Internet Bots Report Table of Content and Complete Online Tasks Faster Than Humans
Total Page:16
File Type:pdf, Size:1020Kb
Load more
Recommended publications
-
EVALUATION of HIDDEN MARKOV MODEL for MALWARE BEHAVIORAL CLASSIFICATION By
EVALUATION OF HIDDEN MARKOV MODEL FOR MALWARE BEHAVIORAL CLASSIFICATION By Mohammad Imran A research thesis submitted to the Department of Computer Science, Capital University of Science & Technology, Islamabad in partial fulfillment of the requirements for the degree of DOCTOR OF PHILOSOPHY IN COMPUTER SCIENCE DEPARTMENT OF COMPUTER SCIENCE CAPITAL UNIVERSITY OF SCIENCE & TECHNOLOGY ISLAMABAD October 2016 Copyright© 2016 by Mr. Mohammad Imran All rights reserved. No part of the material protected by this copyright notice may be reproduced or utilized in any form or any means, electronic or mechanical, including photocopying, recording or by any information storage and retrieval system, without the permission from the author. To my parents and family Contents List of Figures iv List of Tablesv Abbreviations vi Publications vii Acknowledgements viii Abstractx 1 Introduction1 1.1 What is malware?............................1 1.2 Types of malware............................3 1.2.1 Virus...............................3 1.2.2 Worm..............................3 1.2.3 Trojan horse...........................3 1.2.4 Rootkit.............................4 1.2.5 Spyware.............................4 1.2.6 Adware.............................4 1.2.7 Bot................................4 1.3 Malware attack vectors.........................5 1.3.1 Use of vulnerabilities......................5 1.3.2 Drive-by downloads.......................5 1.3.3 Social engineering........................5 1.4 Combating malware: Malware analysis................5 1.4.1 Static analysis..........................6 1.4.2 Dynamic analysis........................9 1.4.3 Hybrid analysis......................... 12 1.5 Combating malware: Malware detection and classification...... 12 1.6 Observations from the literature.................... 13 1.7 Problem statement........................... 15 1.8 Motivation................................ 15 1.8.1 Why malware analysis?..................... 16 1.8.2 Why Hidden Markov Model?................ -
A Case Study in DNS Poisoning
A wrinkle in time: A case study in DNS poisoning Harel Berger Amit Z. Dvir Moti Geva Abstract—The Domain Name System (DNS) provides a trans- lation between readable domain names and IP addresses. The DNS is a key infrastructure component of the Internet and a prime target for a variety of attacks. One of the most significant threat to the DNS’s wellbeing is a DNS poisoning attack, in which the DNS responses are maliciously replaced, or poisoned, by an attacker. To identify this kind of attack, we start by an analysis of different kinds of response times. We present an analysis of typical and atypical response times, while differentiating between the different levels of DNS servers’ response times, from root servers down to internal caching servers. We successfully identify empirical DNS poisoning attacks based on a novel method for DNS response timing analysis. We then present a system we developed to validate our technique that does not require any changes to the DNS protocol or any existing network equipment. Our validation system tested data from different architectures including LAN and cloud environments and real data from an Internet Service Provider (ISP). Our method and system differ from most other DNS poisoning detection methods and achieved high detection rates exceeding 99%. These findings suggest that when used in conjunction with other methods, they can considerably enhance the accuracy of these methods. I. INTRODUCTION Fig. 1. DNS hierarchy example The Domain Name System (DNS) [1], [2] is one of the best known protocols in the Internet. Its main function is to trans- late human-readable domain names into their corresponding following steps. -
Application Development with Tocollege.Net
