Index. Index 2D-PCA 222
|
|
- Herbert Allison
- 5 years ago
- Views:
Transcription
1 274 Index Index 2D-PCA 222 A abrupt change detection 96 Adaptive Resonance Theory (ART) 48, 143, 223, 239 Ad-Hoc Network 92 Anomaly-based Network Intrusion Detection System (A-NIDS) 94-95, 97, 102, 104, 109, 111, 117 anomaly detection 8, 13, 30, 40-41, 43-46, 49, 70-71, 75, 96-97, , , 140, 143, 153, , 187, , 207, 209, , , 238, 240 Anomaly Level Exposure 107, 121 Application Level Network (ALN) 78, 81, 93 application logging 3, 27 Artificial Intelligence (AI) 30, 96, 120, , 192, 218 Artificial Neural Network (ANN) , 223 Audit Data 2, 24, 29, 32, 36, 96, 192 Auto-Reclosing 40, 42, 49, 51, 54, 61, 68, 71, 75 B Back Orifice software 49 Back Propagation (BP) 223 Bayes Decision Rule 46 Bayesian Belief Networks 48, 69 Bayesian methodology 172 behavioral aliasing 45 Blaster.worm 113 Botnets Broadcast Algorithms broadcast service buffer overflow attacks 47, 197 Byzantine agreement protocol 47 C Centralized intrusion detection system 43 Cisco Systems Net Ranger 43 Classification 2, 23, 25, 36-37, 45-46, 71-72, , , 125, 137, 140, 143, 148, 158, 173, 189, 193, 201, , , , 230, 234, 240 Client-Server Model 81, 93 clustering analysis 138, collection strategy collection structure 15 Command and Control (C&C) Server 138, 142, 144 Common Criteria 7, 27 Common Intrusion Detection Framework (CIDF) 34, 44 Competitive Learning Network (CLN) 179, Computer Crime and Security Surveys 95, 118 computer monitoring 3, 8 Computer Security Institute (CSI) 95, 118 correlation matrix
2 COTS-products 3 CPU cycles 22 crossover error rate (CER) 58, 61, 65 Curse of Dimensionality 169, 171, 173, 194 CyberSafe Centrax 43 D Danger Theory 47 data collection 1-9, 13-25, 28-29, 32, 34-37, 42 data collection infrastructure 15, 22 debug registers 14 Defense Advanced Projects Agency (DARPA) 35, 104, 119, 169, 171, , , , 197, 220, , 238, 240 demilitarized zone (DMZ) 101 denial of service (DoS) 41-42, 44-45, 49-51, 75-76, 117, 123, 129, , 148, 150, 153, 158, 164, 166, 180, 197 desktop modem 95 Detection Appliance 43 detection engine 5, 14, 44, 145 detection policy 5, 12 Detection Rate (DR) 3, , 170, , , 208, dimensionality reduction , 176, , 181, dimension reduction 169, 171, 174, 178, 181, 184, 186, , 223, 229 distance-based function 212 distributed denial of service attacks (DDoS) 41-42, 49, 51, 68, 75, 103, , 166 distributed hash tables (DHTs) 77, 80-82, 85, 92 Domain Name System (DNS) , 137, 140 dynamic applications 3 Dynamic patching E eigenvector , embedded systems 25 end-to-end delay 54, 56, 58-61, entropy , , , 115, , 192 Euclidean distance evasion methods 45 Execution Time (ET) 2-3, 6, 8, 12-13, 15, 23-25, 41, 96, 98, 102, 104, 125, 146, 153, 159, , 175, , , , , 213, exposure of anomaly 106 Exposure Threshold 96, 101, , 112, , 121 F false negative rate (FNR) 58, 61, 64-65, 135, 137 False Positive (FP) 42, 45, 52, 58, 61, 64, 68, 122, , , , 193, 195, 201, , 210, 212, , 219 false positive rate (FPR) 58, 61, 64-65, 135, 137, 210, 212, Feature Space 169, 171, 173, 175, 182, 188, 190, 222, financial fraud 45 firewall 41, 50-51, 73, 79, 95, 101, 165 Fixed SST Subspaces (FS) 195, 197, 200 flooding attacks 146, , fuzzy logic 96, 172, 191 G general purpose registers 14 Genetic Algorithm (GA) 70, 172, 191, 217, 219, 223, 238 goal-oriented logging 8, 32 granularity of log trigger 9, 12, 37 H Hash Function 93 helper library 17-18, 21 Hidden Markov Model (HMM) 70, high speed networks 96 histogram 15,
3 Honeypot 45, 48, 69, 72, 125 host based intrusion detection system (HIDS) 43, 47-48, 101 I impossible path execution (IPE) Improved Competitive Learning Network (ICLN) 179, 183, 185 Incident Response Support System (IRSS) 46, 69 Independent Component Analysis (ICA) 191, 222, 239 Information Security Management System (ISMS) 47 information value 169, , 180, 187 insider attack 31, 95 Institute of Standards and Technology 29, 146, 191 internet banking security 45 Internet Engineering Task Force (IETF) 79, 91, 146, 165 Internet Security Systems Real Secure 43 interpreter insertion Inter-Quartile Range (IQR) 103 intrusion detection and intrusion