We study the problem of designing a dynamic mechanism for security management in an interconnected multi-agent system with N strategic agents and one coordinator. The system is modeled as a network of N vertices. Each agent resides in one of the vertices of the network and has a privately known security state that describes its safety level at each time. The evolution of an agent's security state depends on its own state, the states of its neighbors in the network and on actions taken by a network coordinator. Each agent's utility at time instant t depends on its own state, the states of its neighbors in the network and on actions taken by a network coordinator. The objective of the coordinator is to take security actions to maximize the long-term expected social surplus. Being strategic, agents need to be incentivized to reveal their private security state information. This results in a dynamic mechanism design problem for the coordinator. We leverage the inter-temporal correlations between the agents' security states to identify sufficient conditions under which an incentive compatible expected social surplus maximizing mechanism can be constructed. We describe construction of the desired mechanism in two special cases of our formulation.

Data outsourcing is a promising technical paradigm to facilitate cost-effective realtime data storage, processing, and dissemination. In such a system, a data owner proactively pushes a stream of data records to a third-party cloud service provider (CSP) for storage, which in turn processes various types of queries from end users on the data owner's behalf. This paper considers outsourced multi-version key-value stores that have gained increasing popularity in recent years, where a critical security challenge is to ensure the CSP return both authentic and fresh data in response to end users' queries. Despite several recent attempts on authenticating data freshness in outsourced key-value stores, they either incur excessively high communication cost or can only offer very limited real-time guarantee. To fill this gap, this paper introduces KV-Fresh, a novel freshness authentication scheme for outsourced key-value stores that offers strong real-time guarantee. KV-Fresh is designed based on a novel data structure, Linked Key Span Merkle Hash Tree, which enables highly efficient freshness proof by embedding chaining relationship among records generated at different times. Detailed simulation studies using real datasets confirm the efficacy and efficiency of KV-Fresh.

Popularized by Bitcoin, proof-of-work (PoW) blockchain is one of the most widely deployed distributed consensus systems nowadays. Driven by incentives, PoW-based blockchain allows for democratized consensus making with correctness guarantee, as long as majority of the participants in the network are honest and rational. However, such elegant game theoretical security model falls apart when it is deployed on systems with potentially adversarial components and network conditions. For distributed consensus protocol used in blockchain, network connectivity plays a crucial role in the overall security of the system. A well-connected adversary with a communication advantage over honest nodes has a higher chance of winning blockchain forks and harvesting higher-than-usual mining revenue. In this paper we evaluate the impact of network connectivity on PoW blockchain consensus security via modeling analysis. Specifically, we perform the analysis on two adversarial scenarios: 1) honest-but-potentially-colluding, 2) selfish mining. For each scenario we evaluate communication capability of networked nodes from the heterogeneous network connectivity pattern and analyze its impact on consensus security of the underlying blockchain. Our analysis serves as a paradigm for future endeavors that seek to link blockchain security with network connectivity.

While both Internet Service Providers (ISPs) and third-party Security Service Providers (SSPs) offer Distributed Denial-of-Service (DDoS) mitigation services through cloud-based scrubbing centers, it is often beneficial for ISPs to outsource part of the traffic scrubbing to SSPs to achieve less economic cost and better network performance. To explore this potential, we design an online auction mechanism, featured by the challenge of the switching cost of using different winning bids over time. Formulating the social cost minimization as a nonconvex integer program, we firstly relax it and design an online algorithm that breaks it into a series of modified single-shot problems and solves each of them in polynomial time, without requiring knowledge of future inputs; then, we design a randomized rounding algorithm to convert the fractional decisions into integers without violating any constraints; and finally, we design the payment for each bid based on its winning probability. We rigorously prove that our mechanism achieves a parameterized constant competitive ratio for the long-term social cost, plus truthfulness and individual rationality in expectation. We also exhibit its superior practical performance via evaluations driven by real-world data traces.

