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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.

Artificial intelligence. --- Computer security. --- Computer science. --- Computer Science. --- Artificial Intelligence (incl. Robotics). --- Security. --- AI (Artificial intelligence) --- Artificial thinking --- Electronic brains --- Intellectronics --- Intelligence, Artificial --- Intelligent machines --- Machine intelligence --- Thinking, Artificial --- Bionics --- Cognitive science --- Digital computer simulation --- Electronic data processing --- Logic machines --- Machine theory --- Self-organizing systems --- Simulation methods --- Fifth generation computers --- Neural computers --- Computer privacy --- Computer system security --- Computer systems --- Computers --- Cyber security --- Cybersecurity --- Electronic digital computers --- Protection of computer systems --- Security of computer systems --- Data protection --- Security systems --- Hacking --- Protection --- Security measures --- Data protection. --- Artificial Intelligence. --- Data governance --- Data regulation --- Personal data protection --- Protection, Data

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As cyber threats become ever more frequent, a proactive defense that shifts attention from the defender to the attacker environment is key to designing better attack prediction systems. This book offers models to analyze threat intelligence mined from malicious hacker communities, providing insight into the heart of the underground cyber world.

Cyber intelligence (Computer security) --- Cyberterrorism --- Hacking --- Data mining. --- Social sciences --- Hackers --- Prevention. --- Network analysis. --- Social networks. --- Computer hackers --- Computer programmers --- Computer users --- Network analysis (Social sciences) --- SNA (Social network analysis) --- Social network analysis --- System analysis --- Algorithmic knowledge discovery --- Factual data analysis --- KDD (Information retrieval) --- Knowledge discovery in data --- Knowledge discovery in databases --- Mining, Data --- Database searching --- Computer hacking --- Computer programming --- Computer security --- Attacks on computers --- Computer attacks --- Cyber attacks --- Cyber terrorism --- Cyber war --- Cyberwarfare --- Electronic terrorism (Cyberterrorism) --- Computer crimes --- Terrorism --- Cyber spying --- Cyberintelligence (Computer security) --- Cyberspying --- Intelligence, Cyber (Computer security) --- Methodology

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Criminology. Victimology --- Computer architecture. Operating systems