Abbas Acar

Abbas Acar

Florida International University

H-index: 14

North America-United States

About Abbas Acar

Abbas Acar, With an exceptional h-index of 14 and a recent h-index of 14 (since 2020), a distinguished researcher at Florida International University, specializes in the field of Continuous Authentication, IoT Security/Privacy, Homomorphic Encryption.

His recent articles reflect a diverse array of research interests and contributions to the field:

Exploring Jamming and Hijacking Attacks for Micro Aerial Drones

A Survey on Decentralized Identifiers and Verifiable Credentials

50 Shades of Support: A Device-Centric Analysis of Android Security Updates

A comprehensive security and performance assessment of UAV authentication schemes

A review of on-device machine learning for IoT: An energy perspective

Forensic Analysis of Cryptojacking in Host-Based Docker Containers Using Honeypots

Energy consumption of on-device machine learning models for IoT intrusion detection

RøB: Ransomware over Modern Web Browsers

Abbas Acar Information

University

Florida International University

Position

___

Citations(all)

1911

Citations(since 2020)

1851

Cited By

511

hIndex(all)

14

hIndex(since 2020)

14

i10Index(all)

15

i10Index(since 2020)

15

Email

University Profile Page

Florida International University

Abbas Acar Skills & Research Interests

Continuous Authentication

IoT Security/Privacy

Homomorphic Encryption

Top articles of Abbas Acar

Exploring Jamming and Hijacking Attacks for Micro Aerial Drones

Authors

Yassine Mekdad,Abbas Acar,Ahmet Aris,Abdeslam El Fergougui,Mauro Conti,Riccardo Lazzeretti,Selcuk Uluagac

Journal

arXiv preprint arXiv:2403.03858

Published Date

2024/3/6

Recent advancements in drone technology have shown that commercial off-the-shelf Micro Aerial Drones are more effective than large-sized drones for performing flight missions in narrow environments, such as swarming, indoor navigation, and inspection of hazardous locations. Due to their deployments in many civilian and military applications, safe and reliable communication of these drones throughout the mission is critical. The Crazyflie ecosystem is one of the most popular Micro Aerial Drones and has the potential to be deployed worldwide. In this paper, we empirically investigate two interference attacks against the Crazy Real Time Protocol (CRTP) implemented within the Crazyflie drones. In particular, we explore the feasibility of experimenting two attack vectors that can disrupt an ongoing flight mission: the jamming attack, and the hijacking attack. Our experimental results demonstrate the effectiveness of such attacks in both autonomous and non-autonomous flight modes on a Crazyflie 2.1 drone. Finally, we suggest potential shielding strategies that guarantee a safe and secure flight mission. To the best of our knowledge, this is the first work investigating jamming and hijacking attacks against Micro Aerial Drones, both in autonomous and non-autonomous modes.

A Survey on Decentralized Identifiers and Verifiable Credentials

Authors

Carlo Mazzocca,Abbas Acar,Selcuk Uluagac,Rebecca Montanari,Paolo Bellavista,Mauro Conti

Journal

arXiv preprint arXiv:2402.02455

Published Date

2024/2/4

Digital identity has always been considered the keystone for implementing secure and trustworthy communications among parties. The ever-evolving digital landscape has gone through many technological transformations that have also affected the way entities are digitally identified. During this digital evolution, identity management has shifted from centralized to decentralized approaches. The last era of this journey is represented by the emerging Self-Sovereign Identity (SSI), which gives users full control over their data. SSI leverages decentralized identifiers (DIDs) and verifiable credentials (VCs), which have been recently standardized by the World Wide Web Community (W3C). These technologies have the potential to build more secure and decentralized digital identity systems, remarkably contributing to strengthening the security of communications that typically involve many distributed participants. It is worth noting that the scope of DIDs and VCs extends beyond individuals, encompassing a broad range of entities including cloud, edge, and Internet of Things (IoT) resources. However, due to their novelty, existing literature lacks a comprehensive survey on how DIDs and VCs have been employed in different application domains, which go beyond SSI systems. This paper provides readers with a comprehensive overview of such technologies from different perspectives. Specifically, we first provide the background on DIDs and VCs. Then, we analyze available implementations and offer an in-depth review of how these technologies have been employed across different use-case scenarios. Furthermore, we examine recent regulations and …

