Visualization For Computer Security 2005

Networked computers are increasingly ubiquitous, and they are subject to attack, misuse, and abuse. Every effort is being made by organizations and individuals to build and maintain trustworthy computing systems. The traditional, signature-based and statistical methods are limited in their capability to cope with the large, evolving data and the dynamic nature of Internet. In many applications, visualization proves very effective to understand large high-dimensional data. Thus, there is a growing interest in the development of visualization methods as alternative or complementary solutions to the pressing cyber security problems. VizSEC 2005 explores some of these solutions in today¿s current research.

This book constitutes the refereed proceedings of the 5th International Workshop on Visualization for Cyber Security held on September 15, 2008, in Cambridge, Massachusetts, USA, in conjunction with the 11th International Symposium on Recent Advances in Intrusion Detection (RAID). The 18 papers presented in this volume were carefully reviewed and selected from 27 submissions. VizSec research has focused on helping human analysts to detect anomalies and patterns, particularly in computer network defense. This year's paper focus on bridging the gap between visualization and automation.

Big data is presenting challenges to cybersecurity. For an example, the Internet of Things (IoT) will reportedly soon generate a staggering 400 zettabytes (ZB) of data a year. Self-driving cars are predicted to churn out 4000 GB of data per hour of driving. Big data analytics, as an emerging analytical technology, offers the capability to collect, store, process, and visualize these vast amounts of data. Big Data Analytics in Cybersecurity examines security challenges surrounding big data and provides actionable insights that can be used to improve the current practices of network operators and administrators. Applying big data analytics in cybersecurity is critical. By exploiting data from the networks and computers, analysts can discover useful network information from data. Decision makers can make more informative decisions by using this analysis, including what actions need to be performed, and improvement recommendations to policies, guidelines, procedures, tools, and other aspects of the network processes. Bringing together experts from academia, government laboratories, and industry, the book provides insight to both new and more experienced security professionals, as well as data analytics professionals who have varying levels of cybersecurity expertise. It covers a wide range of topics in cybersecurity, which include: Network forensics Threat analysis Vulnerability assessment Visualization Cyber training. In addition, emerging security domains such as the IoT, cloud computing, fog computing, mobile computing, and cyber-social networks are examined. The book first focuses on how big data analytics can be used in different aspects of cybersecurity including network forensics, root-cause analysis, and security training. Next it discusses big data challenges and solutions in such emerging cybersecurity domains as fog computing, IoT, and mobile app security. The book concludes by presenting the tools and datasets for future cybersecurity research.

An introduction to a range of cyber security issues explains how to utilize graphical approaches to displaying and understanding computer security data, such as network traffic, server logs, and executable files, offering guidelines for identifying a network attack, how to assess a system for vulnerabilities with Afterglow and RUMINT visualization software, and how to protect a system from additional attacks. Original. (Intermediate)

This monograph comprises work on network-based Intrusion Detection (ID) that is grounded in visualisation and hybrid Artificial Intelligence (AI). It has led to the design of MOVICAB-IDS (MObile VIsualisation Connectionist Agent-Based IDS), a novel Intrusion Detection System (IDS), which is comprehensively described in this book. This novel IDS combines different AI paradigms to visualise network traffic for ID at packet level. It is based on a dynamic Multiagent System (MAS), which integrates an unsupervised neural projection model and the Case-Based Reasoning (CBR) paradigm through the use of deliberative agents that are capable of learning and evolving with the environment. The proposed novel hybrid IDS provides security personnel with a synthetic, intuitive snapshot of network traffic and protocol interactions. This visualisation interface supports the straightforward detection of anomalous situations and their subsequent identification. The performance of MOVICAB-IDS was tested through a novel mutation-based testing method in different real domains which entailed several attacks and anomalous situations.

This volume constitutes the refereed proceedings of two workshops: the Second International Workshop on Modern Cryptography and Security Engineering (MoCrySEn 2013) and the Third International Workshop on Security and Cognitive Informatics for Homeland Defense (SeCIHD 2013) held within the framework of the IFIP 8.4, 8.9, TC 5 International Cross-Domain Conference, CD-ARES 2013, in Regensburg, Germany, in September 2013. The 16 revised papers presented at MoCrySEn 2013 were carefully reviewed and selected from 30 submissions. They deal with symmetric-key cryptography, public-key cryptography, algorithmic cryptanalysis, software and hardware implementation of cryptographic algorithms, database encryption, and interaction between cryptographic theory and implementation issues. The 15 papers presented at SeCIHD 2013 are organized in topical sections on cyber security and dependability, network security and privacy, and multimedia technology for homeland defense.