Visible to the public Biblio

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2021-05-13
Lit, Yanyan, Kim, Sara, Sy, Eric.  2021.  A Survey on Amazon Alexa Attack Surfaces. 2021 IEEE 18th Annual Consumer Communications Networking Conference (CCNC). :1–7.
Since being launched in 2014, Alexa, Amazon's versatile cloud-based voice service, is now active in over 100 million households worldwide [1]. Alexa's user-friendly, personalized vocal experience offers customers a more natural way of interacting with cutting-edge technology by allowing the ability to directly dictate commands to the assistant. Now in the present year, the Alexa service is more accessible than ever, available on hundreds of millions of devices from not only Amazon but third-party device manufacturers. Unfortunately, that success has also been the source of concern and controversy. The success of Alexa is based on its effortless usability, but in turn, that has led to a lack of sufficient security. This paper surveys various attacks against Amazon Alexa ecosystem including attacks against the frontend voice capturing and the cloud backend voice command recognition and processing. Overall, we have identified six attack surfaces covering the lifecycle of Alexa voice interaction that spans several stages including voice data collection, transmission, processing and storage. We also discuss the potential mitigation solutions for each attack surface to better improve Alexa or other voice assistants in terms of security and privacy.
2021-04-08
Ayub, M. A., Continella, A., Siraj, A..  2020.  An I/O Request Packet (IRP) Driven Effective Ransomware Detection Scheme using Artificial Neural Network. 2020 IEEE 21st International Conference on Information Reuse and Integration for Data Science (IRI). :319–324.
In recent times, there has been a global surge of ransomware attacks targeted at industries of various types and sizes from retail to critical infrastructure. Ransomware researchers are constantly coming across new kinds of ransomware samples every day and discovering novel ransomware families out in the wild. To mitigate this ever-growing menace, academia and industry-based security researchers have been utilizing unique ways to defend against this type of cyber-attacks. I/O Request Packet (IRP), a low-level file system I/O log, is a newly found research paradigm for defense against ransomware that is being explored frequently. As such in this study, to learn granular level, actionable insights of ransomware behavior, we analyze the IRP logs of 272 ransomware samples belonging to 18 different ransomware families captured during individual execution. We further our analysis by building an effective Artificial Neural Network (ANN) structure for successful ransomware detection by learning the underlying patterns of the IRP logs. We evaluate the ANN model with three different experimental settings to prove the effectiveness of our approach. The model demonstrates outstanding performance in terms of accuracy, precision score, recall score, and F1 score, i.e., in the range of 99.7%±0.2%.
2021-02-23
Cushing, R., Koning, R., Zhang, L., Laat, C. d, Grosso, P..  2020.  Auditable secure network overlays for multi-domain distributed applications. 2020 IFIP Networking Conference (Networking). :658—660.

The push for data sharing and data processing across organisational boundaries creates challenges at many levels of the software stack. Data sharing and processing rely on the participating parties agreeing on the permissible operations and expressing them into actionable contracts and policies. Converting these contracts and policies into a operational infrastructure is still a matter of research and therefore begs the question how should a digital data market place infrastructure look like? In this paper we investigate how communication fabric and applications can be tightly coupled into a multi-domain overlay network which enforces accountability. We prove our concepts with a prototype which shows how a simple workflow can run across organisational boundaries.

2021-01-25
More, S., Jamadar, I., Kazi, F..  2020.  Security Visualization and Active Querying for OT Network. :1—6.

Traditionally Industrial Control System(ICS) used air-gap mechanism to protect Operational Technology (OT) networks from cyber-attacks. As internet is evolving and so are business models, customer supplier relationships and their needs are changing. Hence lot of ICS are now connected to internet by providing levels of defense strategies in between OT network and business network to overcome the traditional mechanism of air-gap. This upgrade made OT networks available and accessible through internet. OT networks involve number of physical objects and computer networks. Physical damages to system have become rare but the number of cyber-attacks occurring are evidently increasing. To tackle cyber-attacks, we have a number of measures in place like Firewalls, Intrusion Detection System (IDS) and Intrusion Prevention System (IPS). To ensure no attack on or suspicious behavior within network takes place, we can use visual aids like creating dashboards which are able to flag any such activity and create visual alert about same. This paper describes creation of parser object to convert Common Event Format(CEF) to Comma Separated Values(CSV) format and dashboard to extract maximum amount of data and analyze network behavior. And working of active querying by leveraging packet level data from network to analyze network inclusion in real-time. The mentioned methodology is verified on data collected from Waste Water Treatment Plant and results are presented.,} booktitle = {2020 11th International Conference on Computing, Communication and Networking Technologies (ICCCNT)

2021-01-15
Liu, Y., Lin, F. Y., Ahmad-Post, Z., Ebrahimi, M., Zhang, N., Hu, J. L., Xin, J., Li, W., Chen, H..  2020.  Identifying, Collecting, and Monitoring Personally Identifiable Information: From the Dark Web to the Surface Web. 2020 IEEE International Conference on Intelligence and Security Informatics (ISI). :1—6.