50 Shades of Support: A Device-Centric Analysis of Android Security Updates

Authors

Abbas Acar,Güliz Seray Tuncay,Esteban Luques,Harun Oz,Ahmet Aris,Selcuk Uluagac

Published Date

2024

Android is by far the most popular OS with over three billion active mobile devices. As in any software, uncovering vulnerabilities on Android devices and applying timely patches are both critical. Android Open Source Project has initiated efforts to improve the traceability of security updates through Security Patch Levels assigned to devices. While this initiative provided better traceability for the vulnerabilities, it has not entirely resolved the issues related to the timeliness and availability of security updates for end users. Recent studies on Android security updates have focused on the issue of delay during the security update roll-out, largely attributing this to factors related to fragmentation. However, these studies fail to capture the entire Android ecosystem as they primarily examine flagship devices or do not paint a comprehensive picture of the Android devices’ lifecycle due to the datasets spanning over a short timeframe. To address this gap in the literature, we utilize a device-centric approach to analyze the security update behavior of Android devices. Our approach aims to understand the security update distribution behavior of Original Equipment Manufacturers (OEM) by using a representative set of devices from each OEM and characterize the complete lifecycle of an average Android device. We obtained 367K official security update records from public sources, spanning from 2014 to 2023. Our dataset contains 599 unique devices from four major OEMs that are used in 97 countries and are associated with 109 carriers. We identify significant differences in the roll-out of security updates across different OEMs, device models and types, and …

A comprehensive security and performance assessment of UAV authentication schemes

Authors

Yassine Mekdad,Ahmet Aris,Abbas Acar,Mauro Conti,Riccardo Lazzeretti,Abdeslam El Fergougui,Selcuk Uluagac

Published Date

2024/1

In the past few years, unmanned aerial vehicles (UAVs) have significantly gained attention and popularity from industry, government, and academia. With their rapid development and deployment into the civilian airspace, UAVs play an important role in different applications, including goods delivery, search‐and‐rescue, and traffic monitoring. Therefore, providing secure communication through authentication models for UAVs is necessary for a successful and reliable flight mission. To satisfy such requirements, numerous authentication mechanisms have been proposed in the literature. However, the literature lacks a comprehensive study evaluating the security and performance of these solutions. In this article, we analyze the security and performance of 27 recent UAV authentication works by considering ten different key metrics. First, in the performance analysis, we show that the majority of UAV authentication …

A review of on-device machine learning for IoT: An energy perspective

Authors

Nazli Tekin,Ahmet Aris,Abbas Acar,Selcuk Uluagac,Vehbi Cagri Gungor

Published Date

2023/11/10

Recently, there has been a substantial interest in on-device Machine Learning (ML) models to provide intelligence for the Internet of Things (IoT) applications such as image classification, human activity recognition, and anomaly detection. Traditionally, ML models are deployed in the cloud or centralized servers to take advantage of their abundant computational resources. However, sharing data with the cloud and third parties degrades privacy and may cause propagation delay in the network due to a large amount of transmitted data impacting the performance of real-time applications. To this end, deploying ML models on-device (i.e., on IoT devices), in which data does not need to be transmitted, becomes imperative. However, deploying and running ML models on already resource-constrained IoT devices is challenging and requires intense energy consumption. Numerous works have been proposed in the …

Forensic Analysis of Cryptojacking in Host-Based Docker Containers Using Honeypots

