PPT-Intrusion Detection using Deep Neural Networks

Milad Ghaznavi 1 Outline Introduction Dataset Multi Layer Perceptron Convolutional Neural Network Evaluation Related Work Conclusion 2 Introduction Intrusion Detection Background 3 Intrusion Detection. INTRO TO INTRUSION ALARM. INTRUSION ALARM TECHNOLOGY. An intrusion detection system consists of several different system components wired together to provide protection of persons and property.. INTRUSION ALARM TECHNOLOGY. Intruders. Classes (from [ANDE80]:. two most publicized threats to security are malware and intruders. generally referred to as a . hacker. or . cracker. Examples of Intrusion. remote root compromise. 1. Recurrent Networks. Some problems require previous history/context in order to be able to give proper output (speech recognition, stock forecasting, target tracking, etc.. One way to do that is to just provide all the necessary context in one "snap-shot" and use standard learning. Brains and games. Introduction. Spiking Neural Networks are a variation of traditional NNs that attempt to increase the realism of the simulations done. They more closely resemble the way brains actually operate. Deep Learning @ . UvA. UVA Deep Learning COURSE - Efstratios Gavves & Max Welling. LEARNING WITH NEURAL NETWORKS . - . PAGE . 1. Machine Learning Paradigm for Neural Networks. The Backpropagation algorithm for learning with a neural network. Deep Neural Networks . Huan Sun. Dept. of Computer Science, UCSB. March 12. th. , 2012. Major Area Examination. Committee. Prof. . Xifeng. . Yan. Prof. . Linda . Petzold. Prof. . Ambuj. Singh. Dongwoo Lee. University of Illinois at Chicago . CSUN (Complex and Sustainable Urban Networks Laboratory). Contents. Concept. Data . Methodologies. Analytical Process. Results. Limitations and Conclusion. Christopher Markley, PhD. US Nuclear Regulatory Commission. National Academy of Sciences: Recommendations for Human Intrusion Standards. Not possible to make scientifically supportable predictions of the probability of human intrusion.. Introduction 2. Mike . Mozer. Department of Computer Science and. Institute of Cognitive Science. University of Colorado at Boulder. Hinton’s Brief History of Machine Learning. What was hot in 1987?. Ali Cole. Charly. . Mccown. Madison . Kutchey. Xavier . henes. Definition. A directed network based on the structure of connections within an organism's brain. Many inputs and only a couple outputs. What is an IDS?. An . I. ntrusion . D. etection System is a wall of defense to confront the attacks of computer systems on the internet. . The main assumption of the IDS is that the behavior of intruders is different from legal users.. . 循环神经网络. Neural Networks. Recurrent Neural Networks. Humans don’t start their thinking from scratch every second. As you read this essay, you understand each word based on your understanding of previous words. You don’t throw everything away and start thinking from scratch again. Your thoughts have persistence.. Developing efficient deep neural networks. Forrest Iandola. 1. , Albert Shaw. 2. , Ravi Krishna. 3. , Kurt Keutzer. 4. 1. UC Berkeley → DeepScale → Tesla → Independent Researcher. 2. Georgia Tech → DeepScale → Tesla. CS 469: Security Engineering. These slides are modified with permission from Bill Young (. Univ. of Texas). Coming up: Intrusion Detection. 1. Intrusion . Detection. An . intrusion detection system .