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5G and beyond cellular systems embrace the disaggregation of Radio Access Network (RAN) components, exemplified by the evolution of the fronthual (FH) connection between cellular baseband and radio unit equipment. Crucially, synchronization over the FH is pivotal for reliable 5G services. In recent years, there has been a push to move these links to an Ethernet-based packet network topology, leveraging existing standards and ongoing research for Time-Sensitive Networking (TSN). However, TSN standards, such as Precision Time Protocol (PTP), focus on performance with little to no concern for security. This increases the exposure of the open FH to security risks. Attacks targeting synchronization mechanisms pose significant threats, potentially disrupting 5G networks and impairing connectivity. In this paper, we demonstrate the impact of successful spoofing and replay attacks against PTP synchronization. We show how a spoofing attack is able to cause a production-ready O-RAN and 5G-compliant private cellular base station to catastrophically fail within 2 seconds of the attack, necessitating manual intervention to restore full network operations. To counter this, we design a Machine Learning (ML)-based monitoring solution capable of detecting various malicious attacks with over 97.5% accuracy.

Zero Trust (ZT) is a security paradigm aiming to curtail an attacker's lateral movements within a network by implementing least-privilege and per-request access control policies. However, its widespread adoption is hindered by the difficulty of generating proper rules due to the lack of detailed knowledge of communication requirements and the characteristic behaviors of communicating entities under benign conditions. Consequently, manual rule generation becomes cumbersome and error-prone. To address these problems, we propose ZT-SDN, an automated framework for learning and enforcing network access control in Software-Defined Networks. ZT-SDN collects data from the underlying network and models the network "transactions" performed by communicating entities as graphs. The nodes represent entities, while the directed edges represent transactions identified by different protocol stacks observed. It uses novel unsupervised learning approaches to extract transaction patterns directly from the network data, such as the allowed protocol stacks and port numbers and data transmission behavior. Finally, ZT-SDN uses an innovative approach to generate correct access control rules and infer strong associations between them, allowing proactive rule deployment in forwarding devices. We show the framework's efficacy in detecting abnormal network accesses and abuses of permitted flows in changing network conditions with real network datasets. Additionally, we showcase ZT-SDN's scalability and the network's performance when applied in an SDN environment., Comment: 32 pages, 13 figures, 6 tables

Cutting state monitoring in the milling process is crucial for improving manufacturing efficiency and tool life. Cutting sound detection using machine learning (ML) models, inspired by experienced machinists, can be employed as a cost-effective and non-intrusive monitoring method in a complex manufacturing environment. However, labeling industry data for training is costly and time-consuming. Moreover, industry data is often scarce. In this study, we propose a novel adversarial domain adaptation (DA) approach to leverage abundant lab data to learn from scarce industry data, both labeled, for training a cutting-sound detection model. Rather than adapting the features from separate domains directly, we project them first into two separate latent spaces that jointly work as the feature space for learning domain-independent representations. We also analyze two different mechanisms for adversarial learning where the discriminator works as an adversary and a critic in separate settings, enabling our model to learn expressive domain-invariant and domain-ingrained features, respectively. We collected cutting sound data from multiple sensors in different locations, prepared datasets from lab and industry domain, and evaluated our learning models on them. Experiments showed that our models outperformed the multi-layer perceptron based vanilla domain adaptation models in labeling tasks on the curated datasets, achieving near 92%, 82% and 85% accuracy respectively for three different sensors installed in industry settings., Comment: 8 pages, 3 figures, 3 tables, First two named Authors have equal contribution (Co-first author)

Federated learning enables the collaborative learning of a global model on diverse data, preserving data locality and eliminating the need to transfer user data to a central server. However, data privacy remains vulnerable, as attacks can target user training data by exploiting the updates sent by users during each learning iteration. Secure aggregation protocols are designed to mask/encrypt user updates and enable a central server to aggregate the masked information. MicroSecAgg (PoPETS 2024) proposes a single server secure aggregation protocol that aims to mitigate the high communication complexity of the existing approaches by enabling a one-time setup of the secret to be re-used in multiple training iterations. In this paper, we identify a security flaw in the MicroSecAgg that undermines its privacy guarantees. We detail the security flaw and our attack, demonstrating how an adversary can exploit predictable masking values to compromise user privacy. Our findings highlight the critical need for enhanced security measures in secure aggregation protocols, particularly the implementation of dynamic and unpredictable masking strategies. We propose potential countermeasures to mitigate these vulnerabilities and ensure robust privacy protection in the secure aggregation frameworks.