Personally identifiable information (PII) has become a major target of cyber-attacks, causing severe losses to data breach victims. To protect data breach victims, researchers focus on collecting exposed PII to assess privacy risk and identify at-risk individuals. However, existing studies mostly rely on exposed PII collected from either the dark web or the surface web. Due to the wide exposure of PII on both the dark web and surface web, collecting from only the dark web or the surface web could result in an underestimation of privacy risk. Despite its research and practical value, jointly collecting PII from both sources is a non-trivial task. In this paper, we summarize our effort to systematically identify, collect, and monitor a total of 1,212,004,819 exposed PII records across both the dark web and surface web. Our effort resulted in 5.8 million stolen SSNs, 845,000 stolen credit/debit cards, and 1.2 billion stolen account credentials. From the surface web, we identified and collected over 1.3 million PII records of the victims whose PII is exposed on the dark web. To the best of our knowledge, this is the largest academic collection of exposed PII, which, if properly anonymized, enables various privacy research inquiries, including assessing privacy risk and identifying at-risk populations.

2020-11-23
Haddad, G. El, Aïmeur, E., Hage, H..  2018.  Understanding Trust, Privacy and Financial Fears in Online Payment. 2018 17th IEEE International Conference On Trust, Security And Privacy In Computing And Communications/ 12th IEEE International Conference On Big Data Science And Engineering (TrustCom/BigDataSE). :28–36.
In online payment, customers must transmit their personal and financial information through the website to conclude their purchase and pay the services or items selected. They may face possible fears from online transactions raised by their risk perception about financial or privacy loss. They may have concerns over the payment decision with the possible negative behaviors such as shopping cart abandonment. Therefore, customers have three major players that need to be addressed in online payment: the online seller, the payment page, and their own perception. However, few studies have explored these three players in an online purchasing environment. In this paper, we focus on the customer concerns and examine the antecedents of trust, payment security perception as well as their joint effect on two fundamentally important customers' aspects privacy concerns and financial fear perception. A total of 392 individuals participated in an online survey. The results highlight the importance, of the seller website's components (such as ease of use, security signs, and quality information) and their impact on the perceived payment security as well as their impact on customer's trust and financial fear perception. The objective of our study is to design a research model that explains the factors contributing to an online payment decision.
Ramapatruni, S., Narayanan, S. N., Mittal, S., Joshi, A., Joshi, K..  2019.  Anomaly Detection Models for Smart Home Security. 2019 IEEE 5th Intl Conference on Big Data Security on Cloud (BigDataSecurity), IEEE Intl Conference on High Performance and Smart Computing, (HPSC) and IEEE Intl Conference on Intelligent Data and Security (IDS). :19–24.
Recent years have seen significant growth in the adoption of smart homes devices. These devices provide convenience, security, and energy efficiency to users. For example, smart security cameras can detect unauthorized movements, and smoke sensors can detect potential fire accidents. However, many recent examples have shown that they open up a new cyber threat surface. There have been several recent examples of smart devices being hacked for privacy violations and also misused so as to perform DDoS attacks. In this paper, we explore the application of big data and machine learning to identify anomalous activities that can occur in a smart home environment. A Hidden Markov Model (HMM) is trained on network level sensor data, created from a test bed with multiple sensors and smart devices. The generated HMM model is shown to achieve an accuracy of 97% in identifying potential anomalies that indicate attacks. We present our approach to build this model and compare with other techniques available in the literature.
2020-10-12
Ferguson-Walter, Kimberly, Major, Maxine, Van Bruggen, Dirk, Fugate, Sunny, Gutzwiller, Robert.  2019.  The World (of CTF) is Not Enough Data: Lessons Learned from a Cyber Deception Experiment. 2019 IEEE 5th International Conference on Collaboration and Internet Computing (CIC). :346–353.
The human side of cyber is fundamentally important to understanding and improving cyber operations. With the exception of Capture the Flag (CTF) exercises, cyber testing and experimentation tends to ignore the human attacker. While traditional CTF events include a deeply rooted human component, they rarely aim to measure human performance, cognition, or psychology. We argue that CTF is not sufficient for measuring these aspects of the human; instead, we examine the value in performing red team behavioral and cognitive testing in a large-scale, controlled human-subject experiment. In this paper we describe the pros and cons of performing this type of experimentation and provide detailed exposition of the data collection and experimental controls used during a recent cyber deception experiment-the Tularosa Study. Finally, we will discuss lessons learned and how our experiences can inform best practices in future cyber operations studies of human behavior and cognition.
Foroughi, Farhad, Luksch, Peter.  2018.  Observation Measures to Profile User Security Behaviour. 2018 International Conference on Cyber Security and Protection of Digital Services (Cyber Security). :1–6.
