Day: April 21, 2021

Tara Salman, Washington Univ., to Present Talk April 27

Tara Salman, a final-year PhD candidate at Washington University in St. Louis, will present a talk on Tuesday, April 27, 2021, at 3:00 p.m.

In her talk, “A Collaborative Knowledge-Based Security Solution using Blockchains,” she will present her work on redesigning the blockchains and building a collaborative, distributed, intelligent, and hostile solution that can be used for security purposes.

Talk Title

A Collaborative Knowledge-Based Security Solution using Blockchains

Talk Abstract

Artificial intelligence and machine learning have recently gained wide adaptation in building intelligent yet simple and proactive security solutions such as intrusion identification, malware detection, and threat intelligence. With the increased risk and severity of cyber-attacks and the distributed nature of modern threats and vulnerabilities, it becomes critical to pose a distributed intelligent solution that evaluates the systems’ and networks’ security collaboratively. Blockchain, as a decade-old successful distributed ledger technology, has the potential to build such collaborative solutions. However, to be used for such solutions, the technology needs to be extended so that it can intelligently process the stored information and achieve a collective decision about security risks or threats that might target a system.

In this talk, I will present our work on redesigning the blockchains and build a collaborative, distributed, intelligent, and hostile solution that can be used for security purposes. In particular, we will discuss our work on (1) extending blockchains for general collaborative decision-making applications, where knowledge should be made out of decisions, risks, or any information stored on the blockchain; (2) applying the proposed extensions to security applications such as malware detection and threat intelligence.

Biography

Tara Salman is a final year Ph.D. candidate at Washington University in St. Louis, where she is advised by Raj Jain. She previously received her MS and BSc degrees from Qatar University in 2015 and 2012, respectively. Her research aims to integrate state-of-the-art technologies to provide scalable, collaborative, and intelligent cybersecurity solutions.

Her recent work focuses on the intersection of artificial intelligence, blockchains, and security applications. The work spans several fields, including blockchain technology, security, machine learning, and deep learning applications, cloud computing, and the Internet of Things. She has been selected for the EECS Rising Star in UC Berkeley 2020. Her research has been published in more than twenty internationally recognized conferences and journals and supported by national and international funds.


Dean’s Teaching Showcase, Todd Arney, Applied Computing


by Michael R. Meyer – Director, William G. Jackson CTL

Dennis Livesay , Dean of the College of Computing, has selected Todd Arney, Senior Lecturer in Applied Computing, as our twelfth-week Deans’ Teaching Showcase member.

Arney, an inaugural winner of the Provost’s Award for Sustained Teaching Excellence in 2020, has a long record of outstanding teaching. But, this time, Applied Computing Chair Dan Fuhrmann, while acknowledging that Todd continues to teach a “substantial load” at an “exceptionally high level of quality,” recommended Arney for his behind-the-scenes “efforts to modernize the curricula in the Department of Applied Computing, and to enhance the use of state-of-the-art computing resources across campus, through the use of our new Virtual Cluster.”

Fuhrmann notes the changes in instruction required by the pandemic made Arney’s work a particular “godsend” because it enabled remote teaching. But he emphasizes that “it facilitated a vast improvement in student experience, in comparison to the aging educational computing hardware in the Computer Network and Systems Administration program that preceded it.”

Fuhrmann calls Arney an “evangelist” for the Virtual Cluster and notes that in addition to its implementation within the CNSA and Cybersecurity programs, Arney has made special efforts to reach out to the Department of Civil and Environmental Engineering, bringing a modern computing framework to one of their senior/graduate courses, CEE 4610/5610 (Water Resources System Modeling and Design).

He also worked with AC Academic Advisor Kay Oliver, the instructor for SAT 1090 (Introduction to Applied Computing), to provide introductions on cybersecurity and privacy frameworks for the students to use as a common language for their group work discussions on project design using micro:bit hardware to solve real-world problems.

Currently, Arney is working on additional collaborations with Mechatronics faculty, two senior design projects, and two new faculty members in the College of Computing to help support their courses using the cluster. Fuhrmann emphasizes that “Bringing new resources into our educational programs does not happen overnight, and it does not happen without knowledgeable, dedicated faculty members who see the potential and who make the necessary effort to upgrade the curriculum to take advantage of those resources. Todd Arney is that person in the Department of Applied Computing.”

In choosing Arney, Dean Livesay heartily agrees, noting, “Ensuring that our students have access to the latest technology is time-consuming and represents work that isn’t acknowledged as regularly as it should be. As such, we’re especially proud to recognize Todd’s accomplishments in deploying virtual machines broadly in our classes, and helping others do the same in theirs.”

Arney will be recognized at an end-of-term event with other showcase members, and is also a candidate for the CTL Instructional Award Series (to be determined this summer) recognizing introductory or large-class teaching, innovative or outside the classroom teaching methods, or work in curriculum and assessment.


New Course: Applied Machine Learning


Summary

  • Course Number: 84859, EET 4996-01
  • Class Times: T/R, 9:30-10:45 am
  • Location: EERC 0723
  • Instructor: Dr. Sidike Paheding
  • Course Levels: Graduate, Undergraduate
  • Prerequisite: Python Programming and basic knowledge of statistics.
  • Preferred knowledge: Artificial Intelligence (CS 4811) or Data Mining (CS4821) or Intro to Data Sciences (UN 5550)

Course Description/Overview

Rapid growth and remarkable success of machine learning can be witnessed by tremendous advances in technology, contributing to the fields of healthcare, finance, agriculture, energy, education, transportation and more. This course will emphasize on intuition and real-world applications of Machine Learning (ML) rather than statistics behind it. Key concepts of some popular ML techniques, including deep learning, along with hands-on exercises will be provided to students. By the end of this course, students will be able to apply a variety of ML algorithms to practical

Instructor

Applications Covered

  • Object Detection
  • Digital Recognition
  • Face Recognition
  • Self-Driving Cars
  • Medical Image Segmentation
  • Covid-19 Prediction
  • Spam Email Detection
  • Spectral Signal Categorization

Tools Covered

  • Python
  • scikit learn
  • TensorFlow
  • Keras
  • Open CV
  • pandas
  • matplotlib
  • NumPy
  • seaborn
  • ANACONDA
  • jupyter
  • SPYDER

Download the course description flyer:

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