In applying deep learning for malware classification, it is crucial to account for the prevalence of malware evolution, which can cause trained classifiers to fail on drifted malware. Existing solutions to address concept drift use active learning. They select new samples for analysts to label and then retrain the classifier with the new labels. Our key finding is that the current retraining techniques do not achieve optimal results. These techniques overlook that updating the model with scarce drifted samples requires learning features that remain consistent across pre-drift and post-drift data. The model should thus be able to disregard specific features that, while beneficial for the classification of pre-drift data, are absent in post-drift data, thereby preventing prediction degradation. In this paper, we propose a new technique for detecting and classifying drifted malware that learns drift-invariant features in malware control flow graphs by leveraging graph neural networks with adversarial domain adaptation. We compare it with existing model retraining methods in active learning-based malware detection systems and other domain adaptation techniques from the vision domain. Our approach significantly improves drifted malware detection on publicly available benchmarks and real-world malware databases reported daily by security companies in 2024. We also tested our approach in predicting multiple malware families drifted over time. A thorough evaluation shows that our approach outperforms the state-of-the-art approaches.

In recent years, there has been a growing focus on scrutinizing the security of cellular networks, often attributing security vulnerabilities to issues in the underlying protocol design descriptions. These protocol design specifications, typically extensive documents that are thousands of pages long, can harbor inaccuracies, underspecifications, implicit assumptions, and internal inconsistencies. In light of the evolving landscape, we introduce CellularLint--a semi-automatic framework for inconsistency detection within the standards of 4G and 5G, capitalizing on a suite of natural language processing techniques. Our proposed method uses a revamped few-shot learning mechanism on domain-adapted large language models. Pre-trained on a vast corpus of cellular network protocols, this method enables CellularLint to simultaneously detect inconsistencies at various levels of semantics and practical use cases. In doing so, CellularLint significantly advances the automated analysis of protocol specifications in a scalable fashion. In our investigation, we focused on the Non-Access Stratum (NAS) and the security specifications of 4G and 5G networks, ultimately uncovering 157 inconsistencies with 82.67% accuracy. After verification of these inconsistencies on open-source implementations and 17 commercial devices, we confirm that they indeed have a substantial impact on design decisions, potentially leading to concerns related to privacy, integrity, availability, and interoperability., Comment: Accepted at USENIX Security 24

The introduction of transformers has been an important breakthrough for AI research and application as transformers are the foundation behind Generative AI. A promising application domain for transformers is cybersecurity, in particular the malware domain analysis. The reason is the flexibility of the transformer models in handling long sequential features and understanding contextual relationships. However, as the use of transformers for malware analysis is still in the infancy stage, it is critical to evaluate, systematize, and contextualize existing literature to foster future research. This Systematization of Knowledge (SoK) paper aims to provide a comprehensive analysis of transformer-based approaches designed for malware analysis. Based on our systematic analysis of existing knowledge, we structure and propose taxonomies based on: (a) how different transformers are adapted, organized, and modified across various use cases; and (b) how diverse feature types and their representation capabilities are reflected. We also provide an inventory of datasets used to explore multiple research avenues in the use of transformers for malware analysis and discuss open challenges with future research directions. We believe that this SoK paper will assist the research community in gaining detailed insights from existing work and will serve as a foundational resource for implementing novel research using transformers for malware analysis.

Many machine learning and data mining algorithms rely on the assumption that the training and testing data share the same feature space and distribution. However, this assumption may not always hold. For instance, there are situations where we need to classify data in one domain, but we only have sufficient training data available from a different domain. The latter data may follow a distinct distribution. In such cases, successfully transferring knowledge across domains can significantly improve learning performance and reduce the need for extensive data labeling efforts. Transfer learning (TL) has thus emerged as a promising framework to tackle this challenge, particularly in security-related tasks. This paper aims to review the current advancements in utilizing TL techniques for security. The paper includes a discussion of the existing research gaps in applying TL in the security domain, as well as exploring potential future research directions and issues that arise in the context of TL-assisted security solutions.