Authors

Javier Franco,Abbas Acar,Ahmet Aris,Selcuk Uluagac

Published Date

2023/5/28

Blockchain-based cryptocurrencies have transformed financial transactions and created opportunities to profit from generating new coins through cryptomining. This has led to cybercriminals stealthily using their victim's computational power and resources for their own profit. Recent trends point to an increase in cryptojacking malware targeting devices with greater processing power such as host-based docker engines for faster and greater profit. In our study, we perform a forensic analysis for detecting cryptojacking (i.e., unauthorized cryptomining) in Docker containers using honeypots. Then, we present countermeasures for securing host-based Docker containers. In addition, we propose an approach for monitoring host-based Docker containers for cryptojacking detection. To the best of our knowledge, this is the first study investigating cryptojacking detection with the use of a honeypot system. Our results reveal …

Energy consumption of on-device machine learning models for IoT intrusion detection

Authors

Nazli Tekin,Abbas Acar,Ahmet Aris,A Selcuk Uluagac,Vehbi Cagri Gungor

Journal

Internet of Things

Published Date

2023/4/1

Recently, Smart Home Systems (SHSs) have gained enormous popularity with the rapid development of the Internet of Things (IoT) technologies. Besides offering many tangible benefits, SHSs are vulnerable to attacks that lead to security and privacy concerns for SHS users. Machine learning (ML)-based Intrusion Detection Systems (IDS) are proposed to address such concerns. Conventionally, ML models are trained and tested on computationally powerful platforms such as cloud services. Nevertheless, the data shared with the cloud is vulnerable to privacy attacks and causes latency, which decreases the performance of real-time applications like intrusion detection systems. Therefore, on-device ML models, in which the user data is kept locally, have emerged as promising solutions to ensure the security and privacy of the data for real-time applications. However, performing ML tasks requires high energy …

RøB: Ransomware over Modern Web Browsers

Authors

Harun Oz,Ahmet Aris,Abbas Acar,Güliz Seray Tuncay,Leonardo Babun,Selcuk Uluagac

Published Date

2023

File System Access (FSA) API enables web applications to interact with files on the users' local devices. Even though it can be used to develop rich web applications, it greatly extends the attack surface, which can be abused by adversaries to cause significant harm. In this paper, for the first time in the literature, we extensively study this new attack vector that can be used to develop a powerful new ransomware strain over a browser. Using the FSA API and WebAssembly technology, we demonstrate this novel browser-based ransomware called RøB as a malicious web application that encrypts the user's files from the browser. We use RøB to perform impact analysis with different OSs, local directories, and antivirus solutions as well as to develop mitigation techniques against it. Our evaluations show that RøB can encrypt the victim's local files including cloud-integrated directories, external storage devices, and network-shared folders regardless of the access limitations imposed by the API. Moreover, we evaluate and show how the existing defense solutions fall short against RøB in terms of their feasibility. We propose three potential defense solutions to mitigate this new attack vector. These solutions operate at different levels (ie, browser-level, file-system-level, and user-level) and are orthogonal to each other. Our work strives to raise awareness of the dangers of RøB-like browser-based ransomware strains and shows that the emerging API documentation (ie, the popular FSA) can be equivocal in terms of reflecting the extent of the threat.

Augmenting Security and Privacy in the Virtual Realm: An Analysis of Extended Reality Devices

Authors

Derin Cayir,Abbas Acar,Riccardo Lazzeretti,Marco Angelini,Mauro Conti,Selcuk Uluagac

Journal

IEEE Security & Privacy

Published Date

2023/12/4

We present a device-centric analysis of security and privacy attacks and defenses on extended reality (XR) devices. We present future research directions and propose design considerations to help ensure the security and privacy of XR devices.

The Truth Shall Set Thee Free: Enabling Practical Forensic Capabilities in Smart Environments.

