The deployment of monoculture software stacks can cause a devastating damage even by a single exploit against a single vulnerability. Inspired by the resilience benefit of biological diversity, the concept of software diversity has been proposed in the security domain. Although it is intuitive that software diversity may enhance security, its effectiveness has not been quantitatively investigated. Currently, no theoretical or empirical study has been explored to measure the security effectiveness of network diversity. In this paper, we take a first step towards ultimately tackling the problem. We propose a systematic framework that can model and quantify the security effectiveness of network diversity. We conduct simulations to demonstrate the usefulness of the framework. In contrast to the intuitive belief, we show that diversity does not necessarily improve security from a whole-network perspective. The root cause of this phenomenon is that the degree of vulnerability in diversified software implementations plays a critical role in determining the security effectiveness of software diversity.
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This work studies anomaly detection under differential privacy with Gaussian perturbation using both statistical and information-theoretic tools. In our setting, the adversary aims to modify the differentially private information of a statistical dataset by inserting additional data without being detected by using the differential privacy to her/his own benefit. To this end, firstly via hypothesis testing, we characterize a statistical threshold for the adversary, which balances the privacy budget and the induced bias (the impact of the attack) in order to remain undetected. In addition, we establish the privacy-distortion tradeoff in the sense of the well-known rate-distortion function for the Gaussian mechanism by using an information-theoretic approach and present an upper bound on the variance of the attacker's additional data as a function of the sensitivity and the original data's second-order statistics. Lastly, we introduce a new privacy metric based on Chernoff information for classifying adversaries under differential privacy as a stronger alternative for the Gaussian mechanism. Analytical results are supported by numerical evaluations.
The proliferation of wireless devices and their ever increasing influence on our day-to-day life is very evident and seems irreplaceable. This exponential growth in demand, both in terms of the number of devices and Quality of Service (QoS) had spawned the concept of cross-layer optimization several years ago. The primary goal of the cross-layer approach was to liberate the strict boundary between the layers of the traditional Open Systems Interconnection (OSI) protocol stack. The initial decade focused on establishing the theoretical feasibility of this revolutionary concept and gauging the effectiveness and limits of this idea. During the next phase, the advent of software defined radios (SDR) accelerated the growth of this domain due to its added flexibility. Yet, there has been a gaping abyss between solutions designed in theory and ones deployed in practice. To establish this, we first present an elaborate survey of the cross-layer protocol stack literature. Next, we briefly discuss how a commercial off-the-shelf (COTS), low SWaP (Size, Weight, and Power) embedded SDR (e-SDR) was transformed into a standalone, fieldable transceiver. Thereafter, we provide the software design ethos that focuses on efficiency and flexibility such that the optimization objectives and cross-layer interactions can be reconfigured rapidly. To demonstrate our claims, we provide results from extensive outdoor over-the-air experiments in various settings with up to 10-node network topologies. The results from the field trials demonstrate high reliability, throughput, and dynamic routing capability. To the best of our knowledge, this is the first time in literature, a COTS e-SDR has been leveraged to successfully design a cross-layer optimized transceiver that is capable of forming an ad hoc network that provides high throughput and high reliability in a ruggedized, weatherized, and fieldable form factor.
One of the most prominent and widely-used blockchain privacy solutions are zero-knowledge proof (ZKP) mixers operating on top of smart contract-enabled blockchains. ZKP mixers typically advertise their level of privacy through a so-called anonymity set size, similar to k-anonymity, where a user hides among a set of $k$ other users. In reality, however, these anonymity set claims are mostly inaccurate, as we find through empirical measurements of the currently most active ZKP mixers. We propose five heuristics that, in combination, can increase the probability that an adversary links a withdrawer to the correct depositor on average by 51.94% (108.63%) on the most popular Ethereum (ETH) and Binance Smart Chain (BSC) mixer, respectively. Our empirical evidence is hence also the first to suggest a differing privacy-predilection of users on ETH and BSC. We further identify 105 Decentralized Finance (DeFi) attackers leveraging ZKP mixers as the initial funds and to deposit attack revenue (e.g., from phishing scams, hacking centralized exchanges, and blockchain project attacks). State-of-the-art mixers are moreover tightly intertwined with the growing DeFi ecosystem by offering ``anonymity mining'' (AM) incentives, i.e., mixer users receive monetary rewards for mixing coins. However, contrary to the claims of related work, we find that AM does not always contribute to improving the quality of an anonymity set size of a mixer, because AM tends to attract privacy-ignorant users naively reusing addresses.
Since Bitcoin appeared in 2009, over 6,000 different cryptocurrency projects have followed. The cryptocurrency world may be the only technology where a massive number of competitors offer similar services yet claim unique benefits, including scalability, fast transactions, and security. But are these projects really offering unique features and significant enhancements over their competitors? To answer this question, we conducted a large-scale empirical analysis of code maintenance activities, originality and security across 592 crypto projects. We found that about half of these projects have not been updated for the last six months; over two years, about three-quarters of them disappeared, or were reported as scams or inactive. We also investigated whether 11 security vulnerabilities patched in Bitcoin were also patched in other projects. We found that about 80% of 510 C-language-based cryptocurrency projects have at least one unpatched vulnerability, and the mean time taken to fix the vulnerability is 237.8 days. Among those 510 altcoins, we found that at least 157 altcoins are likely to have been forked from Bitcoin, about a third of them containing only slight changes from the Bitcoin version from which they were forked. As case studies, we did a deep dive into 20 altcoins (e.g., Litecoin, FujiCoin, and Feathercoin) similar to the version of Bitcoin used for the fork. About half of them did not make any technically meaningful change - failing to comply with the promises (e.g., about using Proof of Stake) made in their whitepapers.