Fake base stations (FBSes) pose a significant security threat by impersonating legitimate base stations. Though efforts have been made to defeat this threat, up to this day, the presence of FBSes and the multi-step attacks (MSAs) stemming from them can lead to unauthorized surveillance, interception of sensitive information, and disruption of network services for legitimate users. Therefore, detecting these malicious entities is crucial to ensure the security and reliability of cellular networks. Traditional detection methods often rely on additional hardware, predefined rules, signal scanning, changing protocol specifications, or cryptographic mechanisms that have limitations and incur huge infrastructure costs in accurately identifying FBSes. In this paper, we develop FBSDetector-an effective and efficient detection solution that can reliably detect FBSes and MSAs from layer-3 network traces using machine learning (ML) at the user equipment (UE) side. To develop FBSDetector, we created FBSAD and MSAD, the first-ever high-quality and large-scale datasets for training machine learning models capable of detecting FBSes and MSAs. These datasets capture the network traces in different real-world cellular network scenarios (including mobility and different attacker capabilities) incorporating legitimate base stations and FBSes. The combined network trace has a volume of 6.6 GB containing 751963 packets. Our novel ML models, specially designed to detect FBSes and MSAs, can effectively detect FBSes with an accuracy of 92% and a false positive rate of 5.96% and recognize MSAs with an accuracy of 86% and a false positive rate of 7.82%. We deploy FBSDetector as a real-world solution to protect end-users through an Android app and validate in a controlled lab environment. Compared to the existing solutions that fail to detect FBSes, FBSDetector can detect FBSes in the wild in real time.

The rampant occurrence of cybersecurity breaches imposes substantial limitations on the progress of network infrastructures, leading to compromised data, financial losses, potential harm to individuals, and disruptions in essential services. The current security landscape demands the urgent development of a holistic security assessment solution that encompasses vulnerability analysis and investigates the potential exploitation of these vulnerabilities as attack paths. In this paper, we propose Graphene, an advanced system designed to provide a detailed analysis of the security posture of computing infrastructures. Using user-provided information, such as device details and software versions, Graphene performs a comprehensive security assessment. This assessment includes identifying associated vulnerabilities and constructing potential attack graphs that adversaries can exploit. Furthermore, Graphene evaluates the exploitability of these attack paths and quantifies the overall security posture through a scoring mechanism. The system takes a holistic approach by analyzing security layers encompassing hardware, system, network, and cryptography. Furthermore, Graphene delves into the interconnections between these layers, exploring how vulnerabilities in one layer can be leveraged to exploit vulnerabilities in others. In this paper, we present the end-to-end pipeline implemented in Graphene, showcasing the systematic approach adopted for conducting this thorough security analysis.

Speech recognition systems driven by DNNs have revolutionized human-computer interaction through voice interfaces, which significantly facilitate our daily lives. However, the growing popularity of these systems also raises special concerns on their security, particularly regarding backdoor attacks. A backdoor attack inserts one or more hidden backdoors into a DNN model during its training process, such that it does not affect the model's performance on benign inputs, but forces the model to produce an adversary-desired output if a specific trigger is present in the model input. Despite the initial success of current audio backdoor attacks, they suffer from the following limitations: (i) Most of them require sufficient knowledge, which limits their widespread adoption. (ii) They are not stealthy enough, thus easy to be detected by humans. (iii) Most of them cannot attack live speech, reducing their practicality. To address these problems, in this paper, we propose FlowMur, a stealthy and practical audio backdoor attack that can be launched with limited knowledge. FlowMur constructs an auxiliary dataset and a surrogate model to augment adversary knowledge. To achieve dynamicity, it formulates trigger generation as an optimization problem and optimizes the trigger over different attachment positions. To enhance stealthiness, we propose an adaptive data poisoning method according to Signal-to-Noise Ratio (SNR). Furthermore, ambient noise is incorporated into the process of trigger generation and data poisoning to make FlowMur robust to ambient noise and improve its practicality. Extensive experiments conducted on two datasets demonstrate that FlowMur achieves high attack performance in both digital and physical settings while remaining resilient to state-of-the-art defenses. In particular, a human study confirms that triggers generated by FlowMur are not easily detected by participants., Comment: To appear at lEEE Symposium on Security & Privacy (Oakland) 2024

