際際滷shows by User: AndrewBeard1 / http://www.slideshare.net/images/logo.gif 際際滷shows by User: AndrewBeard1 / Sun, 28 Jan 2018 17:41:41 GMT 際際滷Share feed for 際際滷shows by User: AndrewBeard1 HF Digital Modes /slideshow/hf-digital-modes/86814341 hfdigitalmodes-180128174141
A light introduction to HF digital communication modes used in amateur radio. Presented to the HacDC amateur radio club.]]>
A light introduction to HF digital communication modes used in amateur radio. Presented to the HacDC amateur radio club.]]> Sun, 28 Jan 2018 17:41:41 GMT /slideshow/hf-digital-modes/86814341 AndrewBeard1@slideshare.net(AndrewBeard1) HF Digital Modes AndrewBeard1 A light introduction to HF digital communication modes used in amateur radio. Presented to the HacDC amateur radio club. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/hfdigitalmodes-180128174141-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> A light introduction to HF digital communication modes used in amateur radio. Presented to the HacDC amateur radio club.
HF Digital Modes from Andrew Beard
]]> 291 2 https://cdn.slidesharecdn.com/ss_thumbnails/hfdigitalmodes-180128174141-thumbnail.jpg?width=120&height=120&fit=bounds presentation Black http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 Introduction to Amateur DMR /slideshow/introduction-to-amateur-dmr-86813941/86813941 dmr-2018-180128172800
An overview of Digital Mobile Radio (DMR) for amateur radio operators. Presented to the Laurel Amateur Radio Club (LARC) on 1/24/2018.]]>
An overview of Digital Mobile Radio (DMR) for amateur radio operators. Presented to the Laurel Amateur Radio Club (LARC) on 1/24/2018.]]> Sun, 28 Jan 2018 17:28:00 GMT /slideshow/introduction-to-amateur-dmr-86813941/86813941 AndrewBeard1@slideshare.net(AndrewBeard1) Introduction to Amateur DMR AndrewBeard1 An overview of Digital Mobile Radio (DMR) for amateur radio operators. Presented to the Laurel Amateur Radio Club (LARC) on 1/24/2018. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/dmr-2018-180128172800-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> An overview of Digital Mobile Radio (DMR) for amateur radio operators. Presented to the Laurel Amateur Radio Club (LARC) on 1/24/2018.
Introduction to Amateur DMR from Andrew Beard
]]> 507 3 https://cdn.slidesharecdn.com/ss_thumbnails/dmr-2018-180128172800-thumbnail.jpg?width=120&height=120&fit=bounds presentation Black http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 Introduction to Amateur DMR /slideshow/introduction-to-amateur-dmr/71663812 dmr-170202031943
An overview of Digital Mobile Radio (DMR) for amateur radio operators]]>
An overview of Digital Mobile Radio (DMR) for amateur radio operators]]> Thu, 02 Feb 2017 03:19:42 GMT /slideshow/introduction-to-amateur-dmr/71663812 AndrewBeard1@slideshare.net(AndrewBeard1) Introduction to Amateur DMR AndrewBeard1 An overview of Digital Mobile Radio (DMR) for amateur radio operators <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/dmr-170202031943-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> An overview of Digital Mobile Radio (DMR) for amateur radio operators
Introduction to Amateur DMR from Andrew Beard
]]> 3388 20 https://cdn.slidesharecdn.com/ss_thumbnails/dmr-170202031943-thumbnail.jpg?width=120&height=120&fit=bounds presentation Black http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 Detecting Malicious Websites using Machine Learning /slideshow/detecting-malicious-websites-using-machine-learning-67592863/67592863 detectingmaliciouswebsitesusingmachinelearning-161024171340
We present a set of newly tuned algorithms that can distinguish between malicious and non-malicious websites with a high degree of accuracy using Machine Learning (ML). We use the Bro IDS/IPS tool for extracting the SSL certificates from network traffic and training the ML algorithms. The extracted SSL attributes are then loaded into multiple ML frameworks such as Splunk, AWS ML and we run a series of classification algorithms to identify those attributes that correlate with malicious sites. Our analysis shows that there are a number of emerging patterns that even allow for identification of high-jacked devices and self-signed certificates. We present the results of our analysis which show which attributes are the most relevant for detecting malicious SSL certificates and as well the performance of the ML algorithms. ]]>
We present a set of newly tuned algorithms that can distinguish between malicious and non-malicious websites with a high degree of accuracy using Machine Learning (ML). We use the Bro IDS/IPS tool for extracting the SSL certificates from network traffic and training the ML algorithms. The extracted SSL attributes are then loaded into multiple ML frameworks such as Splunk, AWS ML and we run a series of classification algorithms to identify those attributes that correlate with malicious sites. Our analysis shows that there are a number of emerging patterns that even allow for identification of high-jacked devices and self-signed certificates. We present the results of our analysis which show which attributes are the most relevant for detecting malicious SSL certificates and as well the performance of the ML algorithms. ]]> Mon, 24 Oct 2016 17:13:40 GMT /slideshow/detecting-malicious-websites-using-machine-learning-67592863/67592863 AndrewBeard1@slideshare.net(AndrewBeard1) Detecting Malicious Websites using Machine Learning AndrewBeard1 We present a set of newly tuned algorithms that can distinguish between malicious and non-malicious websites with a high degree of accuracy using Machine Learning (ML). We use the Bro IDS/IPS tool for extracting the SSL certificates from network traffic and training the ML algorithms. The extracted SSL attributes are then loaded into multiple ML frameworks such as Splunk, AWS ML and we run a series of classification algorithms to identify those attributes that correlate with malicious sites. Our analysis shows that there are a number of emerging patterns that even allow for identification of high-jacked devices and self-signed certificates. We present the results of our analysis which show which attributes are the most relevant for detecting malicious SSL certificates and as well the performance of the ML algorithms. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/detectingmaliciouswebsitesusingmachinelearning-161024171340-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> We present a set of newly tuned algorithms that can distinguish between malicious and non-malicious websites with a high degree of accuracy using Machine Learning (ML). We use the Bro IDS/IPS tool for extracting the SSL certificates from network traffic and training the ML algorithms. The extracted SSL attributes are then loaded into multiple ML frameworks such as Splunk, AWS ML and we run a series of classification algorithms to identify those attributes that correlate with malicious sites. Our analysis shows that there are a number of emerging patterns that even allow for identification of high-jacked devices and self-signed certificates. We present the results of our analysis which show which attributes are the most relevant for detecting malicious SSL certificates and as well the performance of the ML algorithms.