Authors

Leonardo Babun,Amit Kumar Sikder,Abbas Acar,A Selcuk Uluagac

Published Date

2022/4/25

In smart environments such as smart homes and offices, the interaction between devices, users, and apps generate abundant data. Such data contain valuable forensic information about events and activities occurring in the smart environment. Nonetheless, current smart platforms do not provide any digital forensic capability to identify, trace, store, and analyze the data produced in these environments. To fill this gap, in this paper, we introduce VERITAS, a novel and practical digital forensic capability for the smart environment. VERITAS has two main components: Collector and Analyzer. The Collector implements mechanisms to automatically collect forensically-relevant data from the smart environment. Then, in the event of a forensic investigation, the Analyzer uses a First Order Markov Chain model to extract valuable and usable forensic information from the collected data. VERITAS then uses the forensic information to infer activities and behaviors from users, devices, and apps that violate the security policies defined for the environment. We implemented and tested VERITAS in a realistic smart office environment with 22 smart devices and sensors that generated 84209 forensically-valuable incidents. The evaluation shows that VERITAS achieves over 95% of accuracy in inferring different anomalous activities and forensic behaviors within the smart environment. Finally, VERITAS is extremely lightweight, yielding no overhead on the devices and minimal overhead in the backend resources (ie, the cloud servers).

Poster: Feasibility of malware visualization techniques against adversarial machine learning attacks

Authors

Harun Oz,Faraz Naseem,Ahmet Aris,Abbas Acar,Guliz Seray Tuncay,A Selcuk Uluagac

Journal

43rd IEEE Symposium on Security and Privacy (S&P)

Published Date

2022

Machine Learning (ML) has been indispensable to malware detection in recent years. Particularly, its subset-deep learning-based models can provide superior performance over traditional methods (ie, signature-based or heuristic-based) for malware detection [1]. However, recent research has shown that the efficiency of ML-based techniques can drop drastically due to adversaries attacking these systems via adversarially crafted/perturbed inputs. Such attacks have their roots in the computer vision domain with the study of Szegedy et al.[2], and then followed by others [3]–[5]. In the malware detection domain, adversarial ML attacks to ML-based malware detectors involve adding carefully crafted perturbations to the malware samples that preserve the malicious functionality of the malware while allowing the samples to evade the target ML-based malware classifiers (ie, modified malware samples are classified as benign). Using such attacks, researchers were able to craft adversarial malware samples and successfully evaded ML-based malware detection systems including Windows Portable Executable (PE)-based malware detectors [6]–[8], Android malware detectors [9],[10], PDF-malware classifiers [11],[12] and even cloud based proprietary anti-virus engines (eg, Kaspersky, Eset, Sophos)[13]. These examples clearly demonstrate that it is possible for attackers to evade state-of-the-art ML-based malware classifiers not by complex concealment techniques (eg, polymorphism, metamorphism, packing), but by simple, minute adversarial perturbations carefully crafted via adversarial ML attacks. In order to defend ML-based malware classifiers …

A Lightweight IoT Cryptojacking Detection Mechanism in Heterogeneous Smart Home Networks

Authors

Ege Tekiner,Abbas Acar,A Selcuk Uluagac

Journal

Proceedings of the 29th Network and Distributed System Security (NDSS) Symposium

Published Date

2022

Recently, cryptojacking malware has become an easy way of reaching and profiting from a large number of victims. Prior works studied the cryptojacking detection systems focusing on both in-browser and host-based cryptojacking malware. However, none of these earlier works investigated different attack configurations and network settings in this context. For example, an attacker with an aggressive profit strategy may increase computational resources to the maximum utilization to benefit more in a short time, while a stealthy attacker may want to stay undetected longer time on the victim’s device. The accuracy of the detection mechanism may differ for an aggressive and stealthy attacker. Not only profit strategies, but also the cryptojacking malware type, the victim’s device as well as various network settings where the network is fully or partially compromised may play a key role in the performance evaluation of the detection mechanisms. In addition, smart home networks with multiple IoT devices are easily exploited by the attackers, and they are equipped to mine cryptocurrency on behalf of the attacker. However, no prior works investigated the impact of cryptojacking malware on IoT devices and compromised smart home networks. In this paper, we first propose an accurate and efficient IoT cryptojacking detection mechanism based on network traffic features, which can detect both in-browser and host-based cryptojacking. Then, we focus on the cryptojacking implementation problem on new device categories (eg, IoT) and designed several novel experiment scenarios to assess our detection mechanism to cover the current attack surface of the …