The IEEE 802.11 family of standards, better known as WiFi, is a widely used protocol utilized by billions of users. Previous works on WiFi formal verification have mostly focused on the four-way handshake and other security aspects. However, recent works have uncovered severe vulnerabilities in functional aspects of WiFi, which can cause information leakage for billions of devices. No formal analysis method exists able to reason on the functional aspects of the WiFi protocol. In this paper, we take the first steps in addressing this gap and present an extensive formal analysis of the functional aspects of the WiFi protocol, more specifically, the fragmentation and the power-save-mode process. To achieve this, we design a novel segment-based formal verification process and introduce a practical threat model (i.e. MAC spoofing) in Tamarin to reason about the various capabilities of the attacker. To this end, we verify 68 properties extracted from WiFi protocol specification, find 3 vulnerabilities from the verification, verify 3 known attacks, and discover 2 new issues. These vulnerabilities and issues affect 14 commercial devices out of 17 tested cases, showing the prevalence and impact of the issues. Apart from this, we show that the proposed countermeasures indeed are sufficient to address the issues. We hope our results and analysis will help vendors adopt the countermeasures and motivate further research into the verification of the functional aspects of the WiFi protocol., Comment: Accepted to AsiaCCS 2024

The present study explored managerial and instructor perceptions of their freshly employed cybersecurity workers' or students' preparedness to work effectively in a changing cybersecurity environment that includes AI tools. Specifically, we related perceptions of technical preparedness to ethical, systems thinking, and communication skills. We found that managers and professors perceive preparedness to use AI tools in cybersecurity to be significantly associated with all three non-technical skill sets. Most important, ethics is a clear leader in the network of relationships. Contrary to expectations that ethical concerns are left behind in the rush to adopt the most advanced AI tools in security, both higher education instructors and managers appreciate their role and see them closely associated with technical prowess. Another significant finding is that professors over-estimate students' preparedness for ethical, system thinking, and communication abilities compared to IT managers' perceptions of their newly employed IT workers.

The emergence of AI tools in cybersecurity creates many opportunities and uncertainties. A focus group with advanced graduate students in cybersecurity revealed the potential depth and breadth of the challenges and opportunities. The salient issues are access to open source or free tools, documentation, curricular diversity, and clear articulation of ethical principles for AI cybersecurity education. Confronting the "black box" mentality in AI cybersecurity work is also of the greatest importance, doubled by deeper and prior education in foundational AI work. Systems thinking and effective communication were considered relevant areas of educational improvement. Future AI educators and practitioners need to address these issues by implementing rigorous technical training curricula, clear documentation, and frameworks for ethically monitoring AI combined with critical and system's thinking and communication skills.

Trust evaluation assesses trust relationships between entities and facilitates decision-making. Machine Learning (ML) shows great potential for trust evaluation owing to its learning capabilities. In recent years, Graph Neural Networks (GNNs), as a new ML paradigm, have demonstrated superiority in dealing with graph data. This has motivated researchers to explore their use in trust evaluation, as trust relationships among entities can be modeled as a graph. However, current trust evaluation methods that employ GNNs fail to fully satisfy the dynamic nature of trust, overlook the adverse effects of trust-related attacks, and cannot provide convincing explanations on evaluation results. To address these problems, we propose TrustGuard, a GNN-based accurate trust evaluation model that supports trust dynamicity, is robust against typical attacks, and provides explanations through visualization. Specifically, TrustGuard is designed with a layered architecture that contains a snapshot input layer, a spatial aggregation layer, a temporal aggregation layer, and a prediction layer. Among them, the spatial aggregation layer adopts a defense mechanism to robustly aggregate local trust, and the temporal aggregation layer applies an attention mechanism for effective learning of temporal patterns. Extensive experiments on two real-world datasets show that TrustGuard outperforms state-of-the-art GNN-based trust evaluation models with respect to trust prediction across single-timeslot and multi-timeslot, even in the presence of attacks. In addition, TrustGuard can explain its evaluation results by visualizing both spatial and temporal views., Comment: Accepted by IEEE TDSC. Code: https://github.com/Jieerbobo/TrustGuard