Recognising user behaviour in real time is an important element of providing appropriate information and help to take suitable action or decision regarding cybersecurity threats. A user's security behaviour profile is a set of structured data and information to describe a user in an interactive environment between the user and computer. The first step for behaviour profiling is user behaviour model development including data collection. The data collection should be transparent as much as possible with minimum user interaction. Monitoring individual actions to obtain labelled training data is less costly and more effective in creating a behaviour profile. The most challenging issue in computer user security can be identifying suitable data. This research aims to determine required observation measures to capture user-system interactions to understand user's behaviour and create a user profile for cybersecurity purposes.
2020-09-28
Li, Wei, Hu, Chunqiang, Song, Tianyi, Yu, Jiguo, Xing, Xiaoshuang, Cai, Zhipeng.  2018.  Privacy-Preserving Data Collection in Context-Aware Applications. 2018 IEEE Symposium on Privacy-Aware Computing (PAC). :75–85.
Thanks to the development and popularity of context-aware applications, the quality of users' life has been improved through a wide variety of customized services. Meanwhile, users are suffering severe risk of privacy leakage and their privacy concerns are growing over time. To tackle the contradiction between the serious privacy issues and the growing privacy concerns in context-aware applications, in this paper, we propose a privacy-preserving data collection scheme by incorporating the complicated interactions among user, attacker, and service provider into a three-antithetic-party game. Under such a novel game model, we identify and rigorously prove the best strategies of the three parties and the equilibriums of the games. Furthermore, we evaluate the performance of our proposed data collection game by performing extensive numerical experiments, confirming that the user's data privacy can be effective preserved.
2020-09-21
Akbay, Abdullah Basar, Wang, Weina, Zhang, Junshan.  2019.  Data Collection from Privacy-Aware Users in the Presence of Social Learning. 2019 57th Annual Allerton Conference on Communication, Control, and Computing (Allerton). :679–686.
We study a model where a data collector obtains data from users through a payment mechanism to learn the underlying state from the elicited data. The private signal of each user represents her individual knowledge about the state. Through social interactions, each user can also learn noisy versions of her friends' signals, which is called group signals. Based on both her private signal and group signals, each user makes strategic decisions to report a privacy-preserved version of her data to the data collector. We develop a Bayesian game theoretic framework to study the impact of social learning on users' data reporting strategies and devise the payment mechanism for the data collector accordingly. Our findings reveal that, the Bayesian-Nash equilibrium can be in the form of either a symmetric randomized response (SR) strategy or an informative non-disclosive (ND) strategy. A generalized majority voting rule is applied by each user to her noisy group signals to determine which strategy to follow. When a user plays the ND strategy, she reports privacy-preserving data completely based on her group signals, independent of her private signal, which indicates that her privacy cost is zero. Both the data collector and the users can benefit from social learning which drives down the privacy costs and helps to improve the state estimation at a given payment budget. We derive bounds on the minimum total payment required to achieve a given level of state estimation accuracy.
2020-07-24
Navya, J M, Sanjay, H A, Deepika, KM.  2018.  Securing smart grid data under key exposure and revocation in cloud computing. 2018 3rd International Conference on Circuits, Control, Communication and Computing (I4C). :1—4.
Smart grid systems data has been exposed to several threats and attacks from different perspectives and have resulted in several system failures. Obtaining security of data and key exposure and enhancing system ability in data collection and transmission process are challenging, on the grounds smart grid data is sensitive and enormous sum. In this paper we introduce smart grid data security method along with advanced Cipher text policy attribute based encryption (CP-ABE). Cloud supported IoT is widely used in smart grid systems. Smart IoT devices collect data and perform status management. Data obtained from the IOT devices will be divided into blocks and encrypted data will be stored in different cloud server with different encrypted keys even when one cloud server is assaulted and encrypted key is exposed data cannot be decrypted, thereby the transmission and encryption process are done in correspondingly. We protect access-tree structure information even after the data is shared to user by solving revocation problem in which cloud will inform data owner to revoke and update encryption key after user has downloaded the data, which preserves the data privacy from unauthorized users. The analysis of the system concludes that our proposed system can meet the security requirements in smart grid systems along with cloud-Internet of things.
2020-07-09
Duan, Huayi, Zheng, Yifeng, Du, Yuefeng, Zhou, Anxin, Wang, Cong, Au, Man Ho.  2019.  Aggregating Crowd Wisdom via Blockchain: A Private, Correct, and Robust Realization. 2019 IEEE International Conference on Pervasive Computing and Communications (PerCom. :1—10.