prevention (ID/IP) 1-5, 8, 13, 15-16, 20, 23-25, 29-31, 33-36, 40-41, 43-50, 61, 68-85, 89-92, 94, 96-97, , , , , , , , 134, , 140, , , 156, , , , 180, , 212, , , 229, 231, Intrusion Detection and Response System (IDRS) 170 Intrusion Detection Message Exchange Format (IDMEF) 79 Intrusion Detection Systems (IDS) 2-5, 7, 12, 15, 17, 19-20, 22-24, 29, 33-36, 40-46, 48-58, 61, 63-68, 75, 78, 81, 92, 101, 104, , 156, 158, 164, 166, , 173, 223, 229, 235, 238, 240 Intrusion Detection Working Group (IDWG) 79, 91 Intrusion Prevention Systems (IPS) 40-42, 44-48, 69, 71 in-vehicle network 25 inverse document frequency (idf) 203 IP Multimedia Subsystem (IMS) 49, 73, 164 Ipsweep 113, 115 J JXTA framework 81 K Kademlia network 81, Kadsim K-Bucket 82, 93 KDD-CUP , 207, 209, 211, 214 Kernel density function keystroke monitoring 41 Key-Value Pair 93 K-Nearest Neighbor (KNN) , , , 236, 238, 241 Komondor test network 89 L learning by example paradigms 96 limited scalability 42, 67 Local Area Network (LAN) 44-45, 55, 73, 94-97, 103, 111, 118, 120, 137, 155, 170 location relative environment 15, 17 location relative monitored asset 15, 20 log control 6, 9, log record 4, 7, 36 log trigger 5-6, 9-10, 12-13, 18, 21, 37 long-time traffic slot (LTTS) , , 121 M malign traffic 95, 110 malware 35, 92, 125, , 132, 137, 139, Mass Spectral Imaging (MSI)
4 McAfee Entercept 46 Method of Remaining Elements (MRE) 94, 96, , 111, , 120 mission-critical applications 95 misuse detection 2, 32, 153 MIT-DARPA dataset 95, 97, 118 mobile ad hoc network (MANET) 44, 71 mobile nodes 45 mobile telecommunications 46 modify compiler 9-10 modify linker 9-10 Modular Weighted PCA (MWPCA) 222 monitoring policy 10 multicast tree 86, 88 Multi Layer Perceptron (MLP) 221, 223 multi-resolution techniques 40 N Neptune 113, 115 network based intrusion detection system (NIDS) 43-45, 77-78, 94-95, , 112, 117, , 159 Network Flight Recorder Intrusion 43 Network Ice Black Ice Defender 43 network probes 96 Network Security 41, 43-44, 68, 70, 73, 78, , 140, 164, 166, , 191, 194, 221, 237 Network Security Wizard Dragon IDS 43 neural networks 41, 44-45, 70-71, 96, 144, 172, , 223, 237, 240 Next Generation Networks (NGN) 145, 166 Nmap 113, 115, 129 Node IDentifier (NodeID) 80-81, 83, 87 Number of Dimensions (ND) 28, 30, 32-35, 71-72, , 164, 169, 171, 176, 179, , , 192, 214, 237, 239 O Open Software 48 Open Source Security Information Management (OSSIM) 47 operating systems (OSs) 3, 14, 17-19, 21-23, 26-27, 31-33, 35, 47, 81, , 201, 203, 208, 211 Optimized Network Engineering Tools (OPNET) 52, 67 OS interface 17-18, 21 OS kernel 17-19, Outlying Subspace Front (OSF) , , 208, 211 output device 4, 6 Overlay Network 77-78, 81, 91, 93 P packet losses 83, partial keyword searches 85 Peer-to-Peer (P2P) Networks 77-78, 80-81, 85, 91, 93, 123, 125, 139, 142, 144 performance counters 14, 19 perl 112 Ping of Death 50, 113, 116 PortScan 104, 111, 113, 115 Portsweep 113, 115 predictive pattern generation 41 Preventive Information Security Management (PrISM) 47 Principal Component Analysis (PCA) 171, , 182, , 226, privileged execution 19 probes 49, 79, 96, 104, 113 processing overhead 10 processing time 171, 173, 178, 182 PROMIS system 81 Proportional Uncertainty (PU) 94-95, , , 108, 110, 115, 121 Q Quality of Service (QoS) 48, 143, 145 R Radial Basis Function Neural Network (RBFNN) 223, 237 Receiver Operating Characteristic (ROC) 161, ,
5 replication 83-84, 87-88, 91, 93 rewrite executable 9-10 rootkits 19 routing protocols 44 runtime compilation S SANS consensus project 3, 32 Sasser worm 113 secure architecture and fault-resilient engine (SAFE) 47 Security Information and Event Management Systems (SIEM) 3 security log 3, 29, 36 Security Operation Center (SOC) 151, 163 Self Organizing features Map (SOM) 173, 179, 183, , 191 sequence matching 41 Service Delivery Platform (SDP) 49 Session Initiation Protocol (SIP) 45, 48, 72, , , Shannon s uncertainty measure 97 short-time traffic slot (STTS) , , 121 signal processing techniques 96 Signature Based Detection 43, 76 Signature-based NIDS (S-NIDS) 95, 102, 117 similarity metrics 46 Singular Value Decomposition (SVD) 173 SIP-based security architecture 151, 164 SIP security 153, , SIP Security Engine Evaluation 159 SMTP server 82 Smurf 50, , 223, 239 snare 19, 29 Snort 43, 45, 47-48, 78, 92, 95, 102, 119, Spamwatch 81 Sparse Subspace Template (SST) , 200 SQL Injection Attacks (SQLIAs) 46, 71 state of lockout 61 state transition analysis 41 Storage Area Networks (SAN) 26, 31-33, 46, 138, 141 storage mechanisms 14 Stream Projected Outlier detector (SPOT) 193, , 205, , 212, Supervised SST Subspaces (SS) 195, Support Vector Machine (SVM) 71, 173, 223 Symantec Net Prowler 43 SYN-flood attacks 41-42, 49-51, 68, T TCP/IP packets 20, 50, 61, 85, 111, , , 144, 180, 196 term frequency (tf) 202 ternary content addressable memory (TCAM) 47 Threat Modeling 8 traffic profiling 94, 96, 117 transaction user (TU) 144, 146, 167 Transport Control Protocol (TCP) 20, 50, 61, 85, 111, , , 144, 180, 196 Tripwire 43 Trojans 170 U UDP packets 85 unitary cardinality , , UNIX 10, 13, 20, 26-28, 30-31, 33, 129 unstable network Unsupervised SST Subspaces (US) 68, 142, 146, 195, 197, 200 User Agent Clients (UACs) 155, 167 User Agent Servers (UASs) 155, 167 V virtual machines 128, 130 Voice over IP (VoIP) 45, 48, 72, 145, 151, 164,
6 W web connected applications 3 Weighted PCA (WPCA) 220 weights matrix Welchia.worm 113 white data 175, 190 whiteness property 175 Wide Area Network (WAN) 45, 170 worms 44-45, 51, 80, 89, 97, 103, 111, 113, 120, 143, 170 X XOR metric 82,
CSE 565 Computer Security Fall 2018
CSE 565 Computer Security Fall 2018 Lecture 19: Intrusion Detection Department of Computer Science and Engineering University at Buffalo 1 Lecture Outline Intruders Intrusion detection host-based network-based
More informationCIH
mitigating at host level, 23 25 at network level, 25 26 Morris worm, characteristics of, 18 Nimda worm, characteristics of, 20 22 replacement login, example of, 17 signatures. See signatures SQL Slammer
More informationAnomaly Detection in Communication Networks
Anomaly Detection in Communication Networks Prof. D. J. Parish High Speed networks Group Department of Electronic and Electrical Engineering D.J.Parish@lboro.ac.uk Loughborough University Overview u u
More informationFlow-based Anomaly Intrusion Detection System Using Neural Network
Flow-based Anomaly Intrusion Detection System Using Neural Network tational power to analyze only the basic characteristics of network flow, so as to Intrusion Detection systems (KBIDES) classify the data
More informationCISSP CEH PKI SECURITY + CEHv9: Certified Ethical Hacker. Upcoming Dates. Course Description. Course Outline
CISSP CEH PKI SECURITY + CEHv9: Certified Ethical Hacker Learn to find security vulnerabilities before the bad guys do! The Certified Ethical Hacker (CEH) class immerses students in an interactive environment
More informationIDS: Signature Detection
IDS: Signature Detection Idea: What is bad, is known What is not bad, is good Determines whether a sequence of instructions being executed is known to violate the site security policy Signatures: Descriptions
More informationIntrusion Detection by Combining and Clustering Diverse Monitor Data
Intrusion Detection by Combining and Clustering Diverse Monitor Data TSS/ACC Seminar April 5, 26 Atul Bohara and Uttam Thakore PI: Bill Sanders Outline Motivation Overview of the approach Feature extraction
More informationIntrusion Detection. Comp Sci 3600 Security. Introduction. Analysis. Host-based. Network-based. Distributed or hybrid. ID data standards.
or Detection Comp Sci 3600 Security Outline or 1 2 3 4 5 or 6 7 8 Classes of or Individuals or members of an organized crime group with a goal of financial reward Their activities may include: Identity
More informationIntrusion Detection System For Denial Of Service Flooding Attacks In Sip Communication Networks
Intrusion Detection System For Denial Of Service Flooding Attacks In Sip Communication Networks So we are proposing a network intrusion detection system (IDS) which uses a Keywords: DDoS (Distributed Denial
More informationApplied IT Security. System Security. Dr. Stephan Spitz 6 Firewalls & IDS. Applied IT Security, Dr.