The most effective domain adaptation (DA) involves the decomposition of data representation into a domain independent representation (DIRep), and a domain dependent representation (DDRep). A classifier is trained by using the DIRep of the labeled source images. Since the DIRep is domain invariant, the classifier can be "transferred" to make predictions for the target domain with no (or few) labels. However, information useful for classification in the target domain can "hide" in the DDRep in current DA algorithms such as Domain-Separation-Networks (DSN). DSN's weak constraint to enforce orthogonality of DIRep and DDRep, allows this hiding and can result in poor performance. To address this shortcoming, we developed a new algorithm wherein a stronger constraint is imposed to minimize the DDRep by using a KL divergent loss for the DDRep in order to create the maximal DIRep that enhances transfer learning performance. By using synthetic data sets, we show explicitly that depending on initialization DSN with its weaker constraint can lead to sub-optimal solutions with poorer DA performance whereas our algorithm with maximal DIRep is robust against such perturbations. We demonstrate the equal-or-better performance of our approach against state-of-the-art algorithms by using several standard benchmark image datasets including Office. We further highlight the compatibility of our algorithm with pretrained models, extending its applicability and versatility in real-world scenarios.

Deep learning (DL) techniques are highly effective for defect detection from images. Training DL classification models, however, requires vast amounts of labeled data which is often expensive to collect. In many cases, not only the available training data is limited but may also imbalanced. In this paper, we propose a novel domain adaptation (DA) approach to address the problem of labeled training data scarcity for a target learning task by transferring knowledge gained from an existing source dataset used for a similar learning task. Our approach works for scenarios where the source dataset and the dataset available for the target learning task have same or different feature spaces. We combine our DA approach with an autoencoder-based data augmentation approach to address the problem of imbalanced target datasets. We evaluate our combined approach using image data for wafer defect prediction. The experiments show its superior performance against other algorithms when the number of labeled samples in the target dataset is significantly small and the target dataset is imbalanced.

5G is the 5th generation cellular network protocol. It is the state-of-the-art global wireless standard that enables an advanced kind of network designed to connect virtually everyone and everything with increased speed and reduced latency. Therefore, its development, analysis, and security are critical. However, all approaches to the 5G protocol development and security analysis, e.g., property extraction, protocol summarization, and semantic analysis of the protocol specifications and implementations are completely manual. To reduce such manual effort, in this paper, we curate SPEC5G the first-ever public 5G dataset for NLP research. The dataset contains 3,547,586 sentences with 134M words, from 13094 cellular network specifications and 13 online websites. By leveraging large-scale pre-trained language models that have achieved state-of-the-art results on NLP tasks, we use this dataset for security-related text classification and summarization. Security-related text classification can be used to extract relevant security-related properties for protocol testing. On the other hand, summarization can help developers and practitioners understand the high level of the protocol, which is itself a daunting task. Our results show the value of our 5G-centric dataset in 5G protocol analysis automation. We believe that SPEC5G will enable a new research direction into automatic analyses for the 5G cellular network protocol and numerous related downstream tasks. Our data and code are publicly available.

Modern smart TVs often communicate with their remote controls (including those smart phone simulated ones) using multiple wireless channels (e.g., Infrared, Bluetooth, and Wi-Fi). However, this multi-channel remote control communication introduces a new attack surface. An inherent security flaw is that remote controls of most smart TVs are designed to work in a benign environment rather than an adversarial one, and thus wireless communications between a smart TV and its remote controls are not strongly protected. Attackers could leverage such flaw to abuse the remote control communication and compromise smart TV systems. In this paper, we propose EvilScreen, a novel attack that exploits ill-protected remote control communications to access protected resources of a smart TV or even control the screen. EvilScreen exploits a multi-channel remote control mimicry vulnerability present in today smart TVs. Unlike other attacks, which compromise the TV system by exploiting code vulnerabilities or malicious third-party apps, EvilScreen directly reuses commands of different remote controls, combines them together to circumvent deployed authentication and isolation policies, and finally accesses or controls TV resources remotely. We evaluated eight mainstream smart TVs and found that they are all vulnerable to EvilScreen attacks, including a Samsung product adopting the ISO/IEC security specification.