CYAN YELLOW MAGENTA BLACK PANTONE 123 C BOOKS FOR PROFESSIONALS BY PROFESSIONALS® THE EXPERT’S VOICE® IN WEB DEVELOPMENT Companion eBook Available Covers Pro Web 2.0 Application GWT 1.5 Pro Development with GWT 2.0 Web Dear Reader, This book is for developers who are ready to move beyond small proof-of-concept Pro sample applications and want to look at the issues surrounding a real deploy- ment of GWT. If you want to see what the guts of a full-fledged GWT application look like, this is the book for you. GWT 1.5 is a game-changing technology, but it doesn’t exist in a bubble. Real deployments need to connect to your database, enforce authentication, protect against security threats, and allow good search engine optimization. To show you all this, we’ll look at the code behind a real, live web site called Application Development with ToCollege.net. This application specializes in helping students who are applying Web 2.0 to colleges; it allows them to manage their application processes and compare the rankings that they give to schools. It’s a slick application that’s ready for you to sign up for and use. Application Development This book will give you a walking tour of this modern Web 2.0 start-up’s code- base. The included source code will provide a functional demonstration of how to merge together the modern Java stack including Hibernate, Spring Security, Spring MVC 2.5, SiteMesh, and FreeMarker. This fully functioning application is better than treasure if you’re a developer trying to wire GWT into a Maven build environment who just wants to see some code that makes it work. -
A Study on Advanced Botnets Detection in Various Computing Systems Using Machine Learning Techniques
SJIF Impact Factor: 7.001| ISI I.F.Value:1.241| Journal DOI: 10.36713/epra2016 ISSN: 2455-7838(Online) EPRA International Journal of Research and Development (IJRD) Volume: 5 | Issue: 12 | December 2020 - Peer Reviewed Journal A STUDY ON ADVANCED BOTNETS DETECTION IN VARIOUS COMPUTING SYSTEMS USING MACHINE LEARNING TECHNIQUES K. Vamshi Krishna Assistant Professor, Department of Computer Science and Engineering, Narasaraopet Engineering College, Narasaraopet, Guntur, India Article DOI: https://doi.org/10.36713/epra5902 ABSTRACT Due to the rapid growth and use of Emerging technologies such as Artificial Intelligence, Machine Learning and Internet of Things, Information industry became so popular, meanwhile these Emerging technologies have brought lot of impact on human lives and internet network equipment has increased. This increment of internet network equipment may bring some serious security issues. A botnet is a number of Internet-connected devices, each of which is running one or more bots.The main aim of botnet is to infect connected devices and use their resource for automated tasks and generally they remain hidden. Botnets can be used to perform Distributed Denial-of-Service (DDoS) attacks, steal data, send spam, and allow the attacker to access the device and its connection. In this paper we are going to address the advanced Botnet detection techniques using Machine Learning. Traditional botnet detection uses manual analysis and blacklist, and the efficiency is very low. Applying machine learning to batch automatic detection of botnets can greatly improve the efficiency of detection. Using machine learning to detect botnets, we need to collect network traffic and extract traffic characteristics, and then use X-Means, SVM algorithm to detect botnets. -
Open Sisko Final__Thesis.Pdf
THE PENNSYLVANIA STATE UNIVERSITY SCHREYER HONORS COLLEGE DEPARTMENT OF INFORMATION SCIENCE AND TECHNOLOGY THE WAKE OF CYBER-WAREFARE STUDENT NAME JACOB SISKO SPRING 2015 A thesis submitted in partial fulfillment of the requirements for a baccalaureate degree in Security and Risk Analysis with honors in Security and Risk Analysis Reviewed and approved* by the following: Gerald Santoro Senior Lecturer Professor Information Sciences and Technology Thesis Supervisor Edward Glantz Senior Lecturer Professor Information Sciences and Technology Honors Adviser * Signatures are on file in the Schreyer Honors College. i ABSTRACT The purpose of my thesis paper is to explain the significance and inter-relationship of cyber-crime and cyber-warfare. To give my reader a full understanding of the issue, I will begin by explaining the history of the Internet, give a definition of both cyber-crime and cyber-warfare, and then explain how they have impacted the Internet. I will also give examples of a few Chinese hacker groups, and what kind of attacks they have successfully carried out. Then I will talk about how recent attacks have become more sophisticated which are capable of causing more damage. I would also like to discuss how the cyber-warfare has impacted Chinese-US relations, and how it has an impact on the economic ties. Because of the currently capability and potential threat, I will explain why cyber-crime and cyber-warfare are so important to monitor because of the potential damage current and future attacks can cause. ii TABLE OF CONTENTS ACKNOWLEDGEMENTS ......................................................................................... iii Chapter 1 Introduction ................................................................................................. 