2021 Index IEEE Transactions on Mobile Computing Vol. 20

Authors

MS Abrishami,A Acar,S Aggarwal,A Aghagolzadeh,I Ahmed,SH Ahmed,K Akkaya,H Aksu,IF Akyildiz,MAU Alam,T Alexandri,AS Alfa,M Alouini,P Ameigeiras,Z An,SMH Andargoli,P Andres-Maldonado,CK Anjinappa,A Arafa,A Asheralieva,H Assasa,C Assi,F Babich,S Badri,RK Balan,S Balasubramaniam,B Ban,L Bao,N Bartolini,J Bassey,SS Bhatti,H Bi,K Bian,E Bigal,B Biswas,N Blefari-Melazzi,SH Bouk,A Bozorgchenani,P Brand,T Braud,J Brendel,R Brunner,Y Bu,D Bugos,E Bulut,C Busch,R Buyya,C Cai,J Cai,L Cai,Z Cai,B Cao,C Cao,D Cao,G Cao,J Cao,X Cao,MR Carlos

Journal

IEEE Transactions on Mobile Computing

Published Date

2022/1

Harwahyu, R., Cheng, R., Liu, D., and Sari, RF, Fair Configuration Scheme for Random Access in NB-IoT with Multiple Coverage Enhancement Levels; TMC April 2021 1408-1419 Hasholzner, R., see Brand, P., TMC Aug. 2021 2518-2535 He, B., see Bu, Y., TMC Feb. 2021 722-738 He, B., see Jin, L., TMC March 2021 1138-1155 He, Q., see Chen, J., TMC Sept. 2021 2728-2744 He, S., see Li, S., TMC Nov. 2021 3117-3130 He, T., see Wang, S., TMC Jan. 2021 204-216 He, T., see Yu, N., TMC March 2021 909-927 He, X., see Fan, X., TMC June 2021 2154-2171 He, Y., see Jiang, C., TMC Feb. 2021 634-646

A first look at code obfuscation for webassembly

Authors

Shrenik Bhansali,Ahmet Aris,Abbas Acar,Harun Oz,A Selcuk Uluagac

Published Date

2022/5/16

WebAssembly (Wasm) has seen a lot of attention lately as it spreads through the mobile computing domain and becomes the new standard for performance-oriented web development. It has diversified its uses far beyond just web applications by acting as an execution environment for mobile agents, containers for IoT devices, and enabling new serverless approaches for edge computing. Within the numerous uses of Wasm, not all of them are benign. With the rise of Wasm-based cryptojacking malware, analyzing Wasm applications has been a hot topic in the literature, resulting in numerous Wasm-based cryptojacking detection systems. Many of these methods rely on static analysis, which traditionally can be circumvented through obfuscation. However, the feasibility of the obfuscation techniques for Wasm programs has never been investigated thoroughly. In this paper, we address this gap and perform the first …

In-browser cryptomining for good: An untold story

Authors

Ege Tekiner,Abbas Acar,A Selcuk Uluagac,Engin Kirda,Ali Aydin Selcuk

Published Date

2021/8/23

In-browser cryptomining uses the computational power of a website's visitors to mine cryptocurrency, i.e., to create new coins. With the rise of ready-to-use mining scripts distributed by service providers (e.g., Coinhive), it has become trivial to turn a website into a cryptominer by copying and pasting the mining script. Both legitimate webpage owners who want to raise an extra revenue under users' explicit consent and malicious actors who wish to exploit the computational power of the users' computers without their consent have started to utilize this emerging paradigm of cryptocurrency operations. In-browser cryptomining, though mostly abused by malicious actors in practice, is indeed a promising funding model that can be utilized by website owners, publishers, or non-profit organizations for legitimate business purposes, such as to collect revenue or donations for humanitarian projects, inter alia. However, our …