Increasing use of machine learning (ML) technologies in privacy-sensitive domains such as medical diagnoses, lifestyle predictions, and business decisions highlights the need to better understand if these ML technologies are introducing leakage of sensitive and proprietary training data. In this paper, we focus on model inversion attacks where the adversary knows non-sensitive attributes about records in the training data and aims to infer the value of a sensitive attribute unknown to the adversary, using only black-box access to the target classification model. We first devise a novel confidence score-based model inversion attribute inference attack that significantly outperforms the state-of-the-art. We then introduce a label-only model inversion attack that relies only on the model's predicted labels but still matches our confidence score-based attack in terms of attack effectiveness. We also extend our attacks to the scenario where some of the other (non-sensitive) attributes of a target record are unknown to the adversary. We evaluate our attacks on two types of machine learning models, decision tree and deep neural network, trained on three real datasets. Moreover, we empirically demonstrate the disparate vulnerability of model inversion attacks, i.e., specific groups in the training dataset (grouped by gender, race, etc.) could be more vulnerable to model inversion attacks., Comment: Conditionally accepted to USENIX Security 2022. This is not the camera-ready version. arXiv admin note: substantial text overlap with arXiv:2012.03404

In order to support a variety of missions and deal with different flight environments, drone control programs typically provide configurable control parameters. However, such a flexibility introduces vulnerabilities. One such vulnerability, referred to as range specification bugs, has been recently identified. The vulnerability originates from the fact that even though each individual parameter receives a value in the recommended value range, certain combinations of parameter values may affect the drone physical stability. In this paper we develop a novel learning-guided search system to find such combinations, that we refer to as incorrect configurations. Our system applies metaheuristic search algorithms mutating configurations to detect the configuration parameters that have values driving the drone to unstable physical states. To guide the mutations, our system leverages a machine learning predictor as the fitness evaluator. Finally, by utilizing multi-objective optimization, our system returns the feasible ranges based on the mutation search results. Because in our system the mutations are guided by a predictor, evaluating the parameter configurations does not require realistic/simulation executions. Therefore, our system supports a comprehensive and yet efficient detection of incorrect configurations. We have carried out an experimental evaluation of our system. The evaluation results show that the system successfully reports potentially incorrect configurations, of which over 85% lead to actual unstable physical states., Comment: Accepted to ICSE2022 Technical Track

Federated learning enables a global machine learning model to be trained collaboratively by distributed, mutually non-trusting learning agents who desire to maintain the privacy of their training data and their hardware. A global model is distributed to clients, who perform training, and submit their newly-trained model to be aggregated into a superior model. However, federated learning systems are vulnerable to interference from malicious learning agents who may desire to prevent training or induce targeted misclassification in the resulting global model. A class of Byzantine-tolerant aggregation algorithms has emerged, offering varying degrees of robustness against these attacks, often with the caveat that the number of attackers is bounded by some quantity known prior to training. This paper presents Simeon: a novel approach to aggregation that applies a reputation-based iterative filtering technique to achieve robustness even in the presence of attackers who can exhibit arbitrary behaviour. We compare Simeon to state-of-the-art aggregation techniques and find that Simeon achieves comparable or superior robustness to a variety of attacks. Notably, we show that Simeon is tolerant to sybil attacks, where other algorithms are not, presenting a key advantage of our approach.

A fundamental premise of SMS One-Time Password (OTP) is that the used pseudo-random numbers (PRNs) are uniquely unpredictable for each login session. Hence, the process of generating PRNs is the most critical step in the OTP authentication. An improper implementation of the pseudo-random number generator (PRNG) will result in predictable or even static OTP values, making them vulnerable to potential attacks. In this paper, we present a vulnerability study against PRNGs implemented for Android apps. A key challenge is that PRNGs are typically implemented on the server-side, and thus the source code is not accessible. To resolve this issue, we build an analysis tool, \sysname, to assess implementations of the PRNGs in an automated manner without the source code requirement. Through reverse engineering, \sysname identifies the apps using SMS OTP and triggers each app's login functionality to retrieve OTP values. It further assesses the randomness of the OTP values to identify vulnerable PRNGs. By analyzing 6,431 commercially used Android apps downloaded from \tool{Google Play} and \tool{Tencent Myapp}, \sysname identified 399 vulnerable apps that generate predictable OTP values. Even worse, 194 vulnerable apps use the OTP authentication alone without any additional security mechanisms, leading to insecure authentication against guessing attacks and replay attacks.