1 History of the Internet ...................................................................................................... 1 Chapter 2 Cyber-crime – Functions and Capabilities ................................................. -
Definition of Bot in Computer Terms
Definition Of Bot In Computer Terms Crippen.Hearsay BenjieEmil crease Russianising gratifyingly uppishly. as kutcha Kin isHy ironfisted: blue-pencilled she ingenerates her frog wholesale boiling and crazily. wadsets her What bots of bot definition in computer terms chatbot interacts with Refers to a connection between networked computers in below the services of one computer the server are requested by trout other the client Information. The definitions of software that in many users to refer to be used to learn. Is Apex legends Dead 2020? Bot Meaning Best 24 Definitions of Bot YourDictionary. Frequently used in computing definitions of bots designed specifically for words usually protect your neighborhood café and conferencing solutions to. 5 Reasons Why Your Chatbot Needs Natural Language. Evaluating your computer term virus indicates that of computing definitions include in. You letter take pictures with these will send form via the mobile networks to other mobile devices with exactly same technology or to email addresses via the Internet. Using the feed, depending on the game is not contain the impact how web hosting, time by the existing data or valley powers bots? Build and manage Einstein Bots to ease my load on both service agents. This witness the beef of rendered slots googletag. What kinds of repetitive tasks can corn handle? Glossary Archive Malwarebytes Labs Malwarebytes Labs. Contrast to bots in computing definitions above methods that a definition is the tech by coders and access. Often, is kernel type of backdoor that gives an attacker similar remote repair, when the AI is unsure or wants clarification. Problem seems to be cut on network shit on devices. -
It's All About Visibility
8 It’s All About Visibility This chapter looks at the critical tasks for getting your message found on the web. Now that we’ve discussed how to prepare a clear targeted message using the right words (Chapter 5, “The Audience Is Listening (What Will You Say?)”), we describe how online visibility depends on search engine optimization (SEO) “eat your broccoli” basics, such as lightweight and crawlable website code, targeted content with useful labels, and inlinks. In addition, you can raise the visibility of your website, products, and services online through online advertising such as paid search advertising, outreach through social websites, and display advertising. 178 Part II Building the Engine Who Sees What and How Two different tribes visit your website: people, and entities known as web spiders (or crawlers or robots). People will experience your website differently based on their own characteristics (their visual acuity or impairment), their browser (Internet Explorer, Chrome, Firefox, and so on), and the machine they’re using to view your website (a TV, a giant computer monitor, a laptop screen, or a mobile phone). Figure 8.1 shows a page on a website as it appears to website visitors through a browser. Figure 8.1 Screenshot of a story page on Model D, a web magazine about Detroit, Michigan, www.modeldmedia.com. What Search Engine Spiders See The web spiders are computer programs critical to your business because they help people who don’t know about your website through your marketing efforts find it through the search engines. The web spiders “crawl” through your website to learn about what it contains and carry information back to the gigantic servers behind the search engines, so that the search engine can provide relevant results to people searching for your product or service. -
An Algorithm for Anomaly-Based Botnet Detection