The Similarities and Differences (Congruency) of Nurse-postoperative Patient Dyads Related to Their Attitudes/perceptions, Subjective and Social Norms/factors, and Actions …

Authors

NUR PINAR AYAZ

Published Date

2021

Despite advances in pharmacology, surgical techniques, and perioperative care, pain is a significant symptom of surgical patients (Centers for Disease Control and Prevention, 2015). This study explored the similarities and differences of nurse-post-operative patient dyads related to attitudes/perceptions, subjective and social norms, including culture/ethnicity, and actions/behaviors related to pain and pain management. Guided by the Theory of Planned Behavior (Azjen, 1991) and Leininger's Theory of Transcultural Nursing (Leininger, 1999), this descriptive qualitative study was based on a purposive sample of 6 nurses (Hispanic, African American, Caucasian) and 12 patients in dyads (nurse and a patient of the same ethnicity and one of a different ethnicity) from a hospital observation within 48 hours of surgery. The results indicated that all nurses used the pain scale to measure pain intensity but did not conduct a comprehensive pain assessment; were concerned about adverse effects and addiction related to opioids; and reluctant to administer opioids beyond the first day post-op. Most patients expected total and quick pain control, with less concern about short-term opioid use. Nurses and patients had limited knowledge of non-pharmacologic and complementary therapies for pain relief. Nurses expressed greater familiarity in caring for patients of the same cultural background, while patients did not identify culture as a factor in their care. Cognitive dissonance occurs when nursing education emphasizes cultural sensitivity, while nurses emphasize “treating all patients the same,” which has implications for education and research.

SoK: cryptojacking malware

Authors

Ege Tekiner,Abbas Acar,A Selcuk Uluagac,Engin Kirda,Ali Aydin Selcuk

Published Date

2021/9/6

Emerging blockchain and cryptocurrency-based technologies are redefining the way we conduct business in cyberspace. Today, a myriad of blockchain and cryp-tocurrency systems, applications, and technologies are widely available to companies, end-users, and even malicious actors who want to exploit the computational resources of regular users through cryptojacking malware. Especially with ready-to-use mining scripts easily provided by service providers (e.g., Coinhive) and untraceable cryptocurrencies (e.g., Monero), cryptojacking malware has become an indispensable tool for attackers. Indeed, the banking industry, major commercial websites, government and military servers (e.g., US Dept. of Defense), online video sharing platforms (e.g., Youtube), gaming platforms (e.g., Nintendo), critical infrastructure resources (e.g., routers), and even recently widely popular remote video conferencing/meeting …

A lightweight privacy-aware continuous authentication protocol-paca

Authors

Abbas Acar,Shoukat Ali,Koray Karabina,Cengiz Kaygusuz,Hidayet Aksu,Kemal Akkaya,Selcuk Uluagac

Journal

ACM Transactions on Privacy and Security (TOPS)

Published Date

2021/9/2

As many vulnerabilities of one-time authentication systems have already been uncovered, there is a growing need and trend to adopt continuous authentication systems. Biometrics provides an excellent means for periodic verification of the authenticated users without breaking the continuity of a session. Nevertheless, as attacks to computing systems increase, biometric systems demand more user information in their operations, yielding privacy issues for users in biometric-based continuous authentication systems. However, the current state-of-the-art privacy technologies are not viable or costly for the continuous authentication systems, which require periodic real-time verification. In this article, we introduce a novel, lightweight, privacy-aware, and secure continuous authentication protocol called PACA. PACA is initiated through a password-based key exchange (PAKE) mechanism, and it continuously …