Crowdsensing, driven by the proliferation of sensor-rich mobile devices, has emerged as a promising data sensing and aggregation paradigm. Despite useful, traditional crowdsensing systems typically rely on a centralized third-party platform for data collection and processing, which leads to concerns like single point of failure and lack of operation transparency. Such centralization hinders the wide adoption of crowdsensing by wary participants. We therefore explore an alternative design space of building crowdsensing systems atop the emerging decentralized blockchain technology. While enjoying the benefits brought by the public blockchain, we endeavor to achieve a consolidated set of desirable security properties with a proper choreography of latest techniques and our customized designs. We allow data providers to safely contribute data to the transparent blockchain with the confidentiality guarantee on individual data and differential privacy on the aggregation result. Meanwhile, we ensure the service correctness of data aggregation and sanitization by delicately employing hardware-assisted transparent enclave. Furthermore, we maintain the robustness of our system against faulty data providers that submit invalid data, with a customized zero-knowledge range proof scheme. The experiment results demonstrate the high efficiency of our designs on both mobile client and SGX-enabled server, as well as reasonable on-chain monetary cost of running our task contract on Ethereum.

2020-04-03
Fawaz, Kassem, Linden, Thomas, Harkous, Hamza.  2019.  Invited Paper: The Applications of Machine Learning in Privacy Notice and Choice. 2019 11th International Conference on Communication Systems Networks (COMSNETS). :118—124.
For more than two decades since the rise of the World Wide Web, the “Notice and Choice” framework has been the governing practice for the disclosure of online privacy practices. The emergence of new forms of user interactions, such as voice, and the enforcement of new regulations, such as the EU's recent General Data Protection Regulation (GDPR), promise to change this privacy landscape drastically. This paper discusses the challenges towards providing the privacy stakeholders with privacy awareness and control in this changing landscape. We will also present our recent research on utilizing Machine learning to analyze privacy policies and settings.
2020-02-17
Eckhart, Matthias, Ekelhart, Andreas, Weippl, Edgar.  2019.  Enhancing Cyber Situational Awareness for Cyber-Physical Systems through Digital Twins. 2019 24th IEEE International Conference on Emerging Technologies and Factory Automation (ETFA). :1222–1225.
Operators of cyber-physical systems (CPSs) need to maintain awareness of the cyber situation in order to be able to adequately address potential issues in a timely manner. For instance, detecting early symptoms of cyber attacks may speed up the incident response process and mitigate consequences of attacks (e.g., business interruption, safety hazards). However, attaining a full understanding of the cyber situation may be challenging, given the complexity of CPSs and the ever-changing threat landscape. In particular, CPSs typically need to be continuously operational, may be sensitive to active scanning, and often provide only limited in-depth analysis capabilities. To address these challenges, we propose to utilize the concept of digital twins for enhancing cyber situational awareness. Digital twins, i.e., virtual replicas of systems, can run in parallel to their physical counterparts and allow deep inspection of their behavior without the risk of disrupting operational technology services. This paper reports our work in progress to develop a cyber situational awareness framework based on digital twins that provides a profound, holistic, and current view on the cyber situation that CPSs are in. More specifically, we present a prototype that provides real-time visualization features (i.e., system topology, program variables of devices) and enables a thorough, repeatable investigation process on a logic and network level. A brief explanation of technological use cases and outlook on future development efforts completes this work.
Roukounaki, Aikaterini, Efremidis, Sofoklis, Soldatos, John, Neises, Juergen, Walloschke, Thomas, Kefalakis, Nikos.  2019.  Scalable and Configurable End-to-End Collection and Analysis of IoT Security Data : Towards End-to-End Security in IoT Systems. 2019 Global IoT Summit (GIoTS). :1–6.