Applied IT Security System Security Dr. Stephan Spitz Stephan.Spitz@de.gi-de.com Overview & Basics System Security Network Protocols and the Internet Operating Systems and Applications Operating System
More informationPROTECTING INFORMATION ASSETS NETWORK SECURITY
PROTECTING INFORMATION ASSETS NETWORK SECURITY PAUL SMITH 20 years of IT experience (desktop, servers, networks, firewalls.) 17 years of engineering in enterprise scaled networks 10+ years in Network Security
More informationANOMALY DETECTION IN COMMUNICTION NETWORKS
Anomaly Detection Summer School Lecture 2014 ANOMALY DETECTION IN COMMUNICTION NETWORKS Prof. D.J.Parish and Francisco Aparicio-Navarro Loughborough University (School of Electronic, Electrical and Systems
More informationACS / Computer Security And Privacy. Fall 2018 Mid-Term Review
ACS-3921-001/4921-001 Computer Security And Privacy Fall 2018 Mid-Term Review ACS-3921/4921-001 Slides Used In The Course A note on the use of these slides: These slides has been adopted and/or modified
More informationRadware Attack Mitigation Solution (AMS) Protect Online Businesses and Data Centers Against Emerging Application & Network Threats - Whitepaper
Radware Attack Mitigation Solution (AMS) Protect Online Businesses and Data Centers Against Emerging Application & Network Threats - Whitepaper Table of Contents Abstract...3 Understanding Online Business
More informationIntrusion Detection System (IDS) IT443 Network Security Administration Slides courtesy of Bo Sheng
Intrusion Detection System (IDS) IT443 Network Security Administration Slides courtesy of Bo Sheng 1 Internet Security Mechanisms Prevent: Firewall, IPsec, SSL Detect: Intrusion Detection Survive/ Response:
More informationIntrusion Detection System with FGA and MLP Algorithm
Intrusion Detection System with FGA and MLP Algorithm International Journal of Engineering Research & Technology (IJERT) Miss. Madhuri R. Yadav Department Of Computer Engineering Siddhant College Of Engineering,
More informationConfiguring Access Rules
Configuring Access Rules Rules > Access Rules About Access Rules Displaying Access Rules Specifying Maximum Zone-to-Zone Access Rules Changing Priority of a Rule Adding Access Rules Editing an Access Rule
More informationETHICAL HACKING & COMPUTER FORENSIC SECURITY
ETHICAL HACKING & COMPUTER FORENSIC SECURITY Course Description From forensic computing to network security, the course covers a wide range of subjects. You will learn about web hacking, password cracking,
More informationBasic Concepts in Intrusion Detection
Technology Technical Information Services Security Engineering Roma, L Università Roma Tor Vergata, 23 Aprile 2007 Basic Concepts in Intrusion Detection JOVAN GOLIĆ Outline 2 Introduction Classification
More informationIntrusion Detection System using AI and Machine Learning Algorithm
Intrusion Detection System using AI and Machine Learning Algorithm Syam Akhil Repalle 1, Venkata Ratnam Kolluru 2 1 Student, Department of Electronics and Communication Engineering, Koneru Lakshmaiah Educational
More informationBehavior-Based IDS: StealthWatch Overview and Deployment Methodology
Behavior-Based IDS: Overview and Deployment Methodology Lancope 3155 Royal Drive, Building 100 Alpharetta, Georgia 30022 Phone: 770.225.6500 Fax: 770.225.6501 www.lancope.com techinfo@lancope.com Overview
More informationIntrusion Detection Systems
Intrusion Detection Systems Dr. Ahmad Almulhem Computer Engineering Department, KFUPM Spring 2008 Ahmad Almulhem - Network Security Engineering - 2008 1 / 15 Outline 1 Introduction Overview History 2 Types
More informationReview on Data Mining Techniques for Intrusion Detection System
Review on Data Mining Techniques for Intrusion Detection System Sandeep D 1, M. S. Chaudhari 2 Research Scholar, Dept. of Computer Science, P.B.C.E, Nagpur, India 1 HoD, Dept. of Computer Science, P.B.C.E,
More informationTestOut Network Pro - English 4.1.x COURSE OUTLINE. Modified
TestOut Network Pro - English 4.1.x COURSE OUTLINE Modified 2017-07-06 TestOut Network Pro Outline - English 4.1.x Videos: 141 (18:42:14) Demonstrations: 81 (10:38:59) Simulations: 92 Fact Sheets: 145
More informationA study on fuzzy intrusion detection
A study on fuzzy intrusion detection J.T. Yao S.L. Zhao L. V. Saxton Department of Computer Science University of Regina Regina, Saskatchewan, Canada S4S 0A2 E-mail: [jtyao,zhao200s,saxton]@cs.uregina.ca
More informationEthical Hacking and Prevention
Ethical Hacking and Prevention This course is mapped to the popular Ethical Hacking and Prevention Certification Exam from US-Council. This course is meant for those professionals who are looking for comprehensive
More informationAURA ACADEMY Training With Expertised Faculty Call Us On For Free Demo
ETHICAL HACKING (CEH) CURRICULUM Introduction to Ethical Hacking What is Hacking? Who is a Hacker? Skills of a Hacker? Types of Hackers? What are the Ethics and Legality?? Who are at the risk of Hacking
More informationGladiator Incident Alert
Gladiator Incident Alert Allen Eaves Sabastian Fazzino FINANCIAL PERFORMANCE RETAIL DELIVERY IMAGING PAYMENT SOLUTIONS INFORMATION SECURITY & RISK MANAGEMENT ONLINE & MOBILE 1 2016 Jack Henry & Associates,
More informationEmerging Threat Intelligence using IDS/IPS. Chris Arman Kiloyan
Emerging Threat Intelligence using IDS/IPS Chris Arman Kiloyan Who Am I? Chris AUA Graduate (CS) Thesis : Cyber Deception Automation and Threat Intelligence Evaluation Using IDS Integration with Next-Gen
More informationWeb Security. Outline
Security CS 161/194-1 Anthony D. Joseph November 21, 2005 s Outline Static and Dynamic Content Firewall review Adding a DMZ Secure Topologies 2 1 Polls How many people have set up a personal web server?
