Lumina Networks Raises $10M With Investments From Both Verizon and AT&T
It will spend some of its funding to expand its business in Europe and in Japan.
6 Lessons Network Security Teams Can Learn from Star Wars
The classic science-fiction film trilogy provides valuable lessons for enterprise security teams on how to protect the network and sensitive corporate data.
ONIE and the Hammer of Thor
Someone left a comment on my Zero-Touch Provisioning post claiming how Big Switch Networks solved ZTP challenge using just IPv6 Link-Local Address and Neighbor Discovery instead of the complicated DHCP/TFTP/whatever sequence.
Here’s what he wrote:
Read more ...Algorithmic glass ceiling in social networks: the effects of social recommendations on network diversity
Algorithmic glass ceiling in social networks: the effects of social recommendations on network diversity Stoica et al., WWW’18
(If you don’t have ACM Digital Library access, the paper can be accessed either by following the link above directly from The Morning Paper blog site, or from the WWW 2018 proceedings page).
Social networks were meant to connect us and bring us together. This paper shows that while they might be quite successful at doing this in the small, on a macro scale they’re actually doing the opposite. Not only do they reinforce and sustain disparities among groups, but they actually reinforce the rate at which disparity grows. I.e., they’re driving us apart. This happens due to the rich-get-richer phenomenon resulting from friend/follow recommendation algorithms.
… we find that prominent social recommendation algorithms can exacerbate the under-representation of certain demographic groups at the top of the social hierarchy… Our mathematical analysis demonstrates the existence of an algorithmic glass ceiling that exhibits all the properties of the metaphorical social barrier that hinders groups like women or people of colour from attaining equal representation.
Organic growth vs algorithmic growth
“In the social networks now governing the knowledge, Continue reading
Quick Post: Parsing AWS Instance Data with JQ
I recently had a need to get a specific subset of information about some AWS instances. Naturally, I turned to the CLI and some CLI tools to help. In this post, I’ll share the command I used to parse the AWS instance data down using the ever-so-handy jq tool.
What I needed, specifically, was the public IP address and the private IP address for each instance. That information is readily accessible using the aws ec2 describe-instances command, but that command provides a ton more information than I needed. So, I decided to try to use jq to parse the JSON output from the AWS CLI. If you’re not familiar with jq, I recommend you take a look at this brief introductory post I wrote back in 2015.
After some trial and error, here’s the final command I used:
aws ec2 describe-instances | jq '.Reservations[] | .Instances[] | \
{Id: .InstanceId, PublicAddress: .PublicIpAddress, \
PrivateAddress: .PrivateIpAddress}'
I’ll refer you to the jq manual for details on breaking down how this filter works. I’ll also point out that there’s nothing terribly groundbreaking or revolutionary about this command; I wanted to share it here just in case it may save someone Continue reading
The devil wears Pravda
Classic Bond villain, Elon Musk, has a new plan to create a website dedicated to measuring the credibility and adherence to "core truth" of journalists. He is, without any sense of irony, going to call this "Pravda". This is not simply wrong but evil.
Musk has a point. Journalists do suck, and many suck consistently. I see this in my own industry, cybersecurity, and I frequently criticize them for their suckage.
But what he's doing here is not correcting them when they make mistakes (or what Musk sees as mistakes), but questioning their legitimacy. This legitimacy isn't measured by whether they follow established journalism ethics, but whether their "core truths" agree with Musk's "core truths".
An example of the problem is how the press fixates on Tesla car crashes due to its "autopilot" feature. Pretty much every autopilot crash makes national headlines, while the press ignores the other 40,000 car crashes that happen in the United States each year. Musk spies on Tesla drivers (hello, classic Bond villain everyone) so he can see the dip in autopilot usage every time such a news story breaks. He's got good reason to be concerned about this.
He argues that autopilot is safer Continue reading
HCI, Storage Revenue, and HPE Next Cost Cuts Boost HPE’s Bottom Line
The company saved about $83 million over the first half of fiscal 2018 though its HPE Next cost-cutting initiative.
Cato Networks Adds Threat Hunting Capability To Its SD-WAN Service
Cato Networks adds a threat-hunting capability to its SD-WAN service to detect compromised devices on customer networks.EMA Report: Network Management Teams Shift Focus to SD-WAN, SDN, and SDDC
SD-WAN is priority for enterprises that want to make their networks more automated.
Kata Containers 1.0 Launch Hones In On Security, Interoperability Fears
The project claims greater security than traditional containers by tapping into virtual machine schema but remains compatible with Docker and Kubernetes in the container ecosystem.
Regular Expression for Network Engineer Part-2
This post is continuation of the Regular Expression for Network Engineer Part-1 , here we have a look for the different methods to find out the pattern in string.
Findall() – returns list of all the matches the pattern in a string without overlapping
- EXAMPLE
[code language = “Python”]
re.findall(pattern, string[, flags])
In [118]: ip
Out[118]: ‘10.10.1.10,29.10.1.10,10.10.1.20,192.168.1.0,172.16.10.1,10.10.10.121’
In [119]: out= re.findall(r'(10.10.10.\d+)’ ,ip)
In [120]: out
Out[120]: [‘10,10.10.1’, ‘10.10.10.121’]
#Above example help us to find out all the IP’s of subnet 10.10.10.0/24 from group of ip’s.
[/code]
Match()-return a match object when pattern is found at the beginning of string, if no pattern is found ,result in None.
- EXAMPLE
[code language = “Python”]
In [189]: text
Out[189]: ‘Cisco IOS Software, 7200 Software (C7200-SPSERVICESK9-M), Version 12.2(33)SRE, RELEASE SOFTWARE (fc1)’
In [190]: out = re.match(r”Cisco”,text)
In [191]: out
Out[191]: <_sre.SRE_Match object; span=(0, 5), match=’Cisco’>
In [192]: out = re.match(r” Software”,text)
In [193]: out
In [194]: out = re.search(r” Software”,text)
In [195]: out
Out[195]: <_sre.SRE_Match object; span=(9, 18), Continue reading