Shared resources facilitate stealthy communication channels, including side channels and covert channels, which greatly endanger the information security, even in cloud environments. As a commonly shared resource, DRAM memory also serves as a source of stealthy channels. Existing solutions rely on two common features of DRAM-based channels, i.e., high cache miss and high bank locality, to detect the existence of such channels. However, such solutions could be defeated. In this paper, we point out the weakness of existing detection solutions by demonstrating a new advanced DRAM-based channel, which utilizes the hardware Intel SGX to conceal cache miss and bank locality. Further, we propose a novel neural network based solution DRAMD to detect such advanced stealthy channels. DRAMD uses hardware performance counters to track not only cache miss events that are used by existing solutions, but also counts of branches and instructions executed, as well as branch misses. Then DRAMD utilizes neural networks to model the access patterns of different applications and therefore detects potential stealthy communication channels. Our evaluation shows that DRAMD achieves up to 99% precision with 100% recall. Furthermore, DRAMD introduces less than 5% performance overheads and negligible impacts on legacy applications.

Mobile devices are promising to apply two-factor authentication in order to improve system security and enhance user privacy-preserving. Existing solutions usually have certain limits of requiring some form of user effort, which might seriously affect user experience and delay authentication time. In this paper, we propose PPGPass, a novel mobile two-factor authentication system, which leverages Photoplethysmography (PPG) sensors in wrist-worn wearables to extract individual characteristics of PPG signals. In order to realize both nonintrusive and secure, we design a two-stage algorithm to separate clean heartbeat signals from PPG signals contaminated by motion artifacts, which allows verifying users without intentionally staying still during the process of authentication. In addition, to deal with noncancelable issues when biometrics are compromised, we design a repeatable and non-invertible method to generate cancelable feature templates as alternative credentials, which enables to defense against man-in-the-middle attacks and replay attacks. To the best of our knowledge, PPGPass is the first nonintrusive and secure mobile two-factor authentication based on PPG sensors in wearables. We build a prototype of PPGPass and conduct the system with comprehensive experiments involving multiple participants. PPGPass can achieve an average F1 score of 95.3%, which confirms its high effectiveness, security, and usability.

Orthogonal blinding based schemes for wireless physical layer security aim to achieve secure communication by injecting noise into channels orthogonal to the main channel and corrupting the eavesdropper's signal reception. These methods, albeit practical, have been proven vulnerable against multi-antenna eavesdroppers who can filter the message from the noise. The venerability is rooted in the fact that the main channel state remains stasis in spite of the noise injection. Our proposed scheme leverages a reconfigurable antenna for Alice to rapidly change the channel state during transmission and a compressive sensing based algorithm for her to predict and cancel the changing effects for Bob. As a result, the communication between Alice and Bob remains clear, whereas randomized channel state prevent Eve from launching the known-plaintext attack. We formally analyze the security of the scheme against both single and multi-antenna eavesdroppers and identify its unique anti-eavesdropping properties due to the artificially created fast changing channel. We conduct extensive simulations and real-world experiments to evaluate its performance. Empirical results show that our scheme can suppress Eve's attack success rate to the level of random guessing, even if she knows all the symbols transmitted through other antenna modes.

The next frontier in communications is the transmission of touch over the Internet -- popularly termed as Tactile Internet (TI) - containing both tactile and kinesthetic feedback. While enormous efforts have been undertaken to design TI enablers, barely any emphasis is paid to contemplate and diagnose the (impaired) performance. Existing qualitative and quantitative performance metrics -- predominantly based on AV transmissions -- serve only as coarse-grained measures of the perceptual impairment, and hence are unsuitable for isolating performance bottlenecks. In this paper, we design quantitative metrics for measuring the quality of a TI session that is agnostic to haptic coders, any sophisticated algorithms and network parameters. As we need to compare transmitted and received haptic signals, we use Dynamic Time Warping from speech recognition literature and evolve two new quantitative metrics (a) Effective Time-Offset (ETO) and (b) Effective Value-Offset (EVO) that comprehensively characterize degradation in haptic signal profile on a finer scale. We clearly outline the mathematical foundation through rigorous TI experiments by incorporating network emulator and haptic devices. We demonstrate the effectiveness of our proposed metrics through practical measurements using a haptic device and we show 40-150x lesser delay adjustments for only 4%-17% increased RMSE compared to DTW.

