Genetic and Evolutionary Computation Conference 2019
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Malpedia: a Collaborative Effort to Inventorize the Malware Landscape
Malpedia: A Collaborative Effort to Inventorize the Malware Landscape Daniel Plohmann @push_pnx [email protected] 2017-12-07 | Botconf, Montpellier Martin Clauß martin.clauß@fkie.fraunhofer.de Steffen Enders [email protected] Elmar Padilla [email protected] 1 © Cyber Analysis and Defense Department, Fraunhofer FKIE $whoami Daniel Plohmann Security Researcher @ Fraunhofer (Europe‘s largest organisation for applied research) Research Scope: Malware Analysis Reverse Engineering Automation 2 © Cyber Analysis and Defense Department, Fraunhofer FKIE Outline Summary Motivation (or: how it began) Approach The Malpedia Corpus & Platform A Comparative Structural Analysis of Windows Malware Future Plans / Conclusion 3 © Cyber Analysis and Defense Department, Fraunhofer FKIE Summary 4 © Cyber Analysis and Defense Department, Fraunhofer FKIE Summary TL;DR What is Malpedia? A free, independent, pooled resource for confidently labeled, unpacked reference samples for malware families and versions Meta data tracker for info such as references (analysis reports, blogs, …), YARA rules, actors, tied to these families Status (2017-12-01): 2491 samples for 669 families, multi-platform (WIN, ELF, APK, OSX, …) Our Contributions Definition of requirements for malware corpora and a reference corpus + platform implementing these A Comprehensive, quantitative static analysis of structural features for 446 Windows malware families 5 © Cyber Analysis and Defense Department, Fraunhofer FKIE Motivation … or -
Evolutionary Data Mining Approach to Creating Digital Logic
EVOLUTIONARY DATA MINING APPROACH TO CREATING DIGITAL LOGIC James F. Smith III, ThanhVu H. Nguyen Code 5741, Naval Research Laboratory, Washington, DC, 20375-5320, USA Keywords: Optimization problems in signal processing, signal reconstruction, system identification, time series and system modeling. Abstract: A data mining based procedure for automated reverse engineering has been developed. The data mining algorithm for reverse engineering uses a genetic program (GP) as a data mining function. A genetic program is an algorithm based on the theory of evolution that automatically evolves populations of computer programs or mathematical expressions, eventually selecting one that is optimal in the sense it maximizes a measure of effectiveness, referred to as a fitness function. The system to be reverse engineered is typically a sensor. Design documents for the sensor are not available and conditions prevent the sensor from being taken apart. The sensor is used to create a database of input signals and output measurements. Rules about the likely design properties of the sensor are collected from experts. The rules are used to create a fitness function for the genetic program. Genetic program based data mining is then conducted. This procedure incorporates not only the experts’ rules into the fitness function, but also the information in the database. The information extracted through this process is the internal design specifications of the sensor. Significant mathematical formalism and experimental results related to GP based data mining for reverse engineering will be provided. 1 INTRODUCTION The rules are used to create a fitness function for the genetic program. Genetic program based data An engineer must design a signal that will yield a mining is then conducted (Bigus 1996, Smith 2003a, particular type of output from a sensor device (SD). -
Artificial Intelligence and Strategic Trade Controls
Technical Report Artificial Intelligence and Strategic Trade Controls Strategic Trade Research Institute Center for International and Security Studies at Maryland June 2020 , The authors of this report invite liberal use of the information provided, requiring only that the reproduced material clearly cite the source, using: Andrea Viski, Scott Jones, Lindsay Rand, Tucker Boyce, and Jonas Siegel, “Artificial Intelligence and Strategic Trade Controls,” Strategic Trade Research Institute and Center for International and Security Studies at Maryland, June 2020. Report Design and Layout by Andrea Viski Copyright 2020, Strategic Trade Research Institute and Center for International and Security