Publications

@conference{amin-quantum-ska,

title = {Performance Evaluation of Quantum-Secure Symmetric Key Agreement},

author = {Amin Rois Sinung Nugroho and Muhammad Ikram and Dali Kaafar},

url = {https://github.com/amin-mq-cyber/pq_ska},

doi = {https://doi.org/10.1109/SIN63213.2024.10871430},

year  = {2024},

date = {2024-12-03},

urldate = {2024-12-03},

journal = {17th International Conference on Security of Information and Networks (SIN’24 CONF)},

publisher = {17th International Conference on Security of Information and Networks (SIN’24 CONF)},

abstract = {Quantum-safe public key exchange protocols face significant challenges, both in hardware-based and software-based approaches. Quantum key distribution, which relies on specialized quantum hardware, presents a significant barrier to widespread adoption due to its high cost and limited scalability. Conversely, software-based solutions using post-quantum algorithms introduce their complications, such as increased resource demands and larger ciphertexts. Furthermore, the security of these post-quantum algorithms remains relatively untested, which has led to the emerging trend of hybrid deployment, combining classical and quantum-resistant techniques to hedge against potential vulnerabilities.



In this work, we address these problems by proposing a novel quantum-safe symmetric key agreement (SKA) protocol that is both lightweight and scalable. Our approach involves a hybrid mechanism, leveraging secret strings distributed through a combination of classical and quantum public key pairs during the initial key exchange. This hybrid approach enhances security by utilizing both quantum-resistant algorithms and classical methods, mitigating the risks associated with the nascent nature of post-quantum cryptography. After the initial key exchange, the protocol completes the process using a quantum-safe AES symmetric key, ensuring both security and efficiency. All communications are securely authenticated over classical TLS, making our solution compatible with existing infrastructure.



The contributions of this work are threefold. First, we demonstrate that our protocol incurs minimal performance overhead, with only 99ms for purely quantum SKA and 199ms for the hybrid version, compared to classical SKA protocol. Second, our SKA protocol remains robust under various network conditions, including delays, packet losses, and bandwidth variations, maintaining small and consistent overheads. Third, we show that our solution is highly scalable, with an overhead of only one second for every additional five concurrent users, and that performance improves significantly with increased computational resources-achieving a 50-60% improvement when scaling from two to four CPUs. Additionally, our security evaluations confirm that the protocol provides consistent and sufficient randomness throughout the key agreement process, ensuring quantum-resistance at every stage.},

keywords = {},

pubstate = {published},

tppubtype = {conference}

}

@conference{amda,

title = {AMDA: Anchor Mobility Data Analytic for Determining Home-Work Location from Mobile Positioning Data.},

author = {Amanda Pratama Putra and

Wa Ode Zuhayeni Madjida and

Ignatius Aditya Setyadi and

Amin Rois Sinung Nugroho and

Alfatihah Reno MNSP Munaf

},

url = {https://proceedings.stis.ac.id/icdsos/article/view/239},

doi = {https://doi.org/10.34123/icdsos.v2021i1.239},

year  = {2022},

date = {2022-04-01},

urldate = {2022-04-01},

publisher = {Vol. 2021 No. 1 (2021): Proceedings of 2021 International Conference on Data Science and Official Statistics (ICDSOS)},

abstract = {In conducting a mobility analysis using Mobile Positioning Data, the most critical step is to define each customer's usual environment. The initial concept of mobility used is the movement that occurs from and to every usual environment, so errors in determining the usual environment will cause incorrect mobility statistics. Therefore, Anchor Mobility Data Analytic (AMDA) is proposed for Home-Work Location Determination from Mobile Positioning Data. This algorithm uses clockwise reversal to make it easier to classify someone in their usual environment. Unfortunately, only about 80% of the raw data can be used to establish usual environments. The remaining 20% do not have sufficient data history. This study found that the accuracy of AMDA in determining monthly home location was 98.8% at the provincial level and 88.7% at the regency level. As for the determination of monthly work locations, 98.9% at the provincial level and 70.4% at the regency level.},