Wireless networking opens up many opportunities to facilitate miniaturized robots in collaborative tasks, while the openness of wireless medium exposes robots to the threats of Sybil attackers, who can break the fundamental trust assumption in robotic collaboration by forging a large number of fictitious robots. Recent advances advocate the adoption of bulky multi-antenna systems to passively obtain fine-grained physical layer signatures, rendering them unaffordable to miniaturized robots. To overcome this conundrum, this paper presents ScatterID, a lightweight system that attaches featherlight and batteryless backscatter tags to single-antenna robots to defend against Sybil attacks. Instead of passively “observing” signatures, ScatterID actively “manipulates” multipath propagation by using backscatter tags to intentionally create rich multipath features obtainable to a single-antenna robot. These features are used to construct a distinct profile to detect the real signal source, even when the attacker is mobile and power-scaling. We implement ScatterID on the iRobot Create platform and evaluate it in typical indoor and outdoor environments. The experimental results show that our system achieves a high AUROC of 0.988 and an overall accuracy of 96.4% for identity verification.

Passive RFID technology is widely used in user authentication and access control. We propose RF-Rhythm, a secure and usable two-factor RFID authentication system with strong resilience to lost/stolen/cloned RFID cards. In RF-Rhythm, each legitimate user performs a sequence of taps on his/her RFID card according to a self-chosen secret melody. Such rhythmic taps can induce phase changes in the backscattered signals, which the RFID reader can detect to recover the user's tapping rhythm. In addition to verifying the RFID card's identification information as usual, the backend server compares the extracted tapping rhythm with what it acquires in the user enrollment phase. The user passes authentication checks if and only if both verifications succeed. We also propose a novel phase-hopping protocol in which the RFID reader emits Continuous Wave (CW) with random phases for extracting the user's secret tapping rhythm. Our protocol can prevent a capable adversary from extracting and then replaying a legitimate tapping rhythm from sniffed RFID signals. Comprehensive user experiments confirm the high security and usability of RF-Rhythm with false-positive and false-negative rates close to zero.

Voice interaction, as an emerging human-computer interaction method, has gained great popularity, especially on smart devices. However, due to the open nature of voice signals, voice interaction may cause privacy leakage. In this paper, we propose a novel scheme, called \emph{SeVI}, to protect voice interaction from being deliberately or unintentionally eavesdropped. SeVI actively generate jamming noise of superior characteristics, while a user is performing voice interaction with his/her device, so that attackers cannot obtain the voice contents of the user. Meanwhile, the device leverages the prior knowledge of the generated noise to adaptively cancel received noise, even when the device usage environment is changing due to movement, so that the user voice interactions are unaffected. SeVI relies on only normal microphone and speakers and can be implemented as light-weight software. We have implemented SeVI on a commercial off-the-shelf (COTS) smartphone and conducted extensive real-world experiments. The results demonstrate that SeVI can defend both online eavesdropping attacks and offline digital signal processing (DSP) analysis attacks.

Although existing surveillance cameras can identify people, their utility is limited by the unavailability of any direct camera-to-human communication. This paper proposes a real-time end-to-end system to solve the problem of digitally associating people in a camera view with their smartphones, without knowing the phones' IP/MAC addresses. The key idea is using a person's unique "context features", extracted from videos, as its sole address. The context address consists of: motion features, e.g. walking velocity; and ambiance features, e.g. magnetic trend and Wi-Fi signal strength. Once receiving a broadcast packet from the camera, a user's phone accepts it only if its context address matches the phone's sensor data. We highlight three novel components in our system: (1) definition of discriminative and noise-robust ambience features; (2) effortless ambient sensing map generation; (3) a context feature selection algorithm to dynamically choose lightweight yet effective features which are encoded into a fixed-length header. Real-world and simulated experiments are conducted for different applications. Our system achieves a sending ratio of 98.5%, an acceptance precision of 93.4%, and a recall of 98.3% with ten people. We believe this is a step towards direct camera-to-human communication and will become a generic underlay to various practical applications.

