activity
20182021
most citedPattern Discovery in Time Series with Byte Pair Encoding

3 citations · 3 across the 2 of their papers we have counts for

collaborators

5 papers

eess.SP20213 cited

Pattern Discovery in Time Series with Byte Pair Encoding

Nazgol Tavabi, Kristina Lerman

The growing popularity of wearable sensors has generated large quantities of temporal physiological and activity data. Ability to analyze this data offers new opportunities for rea…

cs.CR2020

Challenges in Forecasting Malicious Events from Incomplete Data

Nazgol Tavabi, Andrés Abeliuk, Negar Mokhberian +2

The ability to accurately predict cyber-attacks would enable organizations to mitigate their growing threat and avert the financial losses and disruptions they cause. But how predi…

cs.LG2019

Learning Behavioral Representations from Wearable Sensors

Nazgol Tavabi, Homa Hosseinmardi, Jennifer L. Villatte +4

Continuous collection of physiological data from wearable sensors enables temporal characterization of individual behaviors. Understanding the relation between an individual's beha…

cs.CY2019

Characterizing Activity on the Deep and Dark Web

Nazgol Tavabi, Nathan Bartley, Andrés Abeliuk +3

The deep and darkweb (d2web) refers to limited access web sites that require registration, authentication, or more complex encryption protocols to access them. These web sites serv…

cs.SI2018

Discovering Signals from Web Sources to Predict Cyber Attacks

Palash Goyal, KSM Tozammel Hossain, Ashok Deb +5

Cyber attacks are growing in frequency and severity. Over the past year alone we have witnessed massive data breaches that stole personal information of millions of people and wide…