More informationCS System Security 2nd-Half Semester Review
CS 356 - System Security 2nd-Half Semester Review Fall 2013 Final Exam Wednesday, 2 PM to 4 PM you may bring one 8-1/2 x 11 sheet of paper with any notes you would like no cellphones, calculators This
More informationContents. Preface to the Second Edition
Preface to the Second Edition v 1 Introduction 1 1.1 What Is Data Mining?....................... 4 1.2 Motivating Challenges....................... 5 1.3 The Origins of Data Mining....................
More informationLecture 12. Application Layer. Application Layer 1
Lecture 12 Application Layer Application Layer 1 Agenda The Application Layer (continue) Web and HTTP HTTP Cookies Web Caches Simple Introduction to Network Security Various actions by network attackers
More informationChapter 9. Firewalls
Chapter 9 Firewalls The Need For Firewalls Internet connectivity is essential Effective means of protecting LANs Inserted between the premises network and the Internet to establish a controlled link however
More informationComputer Security: Principles and Practice
Computer Security: Principles and Practice Chapter 6 Intrusion Detection First Edition by William Stallings and Lawrie Brown Lecture slides by Lawrie Brown Intruders significant issue hostile/unwanted
More informationTestOut Network Pro - English 5.0.x COURSE OUTLINE. Modified
TestOut Network Pro - English 5.0.x COURSE OUTLINE Modified 2018-03-06 TestOut Network Pro Outline - English 5.0.x Videos: 130 (17:10:31) Demonstrations: 78 (8:46:15) Simulations: 88 Fact Sheets: 136 Exams:
More informationOSSIM Fast Guide
----------------- OSSIM Fast Guide ----------------- February 8, 2004 Julio Casal http://www.ossim.net WHAT IS OSSIM? In three phrases: - VERIFICATION may be OSSIM s most valuable contribution
More informationSANS SEC504. Hacker Tools, Techniques, Exploits and Incident Handling.
SANS SEC504 Hacker Tools, Techniques, Exploits and Incident Handling http://killexams.com/exam-detail/sec504 QUESTION: 315 Which of the following techniques can be used to map 'open' or 'pass through'
More informationUnderstanding Cisco Cybersecurity Fundamentals
210-250 Understanding Cisco Cybersecurity Fundamentals NWExam.com SUCCESS GUIDE TO CISCO CERTIFICATION Exam Summary Syllabus Questions Table of Contents Introduction to 210-250 Exam on Understanding Cisco
More informationOur Narrow Focus Computer Networking Security Vulnerabilities. Outline Part II
Our Narrow Focus 15-441 15-441 Computer Networking 15-641 Lecture 22 Security: DOS Peter Steenkiste Fall 2016 www.cs.cmu.edu/~prs/15-441-f16 Yes: Creating a secure channel for communication (Part I) Protecting
More informationSun Mgt Bonus Lab 2: Zone and DoS Protection on Palo Alto Networks Firewalls 1
Sun Mgt Bonus Lab 2: Zone and DoS Protection on Palo Alto Networks Firewalls 1 Overview Denial of Service (DoS) and Distributed Denial of Service (DDoS) types of attack are attempts to disrupt network
More informationCombination of Three Machine Learning Algorithms for Intrusion Detection Systems in Computer Networks
Vol. () December, pp. 9-8 ISSN95-9X Combination of Three Machine Learning Algorithms for Intrusion Detection Systems in Computer Networks Ali Reza Zebarjad, Mohmmad Mehdi Lotfinejad Dapartment of Computer,
More informationBayesian Learning Networks Approach to Cybercrime Detection
Bayesian Learning Networks Approach to Cybercrime Detection N S ABOUZAKHAR, A GANI and G MANSON The Centre for Mobile Communications Research (C4MCR), University of Sheffield, Sheffield Regent Court, 211
More informationIntrusion Detection Systems (IDS)
Intrusion Detection Systems (IDS) Presented by Erland Jonsson Department of Computer Science and Engineering Intruders & Attacks Cyber criminals Activists State-sponsored organizations Advanced Persistent
More informationGraph-based Detection of Anomalous Network Traffic
Graph-based Detection of Anomalous Network Traffic Do Quoc Le Supervisor: Prof. James Won-Ki Hong Distributed Processing & Network Management Lab Division of IT Convergence Engineering POSTECH, Korea lequocdo@postech.ac.kr
More informationAn Anomaly-Based Intrusion Detection System for the Smart Grid Based on CART Decision Tree