Studies at Maryland Printed in the United States of America Artificial Intelligence and Strategic Trade Controls Acknowledgments The authors would like to sincerely thank the participants of the dialogue on Emerging Technologies and Strategic Trade Controls held at the Stimson Center in Washington DC on March 14, 2019, as well as the participants of the dialogue on Artificial Intelligence and Strategic Trade Controls held at the Ronald Reagan International Trade Center on March 9, 2020. The authors would also like to thank Amy Nelson, Kevin Wolf, Carl Wocke, Aaron Mannes, Timothy Gildea, Aaron Arnold, Todd Perry, Nancy Gallagher, and Richard Cupitt for their support, comments, ideas, and feedback on this report. About the Strategic Trade Research Institute The Strategic Trade Research Institute was founded in 2017 and is an independent, international, board-governed non-profit organization dedicated to building networks of strategic trade research and practice through leadership, research, and innovation. STRI publishes the Strategic Trade Review, the leading peer reviewed journal dedicated to trade and security. -
A Genetic Algorithm Solution Approach Introd
A new optimization model for market basket analysis with allocation considerations: A genetic algorithm solution approach Majeed HEYDARI University of Zanjan, Zanjan, Iran [email protected] Amir YOUSEFLI Imam Khomeini International University, Qazvin, Iran [email protected] Abstract. Nowadays market basket analysis is one of the interested research areas of the data mining that has received more attention by researchers. But, most of the related research focused on the traditional and heuristic algorithms with limited factors that are not the only influential factors of the basket market analysis. In this paper to efficient modeling and analysis of the market basket data, the optimization model is proposed with considering allocation parameter as one of the important and effectual factors of the selling rate. The genetic algorithm approach is applied to solve the formulated non-linear binary programming problem and a numerical example is used to illustrate the presented model. The provided results reveal that the obtained solutions seem to be more realistic and applicable. Keywords: market basket analysis, association rule, non-linear programming, genetic algorithm, optimization. Please cite the article as follows: Heydary, M. and Yousefli, A. (2017), “A new optimization model for market basket analysis with allocation considerations: A genetic algorithm solution approach”, Management & Marketing. Challenges for the Knowledge Society, Vol. 12, No. 1, pp. 1- 11. DOI: 10.1515/mmcks-2017-0001 Introduction Nowadays, data mining is widely used in several aspects of science such as manufacturing, marketing, CRM, retail trade etc. Data mining or knowledge discovery is a process for data analyzing to extract information from large databases. -
PETYA・JIGSAW・WANNACRY・ZEPTO・LOCKY Business Resilience = Data Resilience
PETYA・JIGSAW・WANNACRY・ZEPTO・LOCKY Business Resilience = Data Resilience Speaker Introduction Brent Reichow From Minneapolis, Minnesota (USA) April 1992 Arrived in Chiba, Japan Work History LINC Computers (EDS), NTT-WT, PSINet (C&W) Stellent (Oracle), Internet Security Systems (ISS) July 2004 Co-founded Blueshift K.K. Blueshift Business Leading provider of data protection solutions delivering secure, off-site, disk based, data backup, and disaster recovery services to small, medium and large organizations Client Markets Automotive, education, financial services, healthcare, insurance, legal services, logistics, manufacturing, marketing, media, NPO, real estate, recruiting, retail and technology URL www.dataprotection.co.jp/www.dataprotection.jp Blueshift’s Cloud Backup Business A. Initial full backup is made, compressed and encrypted data is sent to public or private data center locations B. Additional schedule or manual backups, will transfer changed data (deltas) off-site (incremental forever Public or Private Client Site Data Centers • File Server • Mail Server C. Rapid restores (deltas /changed data) WAN / INTERNET • Database Server • Virtual Machine (VM) • Multiple restoration points in time • Cloud 2 Cloud • Restore in minutes not hours G. Retention Policy Administrator • 30 day, 1 year D. Security Location 2 • Longer options • All data is encrypted with 256 bit AES • Data remains encrypted in flight and at rest F. Remote Management E. Onsite Appliance (de-duplication, compression, encryption) • Email alerting functionality • LAN speed restores with local available storage • Manage multiple servers A Billion Dollar Industry So How Does Ransomware Work? Ransomware as a Service (RaaS) - Typically the Developer Receives 30% of Ransom Paid by Victims Zepto Ransomware Attack Internet Phishing Email Workstations USB File Database NAS Server Your computer files have been encrypted Your photos, videos, documents, etc… But, don’t worry! I have not deleted them, yet. -