Detecting Malicious Websites using Machine Learning from Andrew Beard
]]> 1108 3 https://cdn.slidesharecdn.com/ss_thumbnails/detectingmaliciouswebsitesusingmachinelearning-161024171340-thumbnail.jpg?width=120&height=120&fit=bounds presentation Black http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 Detecting Malicious SSL Certificates Using Bro /slideshow/detecting-malicious-ssl-certificates-using-bro/67592558 brocon-detectingmalicioussslcertsusingbro-161024170553
We have developed a set of techniques to detect malicious SSL certificates using data collected by Bro. Our analysis framework consists of Bro for collecting the data and a variety of tools such as Splunk and AWS ML for data analysis. We show how we used Bro for collecting the attributes we needed for SSL certificates from both good and bad sources. Bro is a very effective and simple tool for analyzing and extracting data from network traffic. Next, the extracted data was loaded into Splunk and we ran a series of Machine Learning algorithms to identify those attributes that correlated with malicious activity. The algorithms we used also allowed for categorization of certificates used in the delivery and control of malware. Our analysis showed that there were a number of patterns that emerged that allowed for classification of high-jacked devices, self-signed certificates, etc. We will present the results of our analysis which show which attributes are the most relevant for detecting malicious SSL certificates and as well the performance of the ML algorithms. Finally, we show how well the training has worked in detecting new malicious sources. All of the source code will be made available on github.]]>
We have developed a set of techniques to detect malicious SSL certificates using data collected by Bro. Our analysis framework consists of Bro for collecting the data and a variety of tools such as Splunk and AWS ML for data analysis. We show how we used Bro for collecting the attributes we needed for SSL certificates from both good and bad sources. Bro is a very effective and simple tool for analyzing and extracting data from network traffic. Next, the extracted data was loaded into Splunk and we ran a series of Machine Learning algorithms to identify those attributes that correlated with malicious activity. The algorithms we used also allowed for categorization of certificates used in the delivery and control of malware. Our analysis showed that there were a number of patterns that emerged that allowed for classification of high-jacked devices, self-signed certificates, etc. We will present the results of our analysis which show which attributes are the most relevant for detecting malicious SSL certificates and as well the performance of the ML algorithms. Finally, we show how well the training has worked in detecting new malicious sources. All of the source code will be made available on github.]]> Mon, 24 Oct 2016 17:05:53 GMT /slideshow/detecting-malicious-ssl-certificates-using-bro/67592558 AndrewBeard1@slideshare.net(AndrewBeard1) Detecting Malicious SSL Certificates Using Bro AndrewBeard1 We have developed a set of techniques to detect malicious SSL certificates using data collected by Bro. Our analysis framework consists of Bro for collecting the data and a variety of tools such as Splunk and AWS ML for data analysis. We show how we used Bro for collecting the attributes we needed for SSL certificates from both good and bad sources. Bro is a very effective and simple tool for analyzing and extracting data from network traffic. Next, the extracted data was loaded into Splunk and we ran a series of Machine Learning algorithms to identify those attributes that correlated with malicious activity. The algorithms we used also allowed for categorization of certificates used in the delivery and control of malware. Our analysis showed that there were a number of patterns that emerged that allowed for classification of high-jacked devices, self-signed certificates, etc. We will present the results of our analysis which show which attributes are the most relevant for detecting malicious SSL certificates and as well the performance of the ML algorithms. Finally, we show how well the training has worked in detecting new malicious sources. All of the source code will be made available on github. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/brocon-detectingmalicioussslcertsusingbro-161024170553-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> We have developed a set of techniques to detect malicious SSL certificates using data collected by Bro. Our analysis framework consists of Bro for collecting the data and a variety of tools such as Splunk and AWS ML for data analysis. We show how we used Bro for collecting the attributes we needed for SSL certificates from both good and bad sources. Bro is a very effective and simple tool for analyzing and extracting data from network traffic. Next, the extracted data was loaded into Splunk and we ran a series of Machine Learning algorithms to identify those attributes that correlated with malicious activity. The algorithms we used also allowed for categorization of certificates used in the delivery and control of malware. Our analysis showed that there were a number of patterns that emerged that allowed for classification of high-jacked devices, self-signed certificates, etc. We will present the results of our analysis which show which attributes are the most relevant for detecting malicious SSL certificates and as well the performance of the ML algorithms. Finally, we show how well the training has worked in detecting new malicious sources. All of the source code will be made available on github.
