PPT-SurgeProtector : Mitigating Temporal Algorithmic Complexity Attacks on NFs Using Adversarial

Nirav Atre Hugo Sadok Erica Chiang Weina Wang Justine Sherry Network Functions NFs NFs are key components of the Internet infrastructure Firewalls loadbalancers intrusion prevention systems IPS . Shantanu. . Dutt. ECE Dept.. UIC. Time Complexity. An . algorithm’s . time complexity is a function T(n) of problem size n that represents how much time the algorithm will take to complete its task.. Upon completion of this module, you should be able to:. Export a file system to UNIX . Describe exporting file systems to ESXi. Exporting NFS File Systems to UNIX/ESXi. 1. Exporting NFS File Systems to UNIX/ESXi. etc. Convnets. (optimize weights to predict bus). bus. Convnets. (optimize input to predict ostrich). ostrich. Work on Adversarial examples by . Goodfellow. et al. , . Szegedy. et. al., etc.. Generative Adversarial Networks (GAN) [. Statistical Relational AI. Daniel Lowd. University of Oregon. Outline. Why do we need adversarial modeling?. Because of the dream of AI. Because of current reality. Because of possible dangers. Our initial approach and results. NAS-Royal Society . Sackler. Forum , The Frontiers of Machine Learning. Washington DC, 31 Jan-2 February 2017. Professor Karen Yeung. Director, Centre for Technology, Ethics, Law & Society (TELOS). PRESENTED BY. ALI NASIR BITF13M040. AMMAR HAIDER BITF13M016. SHOIAB BAJWA BITF13M040. AKHTAR YOUNAS BITF13M019. FTP Is a popular File Sharing Protocol. Widely popular on All Operating . Systems. FTP Service is the basic need for everyone who wants to share their Files over internet. Using FTP, Users can Download Upload their files to the server and thus File Sharing becomes very easy. Nets. İlke Çuğu 1881739. NIPS 2014 . Ian. . Goodfellow. et al.. At a . glance. (. http://www.kdnuggets.com/2017/01/generative-adversarial-networks-hot-topic-machine-learning.html. ). Idea. . Behind. —An Introduction. Binghui. Wang, Computer Engineering. Supervisor: Neil . Zhenqiang. Gong. 01/13/2017. Outline. Machine Learning (ML) . Adversarial . ML. Attack . Taxonomy. Capability. Adversarial Training . Holography. Or . How I learned to stop worrying and love the analytic gradient. . Wesley E. Farriss and James R. . Fienup. Clrc. . 2018. Okinawa, Japan. Shankar Raman. Balaji Venkat. Gaurav Raina. Outline. MPLS VPN Security Issues. Router Configuration. Secure Control Plane Exchange. Label hopping applied to data plane. Tic-Toc based Scheme. Control plane algorithms for PEne. Lijie. Chen. MIT. Today’s Topic. Background. . What is Fine-Grained Complexity?. The Methodology of Fine-Grained Complexity. Frontier: Fine-Grained Hardness for Approximation Problems. The Connection. Presenter: Syed Sharjeelullah. Course: CS-732. Authors: Jefferson L. P. Lima. David Macedo. . Cleber. . Zanchettin. Dr. Alex Vakanski. Lecture 1. Introduction to Adversarial Machine Learning. . Lecture Outline. Machine Learning (ML). Adversarial ML (AML). Adversarial examples. Attack taxonomy. Common adversarial attacks. Dr. Alex Vakanski. Lecture . 10. AML in . Cybersecurity – Part I:. Malware Detection and Classification. . Lecture Outline. Machine Learning in cybersecurity. Adversarial Machine Learning in cybersecurity.