keywords = {},

pubstate = {published},

tppubtype = {conference}

}

@conference{data-input-mpd,

title = {Data Input Quality Metrics on Mobile Positioning Data (MPD)},

author = {Alfatihah Reno MNSP Munaf and

Amanda Pratama Putra and

Wa Ode Zuhayeni Madjida and

Ignatius Aditya Setyadi and

Amin Rois Sinung Nugroho

},

url = {https://proceedings.stis.ac.id/icdsos/article/view/134},

doi = {https://doi.org/10.34123/icdsos.v2021i1.134},

year  = {2022},

date = {2022-01-04},

publisher = {Vol. 2021 No. 1 (2021): Proceedings of 2021 International Conference on Data Science and Official Statistics (ICDSOS)},

abstract = {Statistics Indonesia (BPS) has been using Mobile Positioning Data (MPD) to support official statistics since 2016. As a source of big data, MPD also has veracity characteristics, indicating uncertainty in the data. Therefore, it is necessary to check that the data are good enough to allow further analysis and the quality assurance process. Currently, there is no established international standard for quality assurance of MPD. This paper describes the quality matrix used by BPS in examining data from mobile operators. BPS uses thirteen indicators in conducting quality assurance, where the inspection uses several different methods, such as setting a threshold, checking data completeness, and checking the form of data distribution. Exploratory Data Analysis is carried out to determine whether the data meets the requirements for further analysis. We conducted this research on a mobile network operator data for June - July 2020 as the basis for MPD analysis in 2021. Based on the inspection during this period, BPS can cooperate with this cellular operator to conduct data analysis in 2021. However, the operator must repeat the calculation of the required matrix as quality assurance every month.},

keywords = {},

pubstate = {published},

tppubtype = {conference}

}

@conference{amin-inferring-mobile-apps,

title = {Inferring Mobile Apps from the Resource Usage Patterns},

author = {Amin Rois Sinung Nugroho and Qinghua Li},

url = {https://ieeexplore.ieee.org/document/7944876},

doi = {10.1109/MobileCloud.2017.21},

year  = {2017},

date = {2017-04-06},

publisher = {2017 5th IEEE International Conference on Mobile Cloud Computing, Services, and Engineering (MobileCloud)},

abstract = {Despite many applications, mobile cloud computing induces privacy concerns. In particular, when mobile device users offload the computation of a mobile app to the cloud, they may not want the cloud service provider (CSP) to know what kind of app they are using, since that information might be used to infer their personal activities and living habits. One possible way for the CSP to learn the type of an offloaded app is to observe the resource usage patterns of the app (e.g., CPU and memory usage), since different apps have different resource needs due to their distinct computation workloads. To assess this risk, this paper answers the following question: Can the type of mobile app (e.g., email, web browsing, mobile game, etc.) used by a user be inferred from the resource usage pattern of the mobile app? We investigate the resource usage patterns of apps and whether the difference in resource usage pattern is sufficient to classify different types of apps. Specifically, two privacy attacks under the same framework are proposed based on supervised learning algorithms. Then these attacks are implemented and tested in a mobile device and in a cloud computing environment. Experiments show that, when the resource usage patterns on a mobile device are used, the type of app can be inferred with high probabilities; when the resource usage patterns on a cloud server are used, the type of app can be inferred with accuracy much higher than random guess.},

keywords = {},

pubstate = {published},

tppubtype = {conference}

}

@conference{amin-linux-thesis-conference,

title = {Developing GNU/Linux Distribution for Migration of BPS-Statistics Indonesia Computer System from Windows Based to GNU/Linux Based (in Bahasa Indonesia).},

author = {Amin Rois Sinung Nugroho},

year  = {2008},

date = {2008-12-01},

urldate = {2008-12-01},

publisher = {Proceeding of National Conference on Information System, STIKOM, Denpasar, Bali, Indonesia.},

keywords = {},

pubstate = {published},

tppubtype = {conference}

}