Smart Communities

AvidBeam offers innovative video analytics solutions for face recognition, traffic violation, license plate recognition, behavior analysis, and anomaly detection

#computervision #videosearch #facerecognition #arabiclicenseplaterecognition #licenseplaterecognition #behavioranalysis #anomalydetection

Visit Us:- https://www.avidbeam.com/products/

Referenced link: https://hackernoon.com/visualizing-real-time-anomaly-detection-with-python
Discuss on https://discu.eu/q/https://hackernoon.com/visualizing-real-time-anomaly-detection-with-python

Originally posted by HackerNoon | Learn Any Technology / @hackernoon: http://nitter.platypush.tech/hackernoon/status/1649402614872719365#m

Rerun, combined with Bytewax, provides a powerful approach to visualizing streaming data in pure Python in real-time - https://hackernoon.com/visualizing-real-time-anomaly-detection-with-python #anomalydetection #python

Proposing a privacy-friendly water leakage detection approach for various kind of water meters (optical and digital) performed on a very constrained, wireless devices in: “Anomaly detection on compressed data in resource-constrained smart water meters“ by Sarah Klein, Anna Hristoskova, Annanda Rath, Renaud Gonce. Proceedings of the 17th Conference on Computer Science and Intelligence Systems: ACSIS, Vol. 30, pages 635–639 (2022).
#IoT #anomalydetection
Open Access: https://tinyurl.com/2p9j8j36

Calling all data wizards! Join us for a weekend of innovation and problem-solving at our anomaly detection hackathon. Show off your skills, learn new ones, and win awesome prizes. Register now at https://www.meetup.com/apache-pinot/events/290826222/ #anomalydetection #hackathon #datascience

Check out my latest note: Calculating the Harris index with Python libraries https://linkedin.com/pulse/calculating-harris-index-python-libraries-gustavo-sanchez #machinelearning #neuralnetworks #edgeai #edgecomputing #tinyml #anomalydetection
#predictivemaintenance #conditionmonitoring
#timeseriesanalysis #signalprocessing #controlsystems

Anyone have a good resources on statistical analysis of pcap data for anomaly detection?

I made a Raspberry Pi WAP specifically for IoT devices to connect to. My intention was to use it for capturing pcaps of IoT traffic to look for vulnerabilities or discover firmware download sites. So far it’s boring because everything is MQTT. However, the subject of anomalous network activity detection seems intriguing to me. My wife’s a statistician and has been looking for a reason to learn R so I feel like between the two of us this may be a fun family project 🙃. Unfortunately, I literally have no idea where to start. Resources I’ve found with search terms that include “network” are more about social graph networking and not packets. Anyone have any thoughts? Books, scholarly articles, blogs, anything will help. Thanks!
#iot #anomalydetection #statistics #threathunting

Anyone have a good resources on statistical analysis of pcap data for anomaly detection?

I made a Raspberry Pi WAP specifically for IoT devices to connect to. My intention was to use it for capturing pcaps of IoT traffic to look for vulnerabilities or discover firmware download sites. So far it’s boring because everything is MQTT. However, the subject of anomalous network activity detection seems intriguing to me. My wife’s a statistician and had been looking for a reason to learn R so I feel like between the two of us this may be a fun family project 🙃. Unfortunately, I literally have no idea where to start. Resources I’ve found with search terms that include “network” are more about social graph networking and not packets. Anyone have any thoughts? Books, scholarly articles, blogs, anything will help. Thanks!
#iot #anomalydetection #statistics #threathunting

If you ever used our @stratosphere malware datasets, we have good news! Under my direction, we will be actively working on improving all our datasets to make them better and more accessible to the community.

These datasets are unique in many ways and we look to make them even better.

If you have suggestions, ideas, would-be-nice comments, use cases, or any comments in general we would love to hear about it!

These datasets were initially created to aid machine learning researchers in developing new and better models to identify malicious behaviors in the network. When we started, there were few datasets out there with real malware network traffic that lasted longer than just a few minutes.

https://mcfp.felk.cvut.cz/publicDatasets/

#networksecurity #malwaretraffic #machinelearning #datascience #cybersecurity #maliciousbehaviors #datasets #networktrafficanalysis #ml #anomalydetection

The number of #LiteratureReview papers is beginning to exceed the number of actual #research papers in some areas of #anomalyDetection.

#rstats #graph

Referenced link: https://hackernoon.com/3-types-of-anomalies-in-anomaly-detection
Discuss on https://discu.eu/q/https://hackernoon.com/3-types-of-anomalies-in-anomaly-detection

Originally posted by HackerNoon | Learn Any Technology / @hackernoon@twitter.com: https://twitter.com/hackernoon/status/1576001458121711616#m

"3 Types of Anomalies in Anomaly Detection" by @s_kampakis https://hackernoon.com/3-types-of-anomalies-in-anomaly-detection #machinelearning #anomalydetection

In the early 2000s, a #CS student and myself developed an #IDMEF/#IDXP compliant security event message pipelining framework for collecting and consolidating messages from network #IDS, and #EDR products.

In the messages stream, we were able to match multi-stage #correlation #DetectionRules in near real-time (in-memory), before everything was stored in central database. Structural graph-based #AnomalyDetection was developed later by some colleagues.

We called it #MetaIDS.