Optimization-Based Evolutionary Data Mining Techniques for Structural Health Monitoring
Journal of Civil Engineering and Construction 2020;9(1):14-23 https://doi.org/10.32732/jcec.2020.9.1.14 Optimization-Based Evolutionary Data Mining Techniques for Structural Health Monitoring Meisam Gordan, Zubaidah Binti Ismail, Hashim Abdul Razak, Khaled Ghaedi, Haider Hamad Ghayeb Department of Civil Engineering, University of Malaya, 50603, Kuala Lumpur, Malaysia E-mail: [email protected] Received: 27 July 2019; Accepted: 24 August 2019; Available online: 20 November 2019 Abstract: In recent years, data mining technology has been employed to solve various Structural Health Monitoring (SHM) problems as a comprehensive strategy because of its computational capability. Optimization is one the most important functions in Data mining. In an engineering optimization problem, it is not easy to find an exact solution. In this regard, evolutionary techniques have been applied as a part of procedure of achieving the exact solution. Therefore, various metaheuristic algorithms have been developed to solve a variety of engineering optimization problems in SHM. This study presents the most applicable as well as effective evolutionary techniques used in structural damage identification. To this end, a brief overview of metaheuristic techniques is discussed in this paper. Then the most applicable optimization-based algorithms in structural damage identification are presented, i.e. Particle Swarm Optimization (PSO), Genetic Algorithm (GA), Imperialist Competitive Algorithm (ICA) and Ant Colony Optimization (ACO). Some related examples are also detailed in order to indicate the efficiency of these algorithms. Keywords: Structural damage detection; Data mining; Metaheuristic algorithms; Optimization. 1. Introduction In-service aerospace, mechanical, and civil structures are damage-prone during their service life. -
Letters to the Editor
Articles Letters to the Editor Research Priorities for is a product of human intelligence; we puter scientists, innovators, entrepre - cannot predict what we might achieve neurs, statisti cians, journalists, engi - Robust and Beneficial when this intelligence is magnified by neers, authors, professors, teachers, stu - Artificial Intelligence: the tools AI may provide, but the eradi - dents, CEOs, economists, developers, An Open Letter cation of disease and poverty are not philosophers, artists, futurists, physi - unfathomable. Because of the great cists, filmmakers, health-care profes - rtificial intelligence (AI) research potential of AI, it is important to sionals, research analysts, and members Ahas explored a variety of problems research how to reap its benefits while of many other fields. The earliest signa - and approaches since its inception, but avoiding potential pitfalls. tories follow, reproduced in order and as for the last 20 years or so has been The progress in AI research makes it they signed. For the complete list, see focused on the problems surrounding timely to focus research not only on tinyurl.com/ailetter. - ed. the construction of intelligent agents making AI more capable, but also on Stuart Russell, Berkeley, Professor of Com - — systems that perceive and act in maximizing the societal benefit of AI. puter Science, director of the Center for some environment. In this context, Such considerations motivated the “intelligence” is related to statistical Intelligent Systems, and coauthor of the AAAI 2008–09 Presidential Panel on standard textbook Artificial Intelligence: a and economic notions of rationality — Long-Term AI Futures and other proj - Modern Approach colloquially, the ability to make good ects on AI impacts, and constitute a sig - Tom Dietterich, Oregon State, President of decisions, plans, or inferences. -