Kratos: Multi-user multi-device-aware access control system for the smart home

Authors

Amit Kumar Sikder,Leonardo Babun,Z Berkay Celik,Abbas Acar,Hidayet Aksu,Patrick McDaniel,Engin Kirda,A Selcuk Uluagac

Published Date

2020/7/8

In a smart home system, multiple users have access to multiple devices, typically through a dedicated app installed on a mobile device. Traditional access control mechanisms consider one unique trusted user that controls the access to the devices. However, multi-user multi-device smart home settings pose fundamentally different challenges to traditional single-user systems. For instance, in a multi-user environment, users have conflicting, complex, and dynamically changing demands on multiple devices, which cannot be handled by traditional access control techniques. To address these challenges, in this paper, we introduce Kratos, a novel multi-user and multi-device-aware access control mechanism that allows smart home users to flexibly specify their access control demands. Kratos has three main components: user interaction module, back-end server, and policy manager. Users can specify their desired …

Peek-a-boo: I see your smart home activities, even encrypted!

Authors

Abbas Acar,Hossein Fereidooni,Tigist Abera,Amit Kumar Sikder,Markus Miettinen,Hidayet Aksu,Mauro Conti,Ahmad-Reza Sadeghi,Selcuk Uluagac

Published Date

2020/7/8

A myriad of IoT devices such as bulbs, switches, speakers in a smart home environment allow users to easily control the physical world around them and facilitate their living styles through the sensors already embedded in these devices. Sensor data contains a lot of sensitive information about the user and devices. However, an attacker inside or near a smart home environment can potentially exploit the innate wireless medium used by these devices to exfiltrate sensitive information from the encrypted payload (i.e., sensor data) about the users and their activities, invading user privacy. With this in mind, in this work, we introduce a novel multi-stage privacy attack against user privacy in a smart environment. It is realized utilizing state-of-the-art machine-learning approaches for detecting and identifying the types of IoT devices, their states, and ongoing user activities in a cascading style by only passively sniffing the …

See List of Professors in Abbas Acar University(Florida International University)

Abbas Acar FAQs

What is Abbas Acar's h-index at Florida International University?

The h-index of Abbas Acar has been 14 since 2020 and 14 in total.

What are Abbas Acar's top articles?

The articles with the titles of

Exploring Jamming and Hijacking Attacks for Micro Aerial Drones

A Survey on Decentralized Identifiers and Verifiable Credentials

50 Shades of Support: A Device-Centric Analysis of Android Security Updates

A comprehensive security and performance assessment of UAV authentication schemes

A review of on-device machine learning for IoT: An energy perspective

Forensic Analysis of Cryptojacking in Host-Based Docker Containers Using Honeypots

Energy consumption of on-device machine learning models for IoT intrusion detection

RøB: Ransomware over Modern Web Browsers

...

are the top articles of Abbas Acar at Florida International University.

What are Abbas Acar's research interests?

The research interests of Abbas Acar are: Continuous Authentication, IoT Security/Privacy, Homomorphic Encryption

What is Abbas Acar's total number of citations?

Abbas Acar has 1,911 citations in total.

What are the co-authors of Abbas Acar?

The co-authors of Abbas Acar are Ahmad-Reza Sadeghi, Patrick McDaniel, Engin Kirda, Mauro Conti, Selcuk Uluagac.

    Co-Authors

    H-index: 93
    Ahmad-Reza Sadeghi

    Ahmad-Reza Sadeghi

    Technische Universität Darmstadt

    H-index: 83
    Patrick McDaniel

    Patrick McDaniel

    Penn State University

    H-index: 75
    Engin Kirda

    Engin Kirda

    North Eastern University

    H-index: 74
    Mauro Conti

    Mauro Conti

    Università degli Studi di Padova

    H-index: 44
    Selcuk Uluagac

    Selcuk Uluagac

    Florida International University

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