An Algorithm for Anomaly-based Botnet Detection James R. Binkley Suresh Singh Computer Science Dept. Computer Science Dept. Portland State University Portland State University Portland, OR, USA Portland, OR, USA [email protected] [email protected] Abstract individual host in the IP channel was a scanner. We then sort the IRC channels by scanning count, with the top We present an anomaly-based algorithm for detecting suspect channels labeled as possible evil channels. This IRC-based botnet meshes. The algorithm combines an algorithm is not signature-based in any way. It does not IRC mesh detection component with a TCP scan detec- rely on ports or known botnet command strings. As a tion heuristic called the TCP work weight. The IRC com- result, we are immune to zero-day problems. Our algo- ponent produces two tuples, one for determining the IRC rithm does assume that IRC is cleartext and that attacks mesh based on IP channel names, and a sub-tuple which are being made with the botnet mesh. collects statistics (including the TCP work weight) on in- dividual IRC hosts in channels. We sort the channels by the number of scanners producing a sorted list of poten- 2 IRC Botnet Detection Algorithm tial botnets. This algorithm has been deployed in PSU’s DMZ for over a year and has proven effective in reducing Our architecture relies on the observation that IRC hosts the number of botnet clients. are grouped into channels by a channel name (for exam- ple, ”F7”, or ”ubuntu” might be channel names), and that an evil channel is an IRC channel with a majority of hosts 1 Introduction performing TCP SYN scanning. -
Zerohack Zer0pwn Youranonnews Yevgeniy Anikin Yes Men
Zerohack Zer0Pwn YourAnonNews Yevgeniy Anikin Yes Men YamaTough Xtreme x-Leader xenu xen0nymous www.oem.com.mx www.nytimes.com/pages/world/asia/index.html www.informador.com.mx www.futuregov.asia www.cronica.com.mx www.asiapacificsecuritymagazine.com Worm Wolfy Withdrawal* WillyFoReal Wikileaks IRC 88.80.16.13/9999 IRC Channel WikiLeaks WiiSpellWhy whitekidney Wells Fargo weed WallRoad w0rmware Vulnerability Vladislav Khorokhorin Visa Inc. Virus Virgin Islands "Viewpointe Archive Services, LLC" Versability Verizon Venezuela Vegas Vatican City USB US Trust US Bankcorp Uruguay Uran0n unusedcrayon United Kingdom UnicormCr3w unfittoprint unelected.org UndisclosedAnon Ukraine UGNazi ua_musti_1905 U.S. Bankcorp TYLER Turkey trosec113 Trojan Horse Trojan Trivette TriCk Tribalzer0 Transnistria transaction Traitor traffic court Tradecraft Trade Secrets "Total System Services, Inc." Topiary Top Secret Tom Stracener TibitXimer Thumb Drive Thomson Reuters TheWikiBoat thepeoplescause the_infecti0n The Unknowns The UnderTaker The Syrian electronic army The Jokerhack Thailand ThaCosmo th3j35t3r testeux1 TEST Telecomix TehWongZ Teddy Bigglesworth TeaMp0isoN TeamHav0k Team Ghost Shell Team Digi7al tdl4 taxes TARP tango down Tampa Tammy Shapiro Taiwan Tabu T0x1c t0wN T.A.R.P. Syrian Electronic Army syndiv Symantec Corporation Switzerland Swingers Club SWIFT Sweden Swan SwaggSec Swagg Security "SunGard Data Systems, Inc." Stuxnet Stringer Streamroller Stole* Sterlok SteelAnne st0rm SQLi Spyware Spying Spydevilz Spy Camera Sposed Spook Spoofing Splendide -
Universidad Carlos Iii De Madrid Signal Processing
UNIVERSIDAD CARLOS III DE MADRID ESCUELA POLITÉCNICA SUPERIOR BACHELOR THESIS SIGNAL PROCESSING FOR MALWARE ANALYSIS Computer Engineering Department AUTHOR: Raquel Tabuyo Benito TUTOR: Pedro Peris Lopez June, 2016 Bachelor Thesis. Signal Processing for Malware Analysis “Perseverance is not a long race. It is many short races one aftr te oter” -Walter Elliot - Page .2 of 134. - Bachelor Thesis. Signal Processing for Malware Analysis Acknowledgements To my whole family, specially my sister, for whom I have an unconditionally love. I am really grateful for their dedication, patience, support and encouragement to follow my dreams. To Pedro, my Bachelor Thesis tutor, whose kindness and guidance have helped me during this wonderful trip. To my friends, thank you very much for showing me the meaning of true friendship. Without all of you, this would have never been possible. - Page .3 of 134. - Bachelor Thesis. Signal Processing for Malware Analysis Abstract This Project is an experimental analysis of Android malware through images. The analysis is based on classifying the malware into families or differentiating between goodware and malware. This analysis has been done considering two approaches. These two approaches have a common starting point, which is the transformation of Android applications into PNG images. After this conversion, the first approach was subtracting each image from the testing set with the images of the training set, in order to establish which unknown malware belongs to a specific family or to distinguish between goodware and malware. Although the accuracy was higher than the one defined in the requirements, this approach was a time consuming task, so we consider another approach to reduce the time and get the same or better accuracy. -
