refactor: replace : with _ in citation references

literature.bib

@@ -148,7 +148,7 @@
 	year = {2022},
 	bdsk-url-1 = {https://deepgram.com/learn/benchmarking-top-open-source-speech-models}}
 
-@article{Radford:2022aa,
+@article{Radford_2022aa,
 	abstract = {We study the capabilities of speech processing systems trained simply to predict large amounts of transcripts of audio on the internet. When scaled to 680,000 hours of multilingual and multitask supervision, the resulting models generalize well to standard benchmarks and are often competitive with prior fully supervised results but in a zero-shot transfer setting without the need for any fine-tuning. When compared to humans, the models approach their accuracy and robustness. We are releasing models and inference code to serve as a foundation for further work on robust speech processing.},
 	author = {Alec Radford and Jong Wook Kim and Tao Xu and Greg Brockman and Christine McLeavey and Ilya Sutskever},
 	date-added = {2024-02-24 12:22:44 +0100},
@@ -204,7 +204,7 @@
 	bdsk-file-1 = {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},
 	bdsk-url-1 = {http://dx.doi.org/10.1145/3491102.3517684}}
 
-@article{Mazhar:2020aa,
+@article{Mazhar_2020aa,
 	abstract = {As the smart home IoT ecosystem flourishes, it is imperative to gain a better understanding of the unique challenges it poses in terms of management, security, and privacy. Prior studies are limited because they examine smart home IoT devices in testbed environments or at a small scale. To address this gap, we present a measurement study of smart home IoT devices in the wild by instrumenting home gateways and passively collecting real-world network traffic logs from more than 200 homes across a large metropolitan area in the United States. We characterize smart home IoT traffic in terms of its volume, temporal patterns, and external endpoints along with focusing on certain security and privacy concerns. We first show that traffic characteristics reflect the functionality of smart home IoT devices such as smart TVs generating high volume traffic to content streaming services following diurnal patterns associated with human activity. While the smart home IoT ecosystem seems fragmented, our analysis reveals that it is mostly centralized due to its reliance on a few popular cloud and DNS services. Our findings also highlight several interesting security and privacy concerns in smart home IoT ecosystem such as the need to improve policy-based access control for IoT traffic, lack of use of application layer encryption, and prevalence of third-party advertising and tracking services. Our findings have important implications for future research on improving management, security, and privacy of the smart home IoT ecosystem.},
 	author = {M. Hammad Mazhar and Zubair Shafiq},
 	date-added = {2024-02-21 17:51:54 +0100},
@@ -219,7 +219,7 @@
 	bdsk-url-1 = {https://arxiv.org/abs/2001.08288},
 	bdsk-url-2 = {https://arxiv.org/pdf/2001.08288.pdf}}
 
-@article{Trimananda:2019aa,
+@article{Trimananda_2019aa,
 	abstract = {Smart home devices are vulnerable to passive inference attacks based on network traffic, even in the presence of encryption. In this paper, we present PINGPONG, a tool that can automatically extract packet-level signatures for device events (e.g., light bulb turning ON/OFF) from network traffic. We evaluated PINGPONG on popular smart home devices ranging from smart plugs and thermostats to cameras, voice-activated devices, and smart TVs. We were able to: (1) automatically extract previously unknown signatures that consist of simple sequences of packet lengths and directions; (2) use those signatures to detect the devices or specific events with an average recall of more than 97%; (3) show that the signatures are unique among hundreds of millions of packets of real world network traffic; (4) show that our methodology is also applicable to publicly available datasets; and (5) demonstrate its robustness in different settings: events triggered by local and remote smartphones, as well as by homeautomation systems.},
 	author = {Rahmadi Trimananda and Janus Varmarken and Athina Markopoulou and Brian Demsky},
 	date-added = {2024-02-21 17:28:09 +0100},
@@ -441,7 +441,7 @@
 	bdsk-file-1 = {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},
 	bdsk-url-1 = {https://doi.org/10.1109/CNS.2019.8802686}}
 
-@article{Wang:2020aa,
+@article{Wang_2020aa,
 	abstract = {This paper investigates the privacy leakage of smart speakers under an encrypted traffic analysis attack, referred to as voice command fingerprinting. In this attack, an adversary can eavesdrop both outgoing and incoming encrypted voice traffic of a smart speaker, and infers which voice command a user says over encrypted traffic. We first built an automatic voice traffic collection tool and collected two large-scale datasets on two smart speakers, Amazon Echo and Google Home. Then, we implemented proof-of-concept attacks by leveraging deep learning. Our experimental results over the two datasets indicate disturbing privacy concerns. Specifically, compared to 1% accuracy with random guess, our attacks can correctly infer voice commands over encrypted traffic with 92.89\% accuracy on Amazon Echo. Despite variances that human voices may cause on outgoing traffic, our proof-of-concept attacks remain effective even only leveraging incoming traffic (i.e., the traffic from the server). This is because the AI-based voice services running on the server side response commands in the same voice and with a deterministic or predictable manner in text, which leaves distinguishable pattern over encrypted traffic. We also built a proof-of-concept defense to obfuscate encrypted traffic. Our results show that the defense can effectively mitigate attack accuracy on Amazon Echo to 32.18%.},
 	author = {Chenggang Wang and Sean Kennedy and Haipeng Li and King Hudson and Gowtham Atluri and Xuetao Wei and Wenhai Sun and Boyang Wang},
 	date-added = {2023-03-01 21:16:38 +0100},