Essential Faqs to Combating Ransomware
ESSENTIAL FAQS TO COMBATING RANSOMWARE S e p t e m b e r 2016 www.ensilo.com RESEARCH PAPER TABLE OF CONTENTS What is Ransomware? 3 Does Ransomware Only Encrypt Files? 4 What Are the Common Types of Ransomware In-the-Wild? 4 How are Victims Infected? 4 At What Stage Does the Ransomware Encrypt the Data? 5 How Long Does it Take for Ransomware to Encrypt Files? 5 How are the Threat Actors Paid? 6 What Platforms does Ransomware Target? 6 Don’t the Operating System Vendors, such as Microsoft, Place 7 Protections to Prevent Ransomware from Running? Have You Seen Any Ransomware Cases? 7 Is Ransomware a Periodic Fad or a Trending Issue? 8 What Strategy Should Businesses Adopt to Combat Ransomware? 9 Samples of ransomware notes 10 About enSilo 11 RESEARCH PAPER Ransomware isn’t new. But the tactics are. Ransomware has gone from a nickel & dime operation targeting individual computers to a multimillion dollar criminal operation targeting organizations that can afford to pay enterprise-level payments. Research1 showed that a single threat actor was “making more than $30M USD annually from ransomware infections alone”. Clearly, with such a strong financial motivation behind ransomware, the threat criminals behind these types of attacks are not going to stop anytime soon. To help combat against the threat of ransomware, we’ve put together this FAQ. If you see any question you’d like to add, or just want to be heard, feel free to email us: [email protected] WHAT IS RANSOMWARE? Ransomware is an increasingly popular tactic used to steal data and disrupt a system’s operations. -
Digital Transformation Review Eleventh Edition
Digital Transformation Review Eleventh Edition Artificial Intelligence Decoded 2 Artificial Intelligence Decoded Digital Transformation Review Eleventh Edition Artificial Intelligence Decoded Edited by Capgemini Research Institute About the Capgemini Research Institute: The Capgemini Research Institute is Capgemini’s in-house think-tank on all things digital. The Institute publishes research on the impact of digital technologies on large traditional businesses. The team draws on the worldwide network of Capgemini experts and works closely with academic and technology partners. The Institute has dedicated research centers in India, the United Kingdom, and the United States. @capgemini #DTR11 www.capgemini.com/the-digital-transformation-institute Artificial Intelligence Decoded 3 Contents 06 Academia Lanny Cohen’s foreword 32 Professor Luciano Floridi, University of Oxford AI: Adaptable Intelligence 08 Editorial Artificial Intelligence: Preparing Organizations and their People for 38 Drastic Change Michael Schrage, MIT AI: Survival of the Smartest View from Large Organizations The ‘Valley’ 16 Michael Natusch, Prudential Plc 46 AI: Augmented Intelligence Frank Chen, a16z Matches People and Machines Make AI a Daily Habit 22 56 Rajen Sheth, Atif Rafiq, Google Volvo Cars Putting AI to Work: With Customers Democratizing AI for Traditional and Within the Enterprise Businesses 4 Artificial Intelligence Decoded 62 Capgemini Babak Hodjat, Perspective Sentient Technologies AI: Already Delivering Measurable Results across Sectors 92 Turning AI Into -
Multi Objective Optimization of Classification Rules Using Cultural Algorithms
Available online at www.sciencedirect.com Procedia Engineering Procedia Engineering 00 (2011) 000–000 Procedia Engineering 30 (2012) 457 – 465 www.elsevier.com/locate/procedia International Conference on Communication Technology and System Design 2011 Multi Objective Optimization of classification rules using Cultural Algorithms Sujatha Srinivasana, Sivakumar Ramakrishnana, a* aDept. of Computer Science, AVVM Sri Pushpam College, Poondi, Tanjore-613503, Tamil Nadu, India Abstract Classification rule mining is the most sought out by users since they represent highly comprehensible form of knowledge. The rules are evaluated based on objective and subjective metrics. The user must be able to specify the properties of the rules. The rules discovered must have some of these properties to render them useful. These properties may be conflicting. Hence discovery of rules with specific properties is a multi objective optimization problem. Cultural Algorithm (CA) which derives from social structures, and which incorporates evolutionary systems and agents, and uses five knowledge sources (KS’s) for the evolution process better suits the need for solving multi objective optimization problem. In the current study a cultural algorithm for classification rule mining is proposed for multi objective optimization of rules. © 2011 Published by Elsevier Ltd. Selection and/or peer-review under responsibility of ICCTSD 2011 Keywords: Multi-objective-optimization; Classification; Cultural Algorithm; Data mining. 