The Experts below are selected from a list of 3102 Experts worldwide ranked by ideXlab platform

Deniz Kilinc - One of the best experts on this subject based on the ideXlab platform.

  • machine learning based security analysis alarm generation and Threat Forecasting
    International Journal of Engineering, 2020
    Co-Authors: Fatma Bozyiğit, Okan Turksever, Deniz Kilinc
    Abstract:

    Log files keep activity records of each process performed have an important place in terms of security. Systems that provide infrastructure for applications such as network security mainly work on log management. Recently, when the security mechanisms of popular applications are examined, it has been observed that they aim to strengthen their infrastructures with machine learning (ML) methods, but in some respects, they have shortcomings. In this study, we aim to develop an alarm and security reporting system using ML methods. Our study differs from the others since it considers five separate feature (IP reputation, web reputation, malware destination access, botnet) and includes them into ML model.

Okan Turksever - One of the best experts on this subject based on the ideXlab platform.

  • machine learning based security analysis alarm generation and Threat Forecasting
    International Journal of Engineering, 2020
    Co-Authors: Fatma Bozyiğit, Okan Turksever, Deniz Kilinc
    Abstract:

    Log files keep activity records of each process performed have an important place in terms of security. Systems that provide infrastructure for applications such as network security mainly work on log management. Recently, when the security mechanisms of popular applications are examined, it has been observed that they aim to strengthen their infrastructures with machine learning (ML) methods, but in some respects, they have shortcomings. In this study, we aim to develop an alarm and security reporting system using ML methods. Our study differs from the others since it considers five separate feature (IP reputation, web reputation, malware destination access, botnet) and includes them into ML model.

Fatma Bozyiğit - One of the best experts on this subject based on the ideXlab platform.

  • machine learning based security analysis alarm generation and Threat Forecasting
    International Journal of Engineering, 2020
    Co-Authors: Fatma Bozyiğit, Okan Turksever, Deniz Kilinc
    Abstract:

    Log files keep activity records of each process performed have an important place in terms of security. Systems that provide infrastructure for applications such as network security mainly work on log management. Recently, when the security mechanisms of popular applications are examined, it has been observed that they aim to strengthen their infrastructures with machine learning (ML) methods, but in some respects, they have shortcomings. In this study, we aim to develop an alarm and security reporting system using ML methods. Our study differs from the others since it considers five separate feature (IP reputation, web reputation, malware destination access, botnet) and includes them into ML model.

John Pirc - One of the best experts on this subject based on the ideXlab platform.

  • Threat Forecasting: Leveraging Big Data for Predictive Analysis
    2016
    Co-Authors: John Pirc, David Desanto, Iain Davison
    Abstract:

    Drawing upon years of practical experience and using numerous examples and illustrative case studies, Threat Forecasting: Leveraging Big Data for Predictive Analysis discusses important topics, including the danger of using historic data as the basis for predicting future breaches, how to use security intelligence as a tool to develop Threat Forecasting techniques, and how to use Threat data visualization techniques and Threat simulation tools. Readers will gain valuable security insights into unstructured big data, along with tactics on how to use the data to their advantage to reduce risk.Presents case studies and actual data to demonstrate Threat data visualization techniques and Threat simulation toolsExplores the usage of kill chain modelling to inform actionable security intelligenceDemonstrates a methodology that can be used to create a full Threat forecast analysis for enterprise networks of any size

  • 2 Threat Forecasting
    Threat Forecasting#R##N#Leveraging Big Data for Predictive Analysis, 2016
    Co-Authors: John Pirc
    Abstract:

    In this chapter you will learn about the high-level concepts that are associated with big data collection and how they are applied to Threat Forecasting. You will learn how the similarities of weather Forecasting, epidemiology, and high frequency trading algorithms play an important role in Threat Forecasting. You will be introduced to concepts that play a greater role in Chapter 3 and beyond, all of which influence the process of Forecasting and predicting Threat.

Bhaarath Venkateswaran - One of the best experts on this subject based on the ideXlab platform.

  • adaptive internet Threat Forecasting an insight into the world of internet and network security Threat Forecasting using ids ips systems
    2011
    Co-Authors: Bhaarath Venkateswaran
    Abstract:

    Intrusion Prevention Systems (IPS) plays a key role in safeguarding todays data networks. The security effectiveness and performance of these systems are the primary concerns while deploying them inline. In this book we try to address these concerns by taking a pragmatic approach in benchmarking this current generation IPSs using open source tools, techniques and methodology. This approach, we hope will help the network & security administrators to effectively and efficiently secure their corporate network saving their organization significant resources. The book also focuses on modeling an Intrusion Forecasting system(IFS)having the ability to forecast multiple internet Threats. This is performed by integrating our benchmarked contemporary IPS solution into an architecture scheme based on honeynets. This model which will not only have the ability to act as an early Threat warning system to internet security Threats but also adapt and take preventive actions against them, hence effectively protecting corporate assets with very minimal manual intervention.In short, we hope that this book will lay a strong foundation for the evolution of next generation Threat prevention products