Detecting Malicious SSL Certificates Using Bro from Andrew Beard
]]> 4143 7 https://cdn.slidesharecdn.com/ss_thumbnails/brocon-detectingmalicioussslcertsusingbro-161024170553-thumbnail.jpg?width=120&height=120&fit=bounds presentation Black http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 I See You /slideshow/i-see-you-51446365/51446365 iseeyou-150810044658-lva1-app6892
In this talk, we will dive into the data captured during last years Wall of Sheep applications and protocols that are giving your away credentials. This is something that anyone, with the right level of knowledge and inclination, could certainly do with a few basic ingredients. We will enumerate them. The dataset we will focus on was gathered as part of the Wall of Sheep contest during DEF CON 22. While this data was gathered using an off the shelf technology, that platform will not be the topic we discuss. Rather, we will focus on the types and scope of data sent totally in the clear for all to see. Additionally, we will discuss the ramifications this might have in a less "friendly" environment --where loss of one's anonymity, might really, really suck. Finally, we will discuss and recommend ways you can hamper this type of collection.]]>
In this talk, we will dive into the data captured during last years Wall of Sheep applications and protocols that are giving your away credentials. This is something that anyone, with the right level of knowledge and inclination, could certainly do with a few basic ingredients. We will enumerate them. The dataset we will focus on was gathered as part of the Wall of Sheep contest during DEF CON 22. While this data was gathered using an off the shelf technology, that platform will not be the topic we discuss. Rather, we will focus on the types and scope of data sent totally in the clear for all to see. Additionally, we will discuss the ramifications this might have in a less "friendly" environment --where loss of one's anonymity, might really, really suck. Finally, we will discuss and recommend ways you can hamper this type of collection.]]> Mon, 10 Aug 2015 04:46:58 GMT /slideshow/i-see-you-51446365/51446365 AndrewBeard1@slideshare.net(AndrewBeard1) I See You AndrewBeard1 In this talk, we will dive into the data captured during last years Wall of Sheep applications and protocols that are giving your away credentials. This is something that anyone, with the right level of knowledge and inclination, could certainly do with a few basic ingredients. We will enumerate them. The dataset we will focus on was gathered as part of the Wall of Sheep contest during DEF CON 22. While this data was gathered using an off the shelf technology, that platform will not be the topic we discuss. Rather, we will focus on the types and scope of data sent totally in the clear for all to see. Additionally, we will discuss the ramifications this might have in a less "friendly" environment --where loss of one's anonymity, might really, really suck. Finally, we will discuss and recommend ways you can hamper this type of collection. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/iseeyou-150810044658-lva1-app6892-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> In this talk, we will dive into the data captured during last years Wall of Sheep applications and protocols that are giving your away credentials. This is something that anyone, with the right level of knowledge and inclination, could certainly do with a few basic ingredients. We will enumerate them. The dataset we will focus on was gathered as part of the Wall of Sheep contest during DEF CON 22. While this data was gathered using an off the shelf technology, that platform will not be the topic we discuss. Rather, we will focus on the types and scope of data sent totally in the clear for all to see. Additionally, we will discuss the ramifications this might have in a less &quot;friendly&quot; environment --where loss of one&#39;s anonymity, might really, really suck. Finally, we will discuss and recommend ways you can hamper this type of collection.
I See You from Andrew Beard
]]> 1564 5 https://cdn.slidesharecdn.com/ss_thumbnails/iseeyou-150810044658-lva1-app6892-thumbnail.jpg?width=120&height=120&fit=bounds presentation Black http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 https://cdn.slidesharecdn.com/profile-photo-AndrewBeard1-48x48.jpg?cb=1523154160 Specialties: Embedded development https://cdn.slidesharecdn.com/ss_thumbnails/hfdigitalmodes-180128174141-thumbnail.jpg?width=320&height=320&fit=bounds slideshow/hf-digital-modes/86814341 HF Digital Modes https://cdn.slidesharecdn.com/ss_thumbnails/dmr-2018-180128172800-thumbnail.jpg?width=320&height=320&fit=bounds slideshow/introduction-to-amateur-dmr-86813941/86813941 Introduction to Amateu... https://cdn.slidesharecdn.com/ss_thumbnails/dmr-170202031943-thumbnail.jpg?width=320&height=320&fit=bounds slideshow/introduction-to-amateur-dmr/71663812 Introduction to Amateu...