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      Electronic Financial Fraud: Abstract, Definitions, Vulnerabilities, Issues and Causes

      , ,

      Politics of the Machine Beirut 2019 (POM2019)

      Politics of the Machine

      11-14 June 2019

      Electronic Finance, Fraud, Internet of Things IoT, Intrusion Detection/Prevention Systems IDS & IPS, Machine Learning, Deep Learning, Artificial Intelligence, Digital Signature, Digital Profile

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          In financial systems, data under goes many phases of processing such as retrieving phase, data analysis and management phase, decision making and reporting phase making the data flow very comprehensive and complicated. Financial decisions are directly affected by offer and demand information which is relayed through gateways from numerous intangible sources or sensors distributed all over the financial universe. The communication of this information is generally defined as follows: unicast (one to one), multicast (many to many) and broadcast (one to many) generating massive amounts of heterogeneous data dumped in huge databases, the security of which is defined by 3 basic categories: data itself, storage of data and the access of data.

          Nowadays intangible sources of data, distributed sensors and gateways are titled as IoT which is defined as the interconnection of any device that communicate any type of digital data. IoT is facing an obvious and critical challenge in the area of security and privacy. The explosion of IoT devices needs security to respond to this huge phenomenon growth. Financial world has to take in consideration the evolution of the dark malicious side claiming AI and machine learning. The main goal of organizations is to increase production and maximize profit which can be achieved with IoT, but IoT provides EASEABILITY (ease of use) but not FEASABILITY (authenticity and security in other words) in other words more speed loss security. The enormous amount of raw unprocessed heterogeneous data retrieved from IoT sensor can have a vast impact on many domains in firms especially on the world of finance.

          By the year 2020, the number of IoT devices is expected to be 25 billion a lot of which will be processing very sensitive data including financial. Along with the rapid growth of IoT application and devices, cyber-attacks will also be improved and pose a more serious threat to security and privacy than ever before. The major attacks in IoT for the year 2020 are expected to be as follows:

          • IoT authentication

          • IoT encryption

          • IoT PKI

          • IoT security analytics

          • IoT API security

          In future research papers we will conduct more detailed in depth study of the anatomy of each of these attack profiles to determine a possible algorithm of these attacks evolution.

          Our goal is to establish some type of a smart system to guess and predict future attack profiles… Behavior based analogy. Since malicious code is implementing AI and machine learning, it is too hard to establish an ever-mutating signature of attack.

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          Author and article information

          June 2019
          June 2019
          : 9-14
          American University of Technology

          AUT, Borj Chmali
          Lebanese International University

          Beirut, Lebanon
          © Hoballah et al. Published by BCS Learning and Development Ltd. Proceedings of POM Beirut 2019

          This work is licensed under a Creative Commons Attribution 4.0 Unported License. To view a copy of this license, visit

          Politics of the Machine Beirut 2019
          Beirut, Lebanon
          11-14 June 2019
          Electronic Workshops in Computing (eWiC)
          Politics of the Machine
          Product Information: 1477-9358BCS Learning & Development
          Self URI (journal page):
          Electronic Workshops in Computing


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