In recent years, there is a surge of interest in approaches pertaining to security issues of Internet of Things deployments and applications that leverage machine learning and deep learning techniques. A key prerequisite for enabling such approaches is the development of scalable infrastructures for collecting and processing security-related datasets from IoT systems and devices. This paper introduces such a scalable and configurable data collection infrastructure for data-driven IoT security. It emphasizes the collection of (security) data from different elements of IoT systems, including individual devices and smart objects, edge nodes, IoT platforms, and entire clouds. The scalability of the introduced infrastructure stems from the integration of state of the art technologies for large scale data collection, streaming and storage, while its configurability relies on an extensible approach to modelling security data from a variety of IoT systems and devices. The approach enables the instantiation and deployment of security data collection systems over complex IoT deployments, which is a foundation for applying effective security analytics algorithms towards identifying threats, vulnerabilities and related attack patterns.

Alfaleh, Faleh, Alfehaid, Haitham, Alanzy, Mohammed, Elkhediri, Salim.  2019.  Wireless Sensor Networks Security: Case study. 2019 2nd International Conference on Computer Applications Information Security (ICCAIS). :1–4.
Wireless Sensor Networks (WSNs) are important and becoming more important as we integrate wireless sensor networks and the internet with different things, which has changed our life, and it is affected everywhere in our life like shopping, storage, live monitoring, smart home etc., called Internet of Things (IoT), as any use of the network physical devices that included in electronics, software, sensors, actuators, and connectivity which makes available these things to connect, collect and exchange data, and the most importantly thing is the accuracy of the data that has been collected in the Internet of Things, detecting sensor data with faulty readings is an important issue of secure communication and power consumption. So, requirement of energy-efficiency and integrity of information is mandatory.
2020-01-21
Le, Duc C., Nur Zincir-Heywood, A..  2019.  Machine Learning Based Insider Threat Modelling and Detection. 2019 IFIP/IEEE Symposium on Integrated Network and Service Management (IM). :1–6.

Recently, malicious insider attacks represent one of the most damaging threats to companies and government agencies. This paper proposes a new framework in constructing a user-centered machine learning based insider threat detection system on multiple data granularity levels. System evaluations and analysis are performed not only on individual data instances but also on normal and malicious insiders, where insider scenario specific results and delay in detection are reported and discussed. Our results show that the machine learning based detection system can learn from limited ground truth and detect new malicious insiders with a high accuracy.

2020-01-13
Yugha, R., Chithra, S..  2019.  Attribute Based Trust Evaluation for Secure RPL Protocol in IoT Environment. 2019 International Conference on Vision Towards Emerging Trends in Communication and Networking (ViTECoN). :1–7.
Internet of Things (IoT) is an advanced automation technology and analytics systems which connected physical objects that have access through the Internet and have their unique flexibility and an ability to be suitable for any environment. There are some critical applications like smart health care system, in which the data collection, sharing and routing through IoT has to be handled in sensitive way. The IPv6 Routing Protocol for LL(Low-power and Lossy) networks (RPL) is the routing protocols to ensure reliable data transfer in 6LOWPAN networks. However, RPL is vulnerable to number of security attacks which creates a major impact on energy consumption and memory requirements which is not suitable for energy constraint networks like IoT. This requires secured RPL protocol to be used for critical data transfer. This paper introduces a novel approach of combining a lightweight LBS (Location Based Service) authentication and Attribute Based Trust Evaluation (ABTE). The algorithm has been implemented for smart health care system and analyzed how its perform in the RPL protocol for IoT constrained environments.
2020-01-06
Cormode, Graham, Jha, Somesh, Kulkarni, Tejas, Li, Ninghui, Srivastava, Divesh, Wang, Tianhao.  2018.  Privacy at Scale: Local Differential Privacy in Practice. Proceedings of the 2018 International Conference on Management of Data. :1655–1658.
Local differential privacy (LDP), where users randomly perturb their inputs to provide plausible deniability of their data without the need for a trusted party, has been adopted recently by several major technology organizations, including Google, Apple and Microsoft. This tutorial aims to introduce the key technical underpinnings of these deployed systems, to survey current research that addresses related problems within the LDP model, and to identify relevant open problems and research directions for the community.
2019-12-16
Lopes, José, Robb, David A., Ahmad, Muneeb, Liu, Xingkun, Lohan, Katrin, Hastie, Helen.  2019.  Towards a Conversational Agent for Remote Robot-Human Teaming. 2019 14th ACM/IEEE International Conference on Human-Robot Interaction (HRI). :548–549.