1. Introduction Classification rule mining is a class of problems which is the most sought out by decision makers since they produce comprehensible form of knowledge. The rules produced by the rule mining approach are evaluated using various metrics which are called the properties of the rule. -
Efficient Evolutionary Data Mining Algorithms Applied to the Insurance Fraud Prediction
International Journal of Machine Learning and Computing, Vol. 2, No. 3, June 2012 Efficient Evolutionary Data Mining Algorithms Applied to the Insurance Fraud Prediction Jenn-Long Liu, Chien-Liang Chen, and Hsing-Hui Yang instances of insurance cases for data mining. The 5000 Abstract—This study proposes two kinds of Evolutionary instances are divided into 4000 instances to be the training set Data Mining (EvoDM) algorithms to the insurance fraud and 1000 instances to be the test set. Furthermore, this work prediction. One is GA-Kmeans by combining K-means applies K-means, GA-Kmeans and MPSO-Kmeans algorithm with genetic algorithm (GA). The other is algorithms to evaluate the fraud or not from the training set MPSO-Kmeans by combining K-means algorithm with Momentum-type Particle Swarm Optimization (MPSO). The and also evaluate the accuracy of fraud prediction for the test dataset used in this study is composed of 6 attributes with 5000 set. instances for car insurance claim. These 5000 instances are divided into 4000 training data and 1000 test data. Two II. CRISP-DM different initial cluster centers for each attribute are set by means of (a) selecting the centers randomly from the training CRISP-DM (Cross Industry Standard Process for Data set and (b) averaging all data of training set, respectively. Mining) is a data mining process model that describes Thereafter, the proposed GA-Kmeans and MPSO-Kmeans are commonly used approaches for expert data miners use to employed to determine the optimal weights and final cluster solve problems. CRISP-DM was conceived in late 1996 by centers for attributes, and the accuracy of prediction for test set SPSS (then ISL), NCR and DaimlerChrysler (then is computed based on the optimal weights and final cluster Daimler-Benz). -
Human-Computer Interaction 10Th International Conference Jointly With
Human-Computer Interaction th 10 International Conference jointly with: Symposium on Human Interface (Japan) 2003 5th International Conference on Engineering Psychology and Cognitive Ergonomics 2nd International Conference on Universal Access in Human-Computer Interaction 2003 Final Program 22-27 June 2003 • Crete, Greece In cooperation with: Conference Centre, Creta Maris Hotel Chinese Academy of Sciences Japan Management Association Japan Ergonomics Society Human Interface Society (Japan) Swedish Interdisciplinary Interest Group for Human-Computer Interaction - STIMDI Associación Interacción Persona Ordenador - AIPO (Spain) InternationalGesellschaft für Informatik e.V. - GI (Germany) European Research Consortium for Information and Mathematics - ERCIM HCI www.hcii2003.gr Under the auspices of a distinguished international board of 114 members from 24 countries Conference Sponsors Contacts Table of Contents HCI International 2003 HCI International 2003 HCI International 2003 Welcome Note 2 Institute of Computer Science (ICS) Foundation for Research and Technology - Hellas (FORTH) Conference Registration - Secretariat Foundation for Research Thematic Areas & Program Boards 3 Science and Technology Park of Crete Conference Registration takes place at the Conference Secretariat, located at and Technology - Hellas Heraklion, Crete, GR-71110 the Olympus Hall, Conference Centre Level 0, during the following hours: GREECE Institute of Computer Science Saturday, June 21 14:00 – 20:00 http://www.ics.forth.gr Opening Plenary Session FORTH Tel.: