PPT-Keystroke Recognition using

WiFi Signals Alex Liu Wei Wang Muhammad Shahzad Kamran Ali Dept of Computer Science amp Engineering Michigan State University Keystroke Recognition Kamran Ali. Authentication on Long-Text Input. . Summary of eight years of research in this area. Charles Tappert. Seidenberg School of CSIS, Pace University. DPS+PhD. . Biometric Dissertations. Completed. Keystroke Biometric (long text input). Keystroke Biometric Intrusion Detection. Ned Bakelman. Advisor: Dr. Charles Tappert. Research Problem Statement. Using the keystroke biometric, how quickly and how accurately can we detect an intruder’s unauthorized use of another person’s computer?. 2015 . GenCyber. Cybersecurity Workshop. Review of Friday & Monday Sessions. Dr. Charles C. Tappert. Seidenberg School of CSIS, Pace University. http://csis.pace.edu/~ctappert. /. . What . is . n n n n 102-EN—(1013) 1. RECIPIENT OF RECOGNITION Transfer Recognition Points to:Name: Recipient ID Number: Club Name: Address: City: State/Province: Country: ostal Code: Daytime Phone: Gonzalo . Perez. J. Vinnie Monaco. Advisor – Dr. Charles Tappert. Friday, May 1, 2017. Keystroke Biometric Rationale. According to Roy Maxion a research professor of computer science at Carnegie Mellon, “Motions that are performed numerous times, are governed by motor control, not deliberate thought. That is why successfully mimicking keystroke dynamics is physiologically . Keystroke Biometric Intrusion Detection. Ned Bakelman. Advisor: Dr. Charles Tappert. Research Problem Statement. Using the keystroke biometric, how quickly and how accurately can we detect an intruder’s unauthorized use of another person’s computer?. WiFi. signals . Song . Fang. Content. Motivation. Keystroke eavesdropping models. Technical challenges. Research directions. Summary . . 2. 3. Bad. Good. Keystroke Eavesdropping. Virtual Keyboards. Identification Studies. on . Long-Text Input. Mary . Villani. DPS 2006. Fall . 2008. Objective. . For long-text input of 600 keystrokes. Determine the viability of the keystroke biometric – two independent variables. Linda Shapiro. CSE 455. 1. Face recognition: once you’ve detected and cropped a face, try to recognize it. Detection. Recognition. “Sally”. 2. Face recognition: overview. Typical scenario: few examples per face, identify or verify test example. Tanya . Lubicz-Nawrocka. , Academic Engagement Coordinator. Megan Brown, Schools Engagement Officer. @. eusa. @. TanyaLubiczNaw. @Meg2902. Training. Blog post for Open Badge. HEAR recognition. 2. Question to Consider. What are the key challenges police officers face when dealing with persons in behavioral crisis?. 3. Recognizing a. Person in Crisis. Crisis Recognition. 4. Behavioral Crisis: A Definition. 2. Question to Consider. What are the key challenges police officers face when dealing with persons in behavioral crisis?. 3. Recognizing a. Person in Crisis. Crisis Recognition. 4. Behavioral Crisis: A Definition. Abishek Ramasubramanian & . Aayush. Shah. Introduction. Smartphones and tablets are commonly used for . performing privacy . sensitive transactions of banking, payment, . and . social . applications. Linda Shapiro. CSE 455. 1. Face recognition: once you’ve detected and cropped a face, try to recognize it. Detection. Recognition. “Sally”. 2. Face recognition: overview. Typical scenario: few examples per face, identify or verify test example.