By all measures, wireless networking has seen explosive growth over the past decade. Fourth Generation Long Term Evolution (4G LTE) cellular technology has increased the bandwidth available for smartphones, in essence, delivering broadband speeds to mobile devices. The most recent 5G technology is further enhancing the transmission speeds and cell capacity, as well as, reducing latency through the use of different radio technologies and is expected to provide Internet connections that are an order of magnitude faster than 4G LTE. Technology continues to advance rapidly, however, and the next generation, 6G, is already being envisioned. 6G will make possible a wide range of powerful, new applications including holographic telepresence, telehealth, remote education, ubiquitous robotics and autonomous vehicles, smart cities and communities (IoT), and advanced manufacturing (Industry 4.0, sometimes referred to as the Fourth Industrial Revolution), to name but a few. The advances we will see begin at the hardware level and extend all the way to the top of the software "stack." Artificial Intelligence (AI) will also start playing a greater role in the development and management of wireless networking infrastructure by becoming embedded in applications throughout all levels of the network. The resulting benefits to society will be enormous. At the same time these exciting new wireless capabilities are appearing rapidly on the horizon, a broad range of research challenges loom ahead. These stem from the ever-increasing complexity of the hardware and software systems, along with the need to provide infrastructure that is robust and secure while simultaneously protecting the privacy of users. Here we outline some of those challenges and provide recommendations for the research that needs to be done to address them., Comment: A Computing Community Consortium (CCC) white paper, 5 pages

Many proximity-based tracing (PCT) protocols have been proposed and deployed to combat the spreading of COVID-19. In this paper, we take a systematic approach to analyze PCT protocols. We identify a list of desired properties of a contact tracing design from the four aspects of Privacy, Utility, Resiliency, and Efficiency (PURE). We also identify two main design choices for PCT protocols: what information patients report to the server, and which party performs the matching. These two choices determine most of the PURE properties and enable us to conduct a comprehensive analysis and comparison of the existing protocols., Comment: 35 pages, 10 figures, 7 tables

The rise of Artificial Intelligence (AI) will bring with it an ever-increasing willingness to cede decision-making to machines. But rather than just giving machines the power to make decisions that affect us, we need ways to work cooperatively with AI systems. There is a vital need for research in "AI and Cooperation" that seeks to understand the ways in which systems of AIs and systems of AIs with people can engender cooperative behavior. Trust in AI is also key: trust that is intrinsic and trust that can only be earned over time. Here we use the term "AI" in its broadest sense, as employed by the recent 20-Year Community Roadmap for AI Research (Gil and Selman, 2019), including but certainly not limited to, recent advances in deep learning. With success, cooperation between humans and AIs can build society just as human-human cooperation has. Whether coming from an intrinsic willingness to be helpful, or driven through self-interest, human societies have grown strong and the human species has found success through cooperation. We cooperate "in the small" -- as family units, with neighbors, with co-workers, with strangers -- and "in the large" as a global community that seeks cooperative outcomes around questions of commerce, climate change, and disarmament. Cooperation has evolved in nature also, in cells and among animals. While many cases involving cooperation between humans and AIs will be asymmetric, with the human ultimately in control, AI systems are growing so complex that, even today, it is impossible for the human to fully comprehend their reasoning, recommendations, and actions when functioning simply as passive observers., Comment: A Computing Community Consortium (CCC) white paper, 4 pages

The Internet of Things (IoT) and edge computing applications aim to support a variety of societal needs, including the global pandemic situation that the entire world is currently experiencing and responses to natural disasters. The need for real-time interactive applications such as immersive video conferencing, augmented/virtual reality, and autonomous vehicles, in education, healthcare, disaster recovery and other domains, has never been higher. At the same time, there have been recent technological breakthroughs in highly relevant fields such as artificial intelligence (AI)/machine learning (ML), advanced communication systems (5G and beyond), privacy-preserving computations, and hardware accelerators. 5G mobile communication networks increase communication capacity, reduce transmission latency and error, and save energy -- capabilities that are essential for new applications. The envisioned future 6G technology will integrate many more technologies, including for example visible light communication, to support groundbreaking applications, such as holographic communications and high precision manufacturing. Many of these applications require computations and analytics close to application end-points: that is, at the edge of the network, rather than in a centralized cloud. AI techniques applied at the edge have tremendous potential both to power new applications and to need more efficient operation of edge infrastructure. However, it is critical to understand where to deploy AI systems within complex ecosystems consisting of advanced applications and the specific real-time requirements towards AI systems., Comment: A Computing Community Consortium (CCC) white paper, 4 pages