Deep learning has shown great promise in the domain of medical image analysis. Medical professionals and healthcare providers have been adopting the technology to speed up and enhance their work. These systems use deep neural networks (DNN) which are vulnerable to adversarial samples; images with imperceivable changes that can alter the model's prediction. Researchers have proposed defences which either make a DNN more robust or detect the adversarial samples before they do harm. However, none of these works consider an informed attacker which can adapt to the defence mechanism. We show that an informed attacker can evade five of the current state of the art defences while successfully fooling the victim's deep learning model, rendering these defences useless. We then suggest better alternatives for securing healthcare DNNs from such attacks: (1) harden the system's security and (2) use digital signatures.
Fast developing artificial intelligence (AI) technology has enabled various applied systems deployed in the real world, impacting people's everyday lives. However, many current AI systems were found vulnerable to imperceptible attacks, biased against underrepresented groups, lacking in user privacy protection, etc., which not only degrades user experience but erodes the society's trust in all AI systems. In this review, we strive to provide AI practitioners a comprehensive guide towards building trustworthy AI systems. We first introduce the theoretical framework of important aspects of AI trustworthiness, including robustness, generalization, explainability, transparency, reproducibility, fairness, privacy preservation, alignment with human values, and accountability. We then survey leading approaches in these aspects in the industry. To unify the current fragmented approaches towards trustworthy AI, we propose a systematic approach that considers the entire lifecycle of AI systems, ranging from data acquisition to model development, to development and deployment, finally to continuous monitoring and governance. In this framework, we offer concrete action items to practitioners and societal stakeholders (e.g., researchers and regulators) to improve AI trustworthiness. Finally, we identify key opportunities and challenges in the future development of trustworthy AI systems, where we identify the need for paradigm shift towards comprehensive trustworthy AI systems.
This paper focuses on the expected difference in borrower's repayment when there is a change in the lender's credit decisions. Classical estimators overlook the confounding effects and hence the estimation error can be magnificent. As such, we propose another approach to construct the estimators such that the error can be greatly reduced. The proposed estimators are shown to be unbiased, consistent, and robust through a combination of theoretical analysis and numerical testing. Moreover, we compare the power of estimating the causal quantities between the classical estimators and the proposed estimators. The comparison is tested across a wide range of models, including linear regression models, tree-based models, and neural network-based models, under different simulated datasets that exhibit different levels of causality, different degrees of nonlinearity, and different distributional properties. Most importantly, we apply our approaches to a large observational dataset provided by a global technology firm that operates in both the e-commerce and the lending business. We find that the relative reduction of estimation error is strikingly substantial if the causal effects are accounted for correctly.
Reinforcement learning (RL) has advanced greatly in the past few years with the employment of effective deep neural networks (DNNs) on the policy networks. With the great effectiveness came serious vulnerability issues with DNNs that small adversarial perturbations on the input can change the output of the network. Several works have pointed out that learned agents with a DNN policy network can be manipulated against achieving the original task through a sequence of small perturbations on the input states. In this paper, we demonstrate furthermore that it is also possible to impose an arbitrary adversarial reward on the victim policy network through a sequence of attacks. Our method involves the latest adversarial attack technique, Adversarial Transformer Network (ATN), that learns to generate the attack and is easy to integrate into the policy network. As a result of our attack, the victim agent is misguided to optimise for the adversarial reward over time. Our results expose serious security threats for RL applications in safety-critical systems including drones, medical analysis, and self-driving cars.
Recently, deep neural network models have achieved promising results in image captioning task. Yet, "vanilla" sentences, only describing shallow appearances (e.g., types, colors), generated by current works are not satisfied netizen style resulting in lacking engagements, contexts, and user intentions. To tackle this problem, we propose Netizen Style Commenting (NSC), to automatically generate characteristic comments to a user-contributed fashion photo. We are devoted to modulating the comments in a vivid "netizen" style which reflects the culture in a designated social community and hopes to facilitate more engagement with users. In this work, we design a novel framework that consists of three major components: (1) We construct a large-scale clothing dataset named NetiLook, which contains 300K posts (photos) with 5M comments to discover netizen-style comments. (2) We propose three unique measures to estimate the diversity of comments. (3) We bring diversity by marrying topic models with neural networks to make up the insufficiency of conventional image captioning works. Experimenting over Flickr30k and our NetiLook datasets, we demonstrate our proposed approaches benefit fashion photo commenting and improve image captioning tasks both in accuracy and diversity.
Modern communication networks have become very complicated and highly dynamic, which makes them hard to model, predict and control. In this paper, we develop a novel experience-driven approach that can learn to well control a communication network from its own experience rather than an accurate mathematical model, just as a human learns a new skill (such as driving, swimming, etc). Specifically, we, for the first time, propose to leverage emerging Deep Reinforcement Learning (DRL) for enabling model-free control in communication networks; and present a novel and highly effective DRL-based control framework, DRL-TE, for a fundamental networking problem: Traffic Engineering (TE). The proposed framework maximizes a widely-used utility function by jointly learning network environment and its dynamics, and making decisions under the guidance of powerful Deep Neural Networks (DNNs). We propose two new techniques, TE-aware exploration and actor-critic-based prioritized experience replay, to optimize the general DRL framework particularly for TE. To validate and evaluate the proposed framework, we implemented it in ns-3, and tested it comprehensively with both representative and randomly generated network topologies. Extensive packet-level simulation results show that 1) compared to several widely-used baseline methods, DRL-TE significantly reduces end-to-end delay and consistently improves the network utility, while offering better or comparable throughput; 2) DRL-TE is robust to network changes; and 3) DRL-TE consistently outperforms a state-ofthe-art DRL method (for continuous control), Deep Deterministic Policy Gradient (DDPG), which, however, does not offer satisfying performance.
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