EE Seminar: Estimating the security level of cryptographic keys against side-channel attacks and using it to estimate a password strength

18 בנובמבר 2020, 15:00 
ZOOM  

https://us02web.zoom.us/j/88951206317?pwd=elpRWVRaUG5xUGpQaC9QRU5SUHI3UT09
Meeting ID: 889 5120 6317
Passcode: 476348
 

Speaker: Liron David

Ph.D student under the supervision of Prof. Avishai Wool

Wednesday, November 18th, 2020, at 15:00

 

Estimating the security level of cryptographic keys against side-channel attacks and using it to estimate a password strength

Abstract

Efficient rank estimation algorithms are of prime interest in security evaluation against side-channel attacks (SCA). They allow estimating the remaining security after an attack has been performed, quantified as the time complexity and the memory consumption required to brute force the key given the leakages as probability distributions over d subkeys (usually key bytes).

In this talk, I will show a novel rank estimation called ESrank. This is the first rank estimation algorithm with a bounded error ratio, which can be tuned to the desired accuracy. Its error ratio is bounded by g2d-2, for any probability distribution, where d is the number of subkey dimensions and g>1 can be chosen according to the desired accuracy. ESrank is also the first rank estimation algorithm that enjoys provable poly-logarithmic time- and space-complexity.  The ESrank's main idea is to use exponential sampling to drastically reduce the algorithm's complexity.

   Then I will show a novel password strength estimator based on ESrank, called PESrank, which accurately models the behavior of a powerful password cracker. Passwords strength estimators are used to help users avoid picking weak passwords by predicting how many attempts a password cracker would need until it finds a given password.  PESrank calculates the rank of a given password in an optimal descending order of likelihood in fractions of a second---without actually enumerating the passwords---so it is practical for online use. It also has a training time that is drastically shorter than previous methods. Moreover, PESrank is efficiently tweakable to allow model personalization in fractions of a second, without the need to retrain the model; and it is explainable: it is able to provide information on why the password has its calculated rank, and gives the user insight on how to pick a better password.

השתתפות בסמינר תיתן קרדיט שמיעה = עפ"י רישום שם מלא + מספר ת.ז.  בצ'אט

 

 

 

הנכם מוזמנים לסמינר של קרלו אנטונינו - מרצה אורח

21 בדצמבר 2020, 14:00 - 15:00 
הסמינר יתקיים בזום  
0
הנכם מוזמנים לסמינר של קרלו אנטונינו - מרצה אורח

 

 

"ZOOM" SEMINAR

School of Mechanical Engineering Seminar
Monday, December 21, 2020 at 14:00

 

Understanding and controlling icing on surfaces: not really a piece of cake

 

Dr. Carlo Antonini

Department of Materials Science

University of Milano-Bicocca

Abstract presentation

Icing has long been recognized as a serious hazard for safety and for functioning of systems in diverse areas such as transportation, power and communication systems, infrastructures, and even domestic or commercial appliances. The control of water behavior at the interface through the tailoring of surface wetting properties represents a great scientific and technological challenge and opportunity, which can be used as a strategy to control and mitigate ice formation on surfaces. During the lecture, I will present his research activities focused on understanding the interaction mechanism between liquid and solid surfaces in freezing conditions. Focus will be given to the effects of surface wetting and topography on ice nucleation, to the wetting behavior of superhydrophobic surfaces in icing conditions, and to ice adhesion, highlighting the role of environmental conditions where relevant. A brief discussion of practical issues, challenges, and innovations in using non-wetting coatings will also presented.

 

Short Bio

I received the BSc in Aerospace Engineering (2004) and the MSc in Aeronautical Engineering (2007), from Politecnico di Milano, Italy, and the PhD in Technologies for Energy and Environment from University of Bergamo (2011), Italy, with a thesis titled “Superhydrophobicity as a strategy against icing”. In 2012, I received support from the European Research Council (ERC) to join ETH Zurich, Switzerland, as a Marie Curie Fellow; I worked in the Laboratory of Thermodynamics in Emerging Technologies on the project “ICE2: ICEphobicity for severe ICing Environments”. In March 2015 I joined EMPA – Swiss Federal Laboratory for Material Science and Technology – as scientist, focusing on the control of surface wetting properties of cellulose-based materials for various engineering applications, ranging from liquid separation (oil remediation) to thermal insulation. Since 2017 I am the scientific advisor of ApiTech, an italian startup supporting innovation in SMEs. In September 2018 I joined the Department of Materials Science at the University of Bicocca-Milano (Italy), as tenure-track senior assistant professor, with the support of a Rita Levi Montalcini Fellowship. At UNIMIB I have founded the Surface Engineering and Fluid Interfaces Laboratory (SEFI Lab). I am coordinating the MSCA-ITN project SURFICE - Smart surface design for efficient ice protection and control (2021-2024), involving 13 PhD students, 7 universities and 4 companies.