An Anomaly-Based Intrusion Detection System for the Smart Grid Based on CART Decision Tree P. Radoglou-Grammatikis and P. Sarigiannidis* University of Western Macedonia Department of Informatics & Telecommunications
More informationDetecting Specific Threats
The following topics explain how to use preprocessors in a network analysis policy to detect specific threats: Introduction to Specific Threat Detection, page 1 Back Orifice Detection, page 1 Portscan
More informationDistributed Denial of Service (DDoS)
Distributed Denial of Service (DDoS) Defending against Flooding-Based DDoS Attacks: A Tutorial Rocky K. C. Chang Presented by Adwait Belsare (adwait@wpi.edu) Suvesh Pratapa (suveshp@wpi.edu) Modified by
More informationAnomaly Intrusion Detection System Using Hierarchical Gaussian Mixture Model
264 IJCSNS International Journal of Computer Science and Network Security, VOL.8 No.8, August 2008 Anomaly Intrusion Detection System Using Hierarchical Gaussian Mixture Model M. Bahrololum and M. Khaleghi
More informationHybrid Network Intrusion Detection for DoS Attacks
I J C T A, 9(26) 2016, pp. 15-22 International Science Press Hybrid Network Intrusion Detection for DoS Attacks K. Pradeep Mohan Kumar 1 and M. Aramuthan 2 ABSTRACT The growing use of computer networks,
More informationIntrusion Detection Systems (IDS)
Intrusion Detection Systems (IDS) Presented by Erland Jonsson Department of Computer Science and Engineering Contents Motivation and basics (Why and what?) IDS types and detection principles Key Data Problems
More informationImproved Detection of Low-Profile Probes and Denial-of-Service Attacks*
Improved Detection of Low-Profile Probes and Denial-of-Service Attacks* William W. Streilein Rob K. Cunningham, Seth E. Webster Workshop on Statistical and Machine Learning Techniques in Computer Intrusion
More information2. INTRUDER DETECTION SYSTEMS
1. INTRODUCTION It is apparent that information technology is the backbone of many organizations, small or big. Since they depend on information technology to drive their business forward, issues regarding
More informationINTRUSION DETECTION WITH TREE-BASED DATA MINING CLASSIFICATION TECHNIQUES BY USING KDD DATASET
INTRUSION DETECTION WITH TREE-BASED DATA MINING CLASSIFICATION TECHNIQUES BY USING KDD DATASET Bilal Ahmad Department of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics,
More informationHierarchical Adaptive FCM To Detect Attacks Using Layered Approach
Hierarchical Adaptive FCM To Detect Attacks Using Layered Approach J.Jensi Edith 1, Dr. A.Chandrasekar 1.Research Scholar,Sathyabama University, Chennai.. Prof, CSE DEPT, St.Joseph s College of Engg.,
More informationCompTIA Network+ Study Guide Table of Contents
CompTIA Network+ Study Guide Table of Contents Course Introduction Table of Contents Getting Started About This Course About CompTIA Certifications Module 1 / Local Area Networks Module 1 / Unit 1 Topologies
More informationUMSSIA INTRUSION DETECTION
UMSSIA INTRUSION DETECTION INTRUSION DETECTION Sensor1 Event1, Event2 Monitor No intrusion M SensorN Event1, Event2 Alarm! IDS CHARACTERISTICS Characteristics an IDS can be classified/evaluated by: Type
More informationUSG2110 Unified Security Gateways
USG2110 Unified Security Gateways The USG2110 series is Huawei's unified security gateway developed to meet the network security needs of various organizations including the small enterprises, branch offices,
More informationConfiguring attack detection and prevention 1
Contents Configuring attack detection and prevention 1 Overview 1 Attacks that the device can prevent 1 Single-packet attacks 1 Scanning attacks 2 Flood attacks 3 TCP fragment attack 4 Login DoS attack
More informationSPIDeR. A Distributed Multi-Agent Intrusion Detection and Response Framework. Patrick Miller
SPIDeR A Distributed Multi-Agent Intrusion Detection and Response Framework Patrick Miller patrick@spider.doriathproject.com Overview Goals Utilize new and existing sensors collaboratively to generate
More informationIntrusion prevention systems are an important part of protecting any organisation from constantly developing threats.