There are many challenges when it comes to deploying robots remotely including lack of operator situation awareness and decreased trust. Here, we present a conversational agent embodied in a Furhat robot that can help with the deployment of such remote robots by facilitating teaming with varying levels of operator control.

2019-08-26
Mohammad, Z., Qattam, T. A., Saleh, K..  2019.  Security Weaknesses and Attacks on the Internet of Things Applications. 2019 IEEE Jordan International Joint Conference on Electrical Engineering and Information Technology (JEEIT). :431–436.

Internet of Things (IoT) is a contemporary concept for connecting the existing things in our environment with the Internet for a sake of making the objects information are accessible from anywhere and anytime to support a modern life style based on the Internet. With the rapid development of the IoT technologies and widely spreading in most of the fields such as buildings, health, education, transportation and agriculture. Thus, the IoT applications require increasing data collection from the IoT devices to send these data to the applications or servers which collect or analyze the data, so it is a very important to secure the data and ensure that do not reach a malicious adversary. This paper reviews some attacks in the IoT applications and the security weaknesses in the IoT environment. In addition, this study presents the challenges of IoT in terms of hardware, network and software. Moreover, this paper summarizes and points to some attacks on the smart car, smart home, smart campus, smart farm and healthcare.

2019-07-01
Carrasco, A., Ropero, J., Clavijo, P. Ruiz de, Benjumea, J., Luque, A..  2018.  A Proposal for a New Way of Classifying Network Security Metrics: Study of the Information Collected through a Honeypot. 2018 IEEE International Conference on Software Quality, Reliability and Security Companion (QRS-C). :633–634.

Nowadays, honeypots are a key tool to attract attackers and study their activity. They help us in the tasks of evaluating attacker's behaviour, discovering new types of attacks, and collecting information and statistics associated with them. However, the gathered data cannot be directly interpreted, but must be analyzed to obtain useful information. In this paper, we present a SSH honeypot-based system designed to simulate a vulnerable server. Thus, we propose an approach for the classification of metrics from the data collected by the honeypot along 19 months.

2019-06-10
Kumar, A., Aggarwal, A., Yadav, D..  2018.  A Multi-layered Outlier Detection Model for Resource Constraint Hierarchical MANET. 2018 5th IEEE Uttar Pradesh Section International Conference on Electrical, Electronics and Computer Engineering (UPCON). :1–7.

For sharing resources using ad hoc communication MANET are quite effective and scalable medium. MANET is a distributed, decentralized, dynamic network with no fixed infrastructure, which are self- organized and self-managed. Achieving high security level is a major challenge in case of MANET. Layered architecture is one of the ways for handling security challenges, which enables collection and analysis of data from different security dimensions. This work proposes a novel multi-layered outlier detection algorithm using hierarchical similarity metric with hierarchical categorized data. Network performance with and without the presence of outlier is evaluated for different quality-of-service parameters like percentage of APDR and AT for small (100 to 200 nodes), medium (200 to 1000 nodes) and large (1000 to 3000 nodes) scale networks. For a network with and without outliers minimum improvements observed are 9.1 % and 0.61 % for APDR and AT respectively while the maximum improvements of 22.1 % and 104.1 %.

2019-04-01
Ledbetter, W., Glisson, W., McDonald, T., Andel, T., Grispos, G., Choo, K..  2018.  Digital Blues: An Investigation Into the Use of Bluetooth Protocols. 2018 17th IEEE International Conference On Trust, Security And Privacy In Computing And Communications/ 12th IEEE International Conference On Big Data Science And Engineering (TrustCom/BigDataSE). :498–503.
The proliferation of Bluetooth mobile device communications into all aspects of modern society raises security questions by both academicians and practitioners. This environment prompted an investigation into the real-world use of Bluetooth protocols along with an analysis of documented security attacks. The experiment discussed in this paper collected data for one week in a local coffee shop. The data collection took about an hour each day and identified 478 distinct devices. The contribution of this research is two-fold. First, it provides insight into real-world Bluetooth protocols that are being utilized by the general public. Second, it provides foundational research that is necessary for future Bluetooth penetration testing research.