 

Affliliation and contact info

Carlo Antonini, PhD

Department of Materials Science

University of Milano-Bicocca

U5 - Via Roberto Cozzi, 55, 20125 Milano MI

phone: +39 02 6448 5188

carlo.antonini@unimib.it

 

 

 

Links

https://en.unimib.it/carlo-antonini

SEFI Lab https://sefilab.mater.unimib.it/

SURFICE https://cordis.europa.eu/project/id/956703 

 

 

https://zoom.us/j/96584758181?pwd=WC9PMXdsYzJ3NFdEN2Q5ZUtOZEVjdz09 The meeting will be recorded and made available on the School’s site.

 

 

Analog & High voltage electronics engineer

  • Bsc or Msc in electronics.
  • Hands on experience in analog design and high voltage low noise circuits
  • Knowledge in molding technologies, corona measure or high voltage electron acceleration system – advantage
  • Analog Simulation capabilities (spice).  Electric filed strength tools- Advantage
  • Capabilities of system view and leading tasks from start to finish.
  • Team player.
« חזרה לכל המשרות

Mix Digital-Analog Engineer

  • Electronics engineer (BSC). MSC is an advantage.
  • 4~8 years in analog circuits development on the areas described below.
  • Experience on development and integration of the modules in industrial system- Advantage.
  • Main technical expertise areas:
  • Development of digital board design, high speed, FPGA, Memory [DDR] and interface to high end Analog circuits.
  • Experience in Signal integrity simulation, SerDes
  • Experience in board design, schematics and layout tools (OrCad, CST, Allegro..)
« חזרה לכל המשרות

Analog Manager

  • Electronics engineer (BSC). MSC is an advantage.
  • 4~8 years in analog circuits development (hands on)
  • At least 5 years of proven experience as a manager of an analog group.
  • Experience on development and integration of the modules in industrial system- Advantage.
  • Experience in Development of analog board design, high speed, low noise op amp, filters, A2D and D2A circuits.
  • Experience in analog and system simulation
  • Soft skills and decision making
  • Creativity and proactive approach.
« חזרה לכל המשרות

הזמנה לסמינר Optics Outreach של תא הסטודנטים של OSA באוניברסיטת תל אביב

10 בנובמבר 2020, 14:00 
 
הזמנה לסמינר Optics Outreach של תא הסטודנטים של OSA באוניברסיטת תל אביב

שלום לכולם,

תא הסטודנטים של ה-OSA באוניברסיטת תל אביב מזמין אתכם הסטודנטים וחברי הסגל לסמינר Optics Outreach שנועד לחשוף בפניכם את פעילות המחקר בתחומי האופטיקה השונים באוניברסיטה.

הסמינרים יינתנו בעברית ובו נעודד לשאול שאלות בנוגע למחקר, ללימודים לתארים מתקדמים בתחום ולהצטרפות לפרויקטי גמר/תזה/דוקטורט.

הפעם, יעביר את הסמינר ד"ר איתי אפשטיין - חבר סגל חדש בפקולטה להנדסה, החוקר אינטראקציה של אור עם חומרים דו מימדיים.

הסמינר יתקיים בשלישי 10.11.20, בשעה 14:00.

קישור : https://us02web.zoom.us/j/83573975337  

מחכים לראותכם,

​צוות OSA Student Chapter

 

Student position in a medical device startup

  • 3rd or 4th year student of Biomedical Engineering
  • Open minded & team player
  • Organized & responsible
  • Multitasking abilities
  • Fast learner
  • Excellent English-understanding & writing
  • Basic mechanical background
  • Experience working in clean rooms or other manufacturing experience - advantage
  • Experience in QC & documentation - advantage
  • Soft technical & mechanical skills
« חזרה לכל המשרות

ירדן ג'רבי / "ללא פשרות" כישלונות והפסדים בדרך לניצחון ולהגשמת החלום

18 בנובמבר 2020, 20:30 - 21:45 
 
ירדן ג'רבי / "ללא פשרות" כישלונות והפסדים בדרך לניצחון ולהגשמת החלום
ארגון בוגרי הנדסה מזמין אתכם לפתוח שנה באירועי ריאליטי! טוב, לא באמת... רק סיפורים של גיבורות וגיבורים מהעולם האמיתי בהרצאות מעוררות השראה.
 