Network IPS Overview Intrusion prevention systems are an important part of protecting any organisation from constantly developing threats. By using protocol recognition, identification, and traffic analysis
More informationACS-3921/ Computer Security And Privacy. Chapter 9 Firewalls and Intrusion Prevention Systems
ACS-3921/4921-001 Computer Security And Privacy Chapter 9 Firewalls and Intrusion Prevention Systems ACS-3921/4921-001 Slides Used In The Course A note on the use of these slides: These slides has been
More informationCompTIA Security+ Malware. Threats and Vulnerabilities Vulnerability Management
CompTIA Security+ Lecture Six Threats and Vulnerabilities Vulnerability Management Copyright 2011 - VTC Malware Malicious code refers to software threats to network and systems, including viruses, Trojan
More informationActivating Intrusion Prevention Service
Activating Intrusion Prevention Service Intrusion Prevention Service Overview Configuring Intrusion Prevention Service Intrusion Prevention Service Overview Intrusion Prevention Service (IPS) delivers
More informationCertified Ethical Hacker (CEH)
Certified Ethical Hacker (CEH) COURSE OVERVIEW: The most effective cybersecurity professionals are able to predict attacks before they happen. Training in Ethical Hacking provides professionals with the
More informationAnalysis of neural networks usage for detection of a new attack in IDS
Annales UMCS Informatica AI X, 1 (2010) 51-59 DOI: 10.2478/v10065-010-0035-7 Analysis of neural networks usage for detection of a new attack in IDS Przemysław Kukiełka 1, Zbigniew Kotulski 2 1 Institute
More informationA Comparative Study of Supervised and Unsupervised Learning Schemes for Intrusion Detection. NIS Research Group Reza Sadoddin, Farnaz Gharibian, and
A Comparative Study of Supervised and Unsupervised Learning Schemes for Intrusion Detection NIS Research Group Reza Sadoddin, Farnaz Gharibian, and Agenda Brief Overview Machine Learning Techniques Clustering/Classification
More informationDeveloping the Sensor Capability in Cyber Security
Developing the Sensor Capability in Cyber Security Tero Kokkonen, Ph.D. +358504385317 tero.kokkonen@jamk.fi JYVSECTEC JYVSECTEC - Jyväskylä Security Technology - is the cyber security research, development
More informationDetection of Network Intrusions with PCA and Probabilistic SOM
Detection of Network Intrusions with PCA and Probabilistic SOM Palakollu Srinivasarao M.Tech, Computer Networks and Information Security, MVGR College Of Engineering, AP, INDIA ---------------------------------------------------------------------***---------------------------------------------------------------------
More informationNetwork Security. Chapter 0. Attacks and Attack Detection
Network Security Chapter 0 Attacks and Attack Detection 1 Attacks and Attack Detection Have you ever been attacked (in the IT security sense)? What kind of attacks do you know? 2 What can happen? Part
More informationIntrusion Detection -- A 20 year practice. Outline. Till Peng Liu School of IST Penn State University
Intrusion Detection -- A 20 year practice Peng Liu School of IST Penn State University Pennsylvania State Unviersity 1 Outline Motivation Intrusion Detection Techniques Intrusion Detection Products Some
More informationIntelligent and Secure Network
Intelligent and Secure Network BIG-IP IP Global Delivery Intelligence v11.2 IP Intelligence Service Brian Boyan - b.boyan@f5.com Tony Ganzer t.ganzer@f5.com 2 Agenda Welcome & Intro Introduce F5 IP Intelligence
More informationCOURSE PROJECT SEM ATTENTION ALL ADVANCED DIPLOMA & BACHELOR STUDENTS
COURSE PROJECT SEM 2 2014 2015 ATTENTION ALL ADVANCED DIPLOMA & BACHELOR STUDENTS The students who have pre registered for Networking Course Project are required to submit your project proposal. The titles
More informationCourse 831 Certified Ethical Hacker v9
Course 831 Certified Ethical Hacker v9 Duration: 5 days What You Get: CEH v9 Certification exam voucher 5 days of high quality classroom training 18 comprehensive modules 40% of class hours dedicated to
More informationSelecting Features for Intrusion Detection: A Feature Relevance Analysis on KDD 99 Intrusion Detection Datasets
Selecting Features for Intrusion Detection: A Feature Relevance Analysis on KDD 99 Intrusion Detection Datasets H. Günes Kayacık, A. Nur Zincir-Heywood, Malcolm I. Heywood Dalhousie University, Faculty
More informationDynamic Datacenter Security Solidex, November 2009
Dynamic Datacenter Security Solidex, November 2009 Deep Security: Securing the New Server Cloud Virtualized Physical Servers in the open Servers virtual and in motion Servers under attack 2 11/9/09 2 Dynamic
More informationOverview of Firewalls. CSC 474 Network Security. Outline. Firewalls. Intrusion Detection System (IDS)
CSC 474 Network Security Topic 8.4 Firewalls and Intrusion Detection Systems (IDS) 1 Outline Firewalls Filtering firewalls Proxy firewalls Intrusion Detection System (IDS) Rule-based IDS Anomaly detection
More informationF5 comprehensive protection against application attacks. Jakub Sumpich Territory Manager Eastern Europe
F5 comprehensive protection against application attacks Jakub Sumpich Territory Manager Eastern Europe j.sumpich@f5.com Evolving Security Threat Landscape cookie tampering Identity Extraction DNS Cache
More informationModular Policy Framework. Class Maps SECTION 4. Advanced Configuration
[ 59 ] Section 4: We have now covered the basic configuration and delved into AAA services on the ASA. In this section, we cover some of the more advanced features of the ASA that break it away from a
More informationCompTIA Exam CAS-002 CompTIA Advanced Security Practitioner (CASP) Version: 6.0 [ Total Questions: 532 ]