Google Cheat Sheets [.Pdf]
GOOGLE | CHEAT SHEET Key for skill required Novice This two page Google Cheat Sheet lists all Google services and tools as to understand the Intermediate well as background information. The Cheat Sheet offers a great reference underlying concepts to grasp of basic to advance Google query building concepts and ideas. Expert CHEAT SHEET GOOGLE SERVICES Google domains google.co.kr Google Company Information google.ae google.kz Public (NASDAQ: GOOG) and google.com.af google.li (LSE: GGEA) Google AdSense https://www.google.com/adsense/ google.com.ag google.lk google.off.ai google.co.ls Founded Google AdWords https://adwords.google.com/ google.am google.lt Menlo Park, California (1998) Google Analytics http://google.com/analytics/ google.com.ar google.lu google.as google.lv Location Google Answers http://answers.google.com/ google.at google.com.ly Mountain View, California, USA Google Base http://base.google.com/ google.com.au google.mn google.az google.ms Key people Google Blog Search http://blogsearch.google.com/ google.ba google.com.mt Eric E. Schmidt Google Bookmarks http://www.google.com/bookmarks/ google.com.bd google.mu Sergey Brin google.be google.mw Larry E. Page Google Books Search http://books.google.com/ google.bg google.com.mx George Reyes Google Calendar http://google.com/calendar/ google.com.bh google.com.my google.bi google.com.na Revenue Google Catalogs http://catalogs.google.com/ google.com.bo google.com.nf $6.138 Billion USD (2005) Google Code http://code.google.com/ google.com.br google.com.ni google.bs google.nl Net Income Google Code Search http://www.google.com/codesearch/ google.co.bw google.no $1.465 Billion USD (2005) Google Deskbar http://deskbar.google.com/ google.com.bz google.com.np google.ca google.nr Employees Google Desktop http://desktop.google.com/ google.cd google.nu 5,680 (2005) Google Directory http://www.google.com/dirhp google.cg google.co.nz google.ch google.com.om Contact Address Google Earth http://earth.google.com/ google.ci google.com.pa 2400 E. -
Supervised Machine Learning Bot Detection Techniques to Identify
SMU Data Science Review Volume 1 | Number 2 Article 5 2018 Supervised Machine Learning Bot Detection Techniques to Identify Social Twitter Bots Phillip George Efthimion Southern Methodist University, [email protected] Scott aP yne Southern Methodist University, [email protected] Nicholas Proferes University of Kentucky, [email protected] Follow this and additional works at: https://scholar.smu.edu/datasciencereview Part of the Theory and Algorithms Commons Recommended Citation Efthimion, hiP llip George; Payne, Scott; and Proferes, Nicholas (2018) "Supervised Machine Learning Bot Detection Techniques to Identify Social Twitter Bots," SMU Data Science Review: Vol. 1 : No. 2 , Article 5. Available at: https://scholar.smu.edu/datasciencereview/vol1/iss2/5 This Article is brought to you for free and open access by SMU Scholar. It has been accepted for inclusion in SMU Data Science Review by an authorized administrator of SMU Scholar. For more information, please visit http://digitalrepository.smu.edu. Efthimion et al.: Supervised Machine Learning Bot Detection Techniques to Identify Social Twitter Bots Supervised Machine Learning Bot Detection Techniques to Identify Social Twitter Bots Phillip G. Efthimion1, Scott Payne1, Nick Proferes2 1Master of Science in Data Science, Southern Methodist University 6425 Boaz Lane, Dallas, TX 75205 {pefthimion, mspayne}@smu.edu [email protected] Abstract. In this paper, we present novel bot detection algorithms to identify Twitter bot accounts and to determine their prevalence in current online discourse. On social media, bots are ubiquitous. Bot accounts are problematic because they can manipulate information, spread misinformation, and promote unverified information, which can adversely affect public opinion on various topics, such as product sales and political campaigns.