Increasing use of ML technologies in privacy-sensitive domains such as medical diagnoses, lifestyle predictions, and business decisions highlights the need to better understand if these ML technologies are introducing leakages of sensitive and proprietary training data. In this paper, we focus on one kind of model inversion attacks, where the adversary knows non-sensitive attributes about instances in the training data and aims to infer the value of a sensitive attribute unknown to the adversary, using oracle access to the target classification model. We devise two novel model inversion attribute inference attacks -- confidence modeling-based attack and confidence score-based attack, and also extend our attack to the case where some of the other (non-sensitive) attributes are unknown to the adversary. Furthermore, while previous work uses accuracy as the metric to evaluate the effectiveness of attribute inference attacks, we find that accuracy is not informative when the sensitive attribute distribution is unbalanced. We identify two metrics that are better for evaluating attribute inference attacks, namely G-mean and Matthews correlation coefficient (MCC). We evaluate our attacks on two types of machine learning models, decision tree and deep neural network, trained with two real datasets. Experimental results show that our newly proposed attacks significantly outperform the state-of-the-art attacks. Moreover, we empirically show that specific groups in the training dataset (grouped by attributes, e.g., gender, race) could be more vulnerable to model inversion attacks. We also demonstrate that our attacks' performances are not impacted significantly when some of the other (non-sensitive) attributes are also unknown to the adversary.

Technology advances in areas such as sensors, IoT, and robotics, enable new collaborative applications (e.g., autonomous devices). A primary requirement for such collaborations is to have a secure system which enables information sharing and information flow protection. Policy-based management system is a key mechanism for secure selective sharing of protected resources. However, policies in each party of such a collaborative environment cannot be static as they have to adapt to different contexts and situations. One advantage of collaborative applications is that each party in the collaboration can take advantage of knowledge of the other parties for learning or enhancing its own policies. We refer to this learning mechanism as policy transfer. The design of a policy transfer framework has challenges, including policy conflicts and privacy issues. Policy conflicts typically arise because of differences in the obligations of the parties, whereas privacy issues result because of data sharing constraints for sensitive data. Hence, the policy transfer framework should be able to tackle such challenges by considering minimal sharing of data and support policy adaptation to address conflict. In the paper we propose a framework that aims at addressing such challenges. We introduce a formal definition of the policy transfer problem for attribute-based policies. We then introduce the transfer methodology that consists of three sequential steps. Finally we report experimental results., Comment: Presented at AAAI FSS-20: Artificial Intelligence in Government and Public Sector, Washington, DC, USA

Cellular networks represent a critical infrastructure and their security is thus crucial. 5G - the latest generation of cellular networks - combines different technologies to increase capacity, reduce latency, and save energy. Due to its complexity and scale, however, ensuring its security is extremely challenging. In this white paper, we outline recent approaches supporting systematic analyses of 4G LTE and 5G protocols and their related defenses and introduce an initial security and privacy roadmap, covering different research challenges, including formal and comprehensive analyses of cellular protocols as defined by the standardization groups, verification of the software implementing the protocols, the design of robust defenses, and application and device security., Comment: A Computing Community Consortium (CCC) white paper, 8 pages

`Anytime, Anywhere' data access model has become a widespread IT policy in organizations making insider attacks even more complicated to model, predict and deter. Here, we propose Gargoyle, a network-based insider attack resilient framework against the most complex insider threats within a pervasive computing context. Compared to existing solutions, Gargoyle evaluates the trustworthiness of an access request context through a new set of contextual attributes called Network Context Attribute (NCA). NCAs are extracted from the network traffic and include information such as the user's device capabilities, security-level, current and prior interactions with other devices, network connection status, and suspicious online activities. Retrieving such information from the user's device and its integrated sensors are challenging in terms of device performance overheads, sensor costs, availability, reliability and trustworthiness. To address these issues, Gargoyle leverages the capabilities of Software-Defined Network (SDN) for both policy enforcement and implementation. In fact, Gargoyle's SDN App can interact with the network controller to create a `defence-in-depth' protection system. For instance, Gargoyle can automatically quarantine a suspicious data requestor in the enterprise network for further investigation or filter out an access request before engaging a data provider. Finally, instead of employing simplistic binary rules in access authorizations, Gargoyle incorporates Function-based Access Control (FBAC) and supports the customization of access policies into a set of functions (e.g., disabling copy, allowing print) depending on the perceived trustworthiness of the context., Comment: Accepted to IEEE LCN 2018 as full paper, Pre-final version - slightly different than the final version published by the conference