s@lm@n CompTIA Exam CAS-002 CompTIA Advanced Security Practitioner (CASP) Version: 6.0 [ Total Questions: 532 ] Topic break down Topic No. of Questions Topic 1: Volume A 117 Topic 2: Volume B 122 Topic
More informationIntruders. significant issue for networked systems is hostile or unwanted access either via network or local can identify classes of intruders:
Intruders significant issue for networked systems is hostile or unwanted access either via network or local can identify classes of intruders: masquerader misfeasor clandestine user varying levels of competence
More informationDenial of Service (DoS) Attack Detection by Using Fuzzy Logic over Network Flows
Denial of Service (DoS) Attack Detection by Using Fuzzy Logic over Network Flows S. Farzaneh Tabatabaei 1, Mazleena Salleh 2, MohammadReza Abbasy 3 and MohammadReza NajafTorkaman 4 Faculty of Computer
More informationThreat Detection and Mitigation for IoT Systems using Self Learning Networks (SLN)
Threat Detection and Mitigation for IoT Systems using Self Learning Networks (SLN) JP Vasseur, PhD - Cisco Fellow jpv@cisco.com Maik G. Seewald, CISSP Sr. Technical Lead maseewal@cisco.com June 2016 Cyber
More informationCisco IOS Classic Firewall/IPS: Configuring Context Based Access Control (CBAC) for Denial of Service Protection
Cisco IOS Classic Firewall/IPS: Configuring Context Based Access Control (CBAC) for Denial of Service Protection Document ID: 98705 Contents Introduction Prerequisites Requirements Components Used Conventions
More informationINTRUSION DETECTION MODEL IN DATA MINING BASED ON ENSEMBLE APPROACH
INTRUSION DETECTION MODEL IN DATA MINING BASED ON ENSEMBLE APPROACH VIKAS SANNADY 1, POONAM GUPTA 2 1Asst.Professor, Department of Computer Science, GTBCPTE, Bilaspur, chhattisgarh, India 2Asst.Professor,
More informationNetDefend Firewall UTM Services
NetDefend Firewall UTM Services Unified Threat Management D-Link NetDefend UTM firewalls (DFL-260/860/1660/2560/2560G) integrate an Intrusion Prevention System (IPS), gateway AntiVirus (AV), and Web Content
More informationConfiguring attack detection and prevention 1
Contents Configuring attack detection and prevention 1 Overview 1 Attacks that the device can prevent 1 Single-packet attacks 1 Scanning attacks 2 Flood attacks 3 TCP fragment attack 4 Login DoS attack
More informationName of the lecturer Doç. Dr. Selma Ayşe ÖZEL
Y.L. CENG-541 Information Retrieval Systems MASTER Doç. Dr. Selma Ayşe ÖZEL Information retrieval strategies: vector space model, probabilistic retrieval, language models, inference networks, extended
More informationNetwork Intrusion Analysis (Hands on)
Network Intrusion Analysis (Hands on) TCP/IP protocol suite is the core of the Internet and it is vital to understand how it works together, its strengths and weaknesses and how it can be used to detect
More informationDenial of Service. Serguei A. Mokhov SOEN321 - Fall 2004
Denial of Service Serguei A. Mokhov SOEN321 - Fall 2004 Contents DOS overview Distributed DOS Defending against DDOS egress filtering References Goal of an Attacker Reduce of an availability of a system
More informationASA/PIX Security Appliance
I N D E X A AAA, implementing, 27 28 access to ASA/PIX Security Appliance monitoring, 150 151 securing, 147 150 to websites, blocking, 153 155 access control, 30 access policies, creating for web and mail
More informationCOMPUTER NETWORK SECURITY
COMPUTER NETWORK SECURITY Prof. Dr. Hasan Hüseyin BALIK (9 th Week) 9. Firewalls and Intrusion Prevention Systems 9.Outline The Need for Firewalls Firewall Characterictics and Access Policy Type of Firewalls
More informationComputer and Network Security
Computer and Network Security c Copyright 2000 R. E. Newman Computer & Information Sciences & Engineering University Of Florida Gainesville, Florida 32611-6120 nemo@cise.ufl.edu Network Security (Pfleeger
More informationChair for Network Architectures and Services Department of Informatics TU München Prof. Carle. Network Security. Chapter 8
Chair for Network Architectures and Services Department of Informatics TU München Prof. Carle Network Security Chapter 8 System Vulnerabilities and Denial of Service Attacks System Vulnerabilities and
More informationNetwork Intrusion Detection System Using Fuzzy Logic Ppt
Network Intrusion Detection System Using Fuzzy Logic Ppt Network intrusion detection, such as neural networks, appeared at a historic Although the approaches based on expert systems have high accuracy,
More informationCE Advanced Network Security
CE 817 - Advanced Network Security Lecture 5 Mehdi Kharrazi Department of Computer Engineering Sharif University of Technology Acknowledgments: Some of the slides are fully or partially obtained from other
More informationNetDetector The Most Advanced Network Security and Forensics Analysis System
Get Real......Real Solutions For Global Networks www.niksun.com NetDetector The Most Advanced Network Security and Forensics Analysis System NIKSUN, Inc. 1100 Cornwall Road Monmouth Junction, NJ 08852
More informationNIP6000 Next-Generation Intrusion Prevention System
NIP6000 Next-Generation Intrusion Prevention System Thanks to the development of the cloud and mobile computing technologies, many enterprises currently allow their employees to use smart devices, such
More informationPreface Preliminaries. Introduction to VoIP Networks. Public Switched Telephone Network (PSTN) Switching Routing Connection hierarchy Telephone
VoIP quality and performance issues Delay Jitter Packet loss Echo and talk overlap Approaches to maintaining VoIP quality Network-level QoS VoIP codecs VoIP applications and services Fax Emergency numbers
More informationExam: : VPN/Security. Ver :
Exam: Title : VPN/Security Ver : 03.20.04 QUESTION 1 A customer needs to connect smaller branch office locations to its central site and desires a more which solution should you recommend? A. V3PN solution
More information