Large private and government networks are often subjected to attacks like data extrusion and service disruption. Existing anomaly detection systems use offline supervised learning and hence cannot detect anomalies in real-time. Even though unsupervised algorithms are increasingly used, they cannot readily adapt to newer threats. Moreover, such systems also suffer from high cost of storage and require extensive computational resources in addition to employing experts for labeling. In this paper, we propose ADA: Adaptive Deep Log Anomaly Detector, an unsupervised online deep neural network framework that leverages LSTM networks. We regularly adapt to new log patterns to ensure accurate anomaly detection. We also design an adaptive model selection strategy to choose parito-optimal configurations and thereby utilize resources efficiently. Further, we propose a dynamic threshold algorithm to dictate the optimal threshold based on recently detected events to improve the detection accuracy. We then use the predictions to guide storage of abnormal data and effectively reduce the overall storage cost. We compare ADA with the state-of-the-art using the Los Alamos National Laboratory cyber security dataset and show that ADA accurately detects anomalies with high F1-score ~95% and it is 97 times faster than existing approaches and incurs very low storage cost.

Website Fingerprinting (WF) attacks allow an adversary to recognize the visited websites by exploiting and analyzing network traffic patterns. The success rate of WF attacks is highly dependent on the set of network traffic features used to build the fingerprint. Such features can be used to launch a machine/deep learning-based WF attack which can break the existing state-of-the-art defense mechanisms. In this paper, we use an adversarial learning technique to present a novel defense mechanism, Deep Fingerprinting Defender (DFD), against deep learning-based WF attacks. The DFD aims to break the inherent pattern of the Tor users' online activity through the careful injection of dummy patterns in specific locations in a packet flow. We designed two configurations for dummy message injection, the one-way injection and two-way injection. We conducted extensive experiments to evaluate the performance of DFD over both closed-world and open-world settings. Our results demonstrate that these two configurations can successfully break the Tor network traffic pattern and achieve a high evasion rate of 86.02% over one-way client-side injection rate of 100%, a promising improvement in comparison with state-of-the-art adversarial trace's evasion rate of 60%. Moreover, DFD outperforms its state-of-the-art alternatives by requiring lower bandwidth overhead; 14.26% using client-side injection.

With the emergence of the information age, mobile data has become more random, heterogeneous and massive. Thanks to its many advantages, deep learning is increasingly applied in communication fields such as modulation recognition. However, recent studies show that the deep neural networks (DNN) is vulnerable to adversarial examples, where subtle perturbations deliberately designed by an attacker can fool a classifier model into making mistakes. From the perspective of an attacker, this study adds elaborate adversarial examples to the modulation signal, and explores the threats and impacts of adversarial attacks on the DNN-based modulation recognition in different environments. The results show that regardless of a white-box or a black-box model, the adversarial attack can reduce the accuracy of the target model. Among them, the performance of the iterative attack is superior to the one-step attack in most scenarios. In order to ensure the invisibility of the attack (the waveform being consistent before and after the perturbations), an appropriate perturbation level is found without losing the attack effect. Finally, it is attested that the signal confidence level is inversely proportional to the attack success rate, and several groups of signals with high robustness are obtained.

Zero-day Web attacks are arguably the most serious threats to Web security, but are very challenging to detect because they are not seen or known previously and thus cannot be detected by widely-deployed signature-based Web Application Firewalls (WAFs). This paper proposes ZeroWall, an unsupervised approach, which works with an existing WAF in pipeline, to effectively detecting zero-day Web attacks. Using historical web requests allowed by an existing signature-based WAF, a vast majority of which are assumed to be benign, ZeroWall trains a self-translation machine using an encoder-decoder recurrent neural network to capture the syntax and semantic patterns of benign requests. In real-time detection, a zero-day attack request (which the WAF fails to detect), not understood well by self-translation machine, cannot be translated back to its original request by the machine, thus is declared as an attack. In our evaluation using 8 real-world traces of 1.4 billion Web requests, ZeroWall successfully detects real zero-day attacks missed by existing WAFs and achieves high F1-scores over 0.98, which significantly outperforms all baseline approaches.