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Profile Details
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USD 50 /hr
Hire Dr. Anibrata P.
Italy
USD 50 /hr

Software Architect | Quantum Software Engineering | Security and Privacy Expert | Techinical Writing & Review

Profile Summary
Subject Matter Expertise
Services
Writing Technical Writing, General Proofreading & Editing, Translation
Research Scientific and Technical Research, Systematic Literature Review
Consulting Scientific and Technical Consulting
Data & AI Predictive Modeling, Algorithm Design-ML, Data Visualization, Big Data Analytics, Data Mining, Data Cleaning, Data Processing, Data Insights
Work Experience

PhD Student

University of Bari Aldo Moro

February 2022 - January 2025

Consultant

Tata Consultancy Services (India)

February 2007 - March 2014

Education

PhD in Computer Science

University of Bari - Italy

February 2022 - Present

Masters in Computer Science

Universidade do Amazonas - Brazil

April 2014 - April 2016

Computer Science and Engineering

Kalyani University - India

July 2000 - July 2004

Certifications
  • Certification details not provided.
Publications
CONFERENCE PAPER
Anibrata Pal, Vita Santa Barletta, Danilo Caivano, Christian Catalano, Mirko De Vincentiis (2024). Machine Learning for Automotive Security in Technology Transfer . Information Systems and Technologies.
Anibrata Pal, Vita Santa Barletta, Christian Catalano, Mirko De Vincentiis, Michele Scalera (2023). Artificial Intelligence for Automotive Security: How to Support Developers in Automotive Solutions . 2023 IEEE International Conference on Metrology for eXtended Reality, Artificial Intelligence and Neural Engineering (MetroXRAINE).
Anibrata Pal, Vita Santa Barletta, Danilo Caivano, Mirko De Vincentiis, Francesco Volpe (2023). Automotive Knowledge Base for Supporting Vehicle-SOC Analysts . 2023 IEEE International Conference on Metrology for eXtended Reality, Artificial Intelligence and Neural Engineering (MetroXRAINE).
De Vincentiis, M., Pal, A., Ragone, A., Scalera, M.(2023). A Multi-class Intrusion Detection System for Cyber Security Education in Automotive Industry . CEUR Workshop Proceedings. 3408.
Securing Smart Cities: Unraveling Quantum as a Service @inproceedings{10.1145/3617570.3617865,author={Caivano, Danilo and De Vincentiis, Mirko and Pal, Anibrata and Ragone, Azzurra},title={Securing Smart Cities: Unraveling Quantum as a Service},year={2023},isbn={9798400703768},publisher={Association for Computing Machinery},address={New York, NY, USA},url={https://doi.org/10.1145/3617570.3617865},doi={10.1145/3617570.3617865},abstract={Smart Cities attract significant attention and investment from government and private entities, leading to their rapid development. With such growth in Smart Cities, data volume and diversity increase due to Internet of Things (IoT) sensors in devices. However, this abundance of data exposes millions of vulnerable devices to cyber threats, risking compromised security and sensitive information. To address these risks, Smart Cities utilize Intelligent Operations Centers (IOCs) equipped with Machine Learning (ML) algorithms. These algorithms continuously monitor and protect against security incidents. Although Quantum Computing (QC) has shown promise in Smart City applications, its usage as a service is still in the early stages. In this paper, we propose to investigate the utilization of Quantum as a Service (QaaS) to develop an architecture for securing a Smart City. Our approach employs Quantum Classifiers, QBoost from D-Wave Leap Quantum Cloud and Variational Quantum Classifier, and PegaSoS Quantum Support Vector Classifier from IBM Quantum Services. These provide real-time data classification and a user-friendly dashboard to display security incidents in the Smart City. Among the three quantum classifiers considered for the proposed architecture, QBoost performed the best both regarding quality and processing time.},booktitle={Proceedings of the 2nd International Workshop on Quantum Programming for Software Engineering},pages={1–6},numpages={6},keywords={Security, Quantum as a service, Quantum Machine Learning, Smart City},location={<conf-loc>, <city>San Francisco</city>, <state>CA</state>, <country>USA</country>, </conf-loc>},series={QP4SE 2023}} . Proceedings of the 2nd International Workshop on Quantum Programming for Software Engineering.
Extending Developer Support: Quantum Artificial Intelligence for Automotive Security @inproceedings{10.1145/3617570.3617866,author={Caivano, Danilo and De Vincentiis, Mirko and Pal, Anibrata and Scalera, Michele},title={Extending Developer Support: Quantum Artificial Intelligence for Automotive Security},year={2023},isbn={9798400703768},publisher={Association for Computing Machinery},address={New York, NY, USA},url={https://doi.org/10.1145/3617570.3617866},doi={10.1145/3617570.3617866},abstract={With the adoption of advanced technology in the automotive field, managing the risks of attack in modern vehicles becomes essential. Some research works substantially exploit Machine Learning algorithms to identify threats conducted on vehicles, particularly on the Controller Area Network (CAN) bus. Therefore, it is necessary not only to use Intrusion Detection Systems (IDSs) to identify attacks but also to help the engineers in the automotive field understand the dangerousness of the attack and help them resolve the vulnerability. With the increasing attention to Quantum Computing (QC), QC-based Artificial Intelligence algorithms have become very popular among many researchers for improving the prediction and the time performance to identify an attack. This paper proposes a methodology, SeQuADE (Secure Quantum Automotive Development and Engineering), to identify CAN attacks and to support developers by proposing associated automotive vulnerabilities and solutions obtained from National Vulnerability Database (NVD).},booktitle={Proceedings of the 2nd International Workshop on Quantum Programming for Software Engineering},pages={7–12},numpages={6},keywords={automotive, QBoost, multi-class IDS, NLP},location={<conf-loc>, <city>San Francisco</city>, <state>CA</state>, <country>USA</country>, </conf-loc>},series={QP4SE 2023}} . Proceedings of the 2nd International Workshop on Quantum Programming for Software Engineering.
Baldassarre, M.T., De Vincentiis, M., Pal, A., Scalera, M.(2023). Quantum Artificial Intelligence for Cyber Security Education in Software Engineering . CEUR Workshop Proceedings. 3408.
Caivano, D., De Vincentiis, M., Nitti, F., Pal, A.(2022). Quantum optimization for fast CAN bus intrusion detection . QP4SE 2022 - Proceedings of the 1st International Workshop on Quantum Programming for Software Engineering, co-located with ESEC/FSE 2022. p. 15-18.
BOOK
Barletta, V.S., Caivano, D., Lako, A., Pal, A.(2023). Quantum as a Service Architecture for Security in a Smart City . Communications in Computer and Information Science. 1871 CCIS. p. 76-89.