הג'ודוקא ירדן ג'רבי, מדליסטית אולימפית (ריו 2016), אלופת עולם (2013) ופיינליסטית "הישרדות", משתפת בסיפור חייה.
 
על הילדה שבגיל 3 צפתה ביעל ארד זוכה במדליית הכסף ושאלה את אמא "איך קונים כרטיס לאולימפיאדה?". 24 שנים אחר כך, כשדמעות מציפות את עיניה, היא תעמוד על הפודיום, לאחר שזכתה בעצמה במדליה אולימפית.
 
סיפור על התמודדות עם לחץ, פציעות, כישלונות והפסדים וכמובן גם על הצלחה פנומנלית ועל הגשמת חלומות דרך נחישות, כוח רצון והתמדה.
 
ההשתתפות ללא עלות לסטודנטים ולבוגרי הנדסה, מותנה בהרשמה מראש בקישור הבא
https://www.eventbrite.com/e/127596278905​
 
​​לנרשמים ישלח בהמשך לינק למפגש.

EE Seminar: Automatic breast lesion classification by joint neural analysis of mammography and ultrasound

24 בנובמבר 2020, 15:00 
ZOOM  

https://us04web.zoom.us/j/9811616388?pwd=UGlnWVFFT3lFVkFpZlNmVHFNUXRVdz09

Passcode: 5F4CPq

 

Speaker: Gavriel Habib

M.Sc. student under the supervision of Prof. Nahum Kiryati and Dr. Arnaldo Mayer

Tuesday, November 24th, 2020, at 15:00

 

Automatic breast lesion classification by joint neural analysis of mammography and ultrasound

Abstract:

            Breast cancer is the most common cancer in women worldwide. Mammography is a frequent diagnostic approach with proven mortality reduction and early disease treatment benefits. However, as it suffers from poor lesion visibility in dense breasts, radiologists are using breast ultrasound as a complementary imaging modality. Yet, existing computer-aided diagnosis (CAD) systems for the breast are generally based on a single modality.

In this research, we propose a deep learning-based method for classifying breast cancer lesions from their respective mammography and ultrasound images. The proposed approach is based on a GoogleNet architecture, fine-tuned for our own dataset in two training steps. First, a distinct neural network is trained separately for each modality, generating high-level features. Then, the aggregated features originating from each modality are used to train a multimodal network to provide the final classification. Our approach outperforms state-of-the-art mono-modal models and performs similarly to an average radiologist, surpassing two out of four radiologists participating in a reader study. These results suggest that our model may become a valuable decision support tool for radiologists.

 

השתתפות בסמינר תיתן קרדיט שמיעה = עפ"י רישום שם מלא + מספר ת.ז.  בצ'אט

 

 

 

 

 

 

 

 

 

 

EE Seminar: How to Learn Under Constraints?

16 בנובמבר 2020, 18:00 
ZOOM  

https://us02web.zoom.us/j/87990044156?pwd=bHNKTy9MSkExS3YwSDY4ajRZZElFdz09
Meeting ID: 879 9004 4156
Passcode: TAUEESYS

 

 

Speaker: Dr.  Michal Moshkovitz

Qualcomm Institute at UC San Diego

Monday, November 16th, 2020, at 18:00

How to Learn Under Constraints? 

Abstract

            The goal of classical machine learning is to learn a high-accuracy model from given examples. The learning process has no constraints besides running in a reasonable time relative to the input size. However, these days, as machine learning is utilized in high-stake applications like healthcare and law, learning has to obey several new constraints. In this talk, I will focus on two types of constraints: explainability and bounded memory. I will present the first explainable algorithm for k-means clustering that has provable guarantees. Then I will focus on another constraint -- learning with bounded memory, where I will present a characterization of high-accuracy learning with bounded memory and its equivalence to learning with statistical queries. 
Short Bio

Michal is a postdoctoral fellow at the Qualcomm Institute of the University of California, San Diego. Her interests lie in the foundations of AI, exploring how different constraints affect learning. She works on explainable machine learning, bounded memory learning, and online no-substitution clustering. Michal received her PhD from the Hebrew University and an MSc from Tel-Aviv University. She was the recipient of an Anita Borg scholarship from Google and a Hoffman scholarship from the Hebrew University.‏

 

השתתפות בסמינר תיתן קרדיט שמיעה = עפ"י רישום שם מלא + מספר ת.ז.  בצ'אט

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