TY - BOOK ID - 32841759 TI - Artificial Intelligence Tools for Cyber Attribution AU - Nunes, Eric. AU - Shakarian, Paulo. AU - Simari, Gerardo I. AU - Ruef, Andrew. PY - 2018 SN - 3319737880 3319737872 PB - Cham : Springer International Publishing : Imprint: Springer, DB - UniCat KW - Artificial intelligence. KW - Computer security. KW - Computer science. KW - Computer Science. KW - Artificial Intelligence (incl. Robotics). KW - Security. KW - AI (Artificial intelligence) KW - Artificial thinking KW - Electronic brains KW - Intellectronics KW - Intelligence, Artificial KW - Intelligent machines KW - Machine intelligence KW - Thinking, Artificial KW - Bionics KW - Cognitive science KW - Digital computer simulation KW - Electronic data processing KW - Logic machines KW - Machine theory KW - Self-organizing systems KW - Simulation methods KW - Fifth generation computers KW - Neural computers KW - Computer privacy KW - Computer system security KW - Computer systems KW - Computers KW - Cyber security KW - Cybersecurity KW - Electronic digital computers KW - Protection of computer systems KW - Security of computer systems KW - Data protection KW - Security systems KW - Hacking KW - Protection KW - Security measures KW - Data protection. KW - Artificial Intelligence. KW - Data governance KW - Data regulation KW - Personal data protection KW - Protection, Data UR - https://www.unicat.be/uniCat?func=search&query=sysid:32841759 AB - This SpringerBrief discusses how to develop intelligent systems for cyber attribution regarding cyber-attacks. Specifically, the authors review the multiple facets of the cyber attribution problem that make it difficult for “out-of-the-box” artificial intelligence and machine learning techniques to handle.  Attributing a cyber-operation through the use of multiple pieces of technical evidence (i.e., malware reverse-engineering and source tracking) and conventional intelligence sources (i.e., human or signals intelligence) is a difficult problem not only due to the effort required to obtain evidence, but the ease with which an adversary can plant false evidence. This SpringerBrief not only lays out the theoretical foundations for how to handle the unique aspects of cyber attribution – and how to update models used for this purpose – but it also describes a series of empirical results, as well as compares results of specially-designed frameworks for cyber attribution to standard machine learning approaches.  Cyber attribution is not only a challenging problem, but there are also problems in performing such research, particularly in obtaining relevant data. This SpringerBrief describes how to use capture-the-flag for such research, and describes issues from organizing such data to running your own capture-the-flag specifically designed for cyber attribution. Datasets and software are also available on the companion website. ER -