, we discuss several clustering features and their impact on the clustering results.

RecDroid is a smartphone permission response recommendation system which utilizes the responses from expert users in the network to help inexperienced users. However, in such system, malicious users can mislead the recommendation system by providing untruthful responses. Although detection system can be deployed to detect the malicious users, and exclude them from recommendation system, there are still undetected malicious users that may cause damage to RecDroid. Therefore, relying on environment knowledge to detect the malicious users is not sufficient. In this work, we present a game-theoretic model to analyze the interaction (request/response) between RecDroid users and RecDroid system using a static Bayesian game formulation. In the game RecDroid system chooses the best response strategy to minimize its loss from malicious users. We analyze the game model and explain the Nash equilibrium in a static scenario under different conditions. Through the static game model we discuss the strategy that RecDroid can adopt to disincentivize attackers in the system, so that attackers are discouraged to perform malicious users attack. Finally, we discuss several game parameters and their impact on players' outcome.

With the exponential growth of smartphone apps, it is prohibitive for apps market places, such as Google App Store for example, to thoroughly verify if an app is legitimate or malicious. As a result, mobile users are left to decide for themselves whether an app is safe to use. Even worse, recent studies have shown that most apps in markets request to collect data irrelevant to the main functions of the apps, which could cause leaking of private information or inefficient use of mobile resources. To assist users to make a right decision as for whether a permission request should be accepted, we propose RecDroid. RecDroid is a crowdsourcing recommendation framework that collects apps' permission requests and users' permission responses, from which a ranking algorithm is used to evaluate the expertise level of users and a voting algorithm is used to compute an appropriate response to the permission request (accept or reject). To bootstrap the recommendation system, RecDroid relies on a small set of seed expert users that could make reliable recommendations for a small set of application. Our evaluation results show that RecDroid can provide high accuracy and satisfying coverage with careful selection of parameters. The results also show that a small coverage from seed experts is sufficient for RecDroid to cover the majority of the app requests.

Nowadays, it is prohibitive for apps market places, such as Google App Store, to thoroughly verify an app's resource permission requests to be legitimate or malicious. As a result, mobile users are left to decide for themselves whether an app is safe to use or not. To assist users to make correct decisions as for whether to accept a permission request or not, we propose RecDroid. RecDroid is a crowdsourcing recommendation framework that collects apps' permission requests and users' responses to those requests, from which an experts ranking algorithm is used to seek expert users in the system and a recommendation algorithm is used to suggest appropriate responses to permission requests (accept or reject) based on experts' responses. In this demo, we demonstrate a user case to show how the RecDroid system assists users in permission control. We also explain the major principles and processes behind that support the RecDroid recommendation system.