Northeastern University College of Engineering

Xiang Zhang PhD Proposal Review
2023-12-20 14:00 - 15:00 (America/New_York)
Title:Confidentiality and Privacy Preserving: Intertwining Deep Learning and Side-channel Analysis
Meeting ID: 976 4324 8925 Passcode: [REDACTED]
Committee Members:
Prof. Yunsi Fei (Advisor)
Prof. Adam Ding
Prof. Lili Su
Abstract:
In the past decade, deep learning-empowered technologies have significantly permeated our daily lives, revolutionizing diverse application domains with superb performance. In hardware security, deep learning has been employed for power or electromagnetic side-channel analysis (SCA) and protection, and the security of deep learning implementations starts gaining traction.
This dissertation delves into the intertwining deep learning techniques and side-channel analysis. It addresses two critical questions: how to extend deep learning to other types of SCAs; what confidentiality and privacy vulnerabilities deep learning models have.
Our research work first explores deep learning-assisted cache side-channel attacks and introduces innovative countermeasures grounded in the principles of adversarial samples against deep learning. We first design a novel high-frequency cache monitor, which runs concurrent to the victim execution and collects run-time timing traces, while previous cache monitors are only able to collect timing samples. Such timing traces facilitate follow-on non-profiled Differential Deep Learning Analysis (DDLA) for secret retrieval. We also propose a novel countermeasure against the new DDLA, leveraging the concept of adversarial examples, which deliberately introduces obfuscation operations in the victim program so as to generate ‘adversarial’ timing traces and therefore circumvent the follow-on DDLA.
The second part of the dissertation addresses the vulnerability of deep neural network (DNN) implementations and presents novel methodologies for enhancing user privacy. It introduces a technique for extracting deep learning models through software-based power side channels. By manipulating model inputs and leveraging the on-chip Intel Running Average Power Limit (RAPL) sensors reporting, the entire model parameters can be extracted when the model inference is executed on modern processors. To protect both the model confidentiality and the input privacy, this dissertation proposes to obfuscate the model inputs while preserving the end-to-end functionality. It introduces an encoder to transform the inputs before feeding the DNN model, and appends a decoder after the model outputs to recover the intended results. The approach, compared to traditional encryption or masking techniques, is more efficient and can effectively protect both user privacy and model confidentiality.
The overall goal of the dissertation is to further investigate the power of deep learning in SCA and countermeasure and safeguard secure DNN implementations.

Night of yalda
2023-12-15 12:00 - 13:00
Honor the longest night of the year in this Persian cultural tradition to offer hope and light.
There will be watermelon, pomegranate, Persian food, and of course Persian music.
Not necessary, but we will be happy if you show up in red.
Location: 111 Snell Library

LinkedIn, CV, Resume: A CommLab Workshop Series
2023-12-13 17:00 - 18:00, 2023-12-06 17:00 - 18:00, 2023-11-29 17:00 - 18:00
Join our empowering LinkedIn, CV, Resume Workshop series any Wednesday from 5 pm to 6 pm ET. This collaborative space offers valuable tips and peer feedback to enhance your online profile and professional presence. Whether you’re a student or seasoned professional, our community will help you optimize your LinkedIn profile, CV, and resume to stand out in today’s job market. Don’t miss this chance to learn, grow, and build a strong network together! This is a virtual workshop series, you can drop in any time between 5-6pm and any week. Join us virtually on Zoom.

Bioengineering Fall Seminar Series
2023-12-13 12:00 - 13:00, 2023-12-06 12:00 - 13:00
Join the Department of Bioengineering for our Fall Seminar Series, where we host scholars from outside institutions to come to campus and give a seminar on their research.
Every Wednesday, 12-1pm in Shillman Hall 335, unless otherwise stated. Schedule below.
September 13th:

Speaker: Associate Professor Nikolai Slavov
Institution: Northeastern Department of Bioengineering

September 20th:

Speaker: Associate Professor Erel Levine
Institution: Northeastern Department of Bioengineering

September 22nd (*Friday 12-1pm, in SH 305*)

Speaker: Assistant Professor Abbas Yaseen
Institution: Northeastern Department of Bioengineering

September 27th:

Speaker: Marisol Herrera-Perez, PhD
Institution: University of Rochester
Research Focus: Cellular self organization

October 4th:

Speaker: Michele Marcolongo, PhD
Institution: Villanova University
Research Focus: Biomimetic proteoglycans

October 11th:

Speaker: Keith Dunker, PhD
Institution: Indiana University
Research Focus: Intrinsically disordered proteins

October 18th:

Speaker: Tyrone Hayes, PhD
Institution: University of Cailfornia, Berkeley
Research Focus: Endocrine disruption

October 25th:

Speaker: Lauren Black III, PhD
Institution: Tufts University
Research Focus: Cardiovascular tissue engineering, tissue mechanics

November 1st:

Speaker: Junjie Zhang, PhD
Institution: University of Texas A&M
Research Focus: Computational bioengineering, structural biology modeling

November 8th:

None

November 15th:

Speaker: Ritu Raman, PhD
Institution: Massachusetts Institute of Technology
Research Focus: Adaptive biological actuators

November 22nd:

Thanksgiving Break

November 29th:

Speaker: Debora Marks, PhD
Institution: Harvard University
Research Focus: Computational and systems biology

December 6th:

Speaker: Srivallesha (Valli) Mallidi, PhD
Institution: Tufts University
Research Focus: Photoacoustic imaging

December 13th:

Speaker: Kristin Koutmou, PhD
Institution: University of Michigan
Research Focus: Chemical modification to mRNA

Challenging Conversations: A CommLab Workshop Series
2023-12-12 17:00 - 18:00, 2023-12-05 17:00 - 18:00
Join our weekly virtual workshop, “Challenging Conversations,” every Tuesday from 5:00 PM to 6:00 PM. This community of practice helps you build the skill of navigating difficult discussions with peers and individuals of varying authority. Learn practical strategies, engage in role-playing exercises, and connect with like-minded peers to enhance your communication abilities. Register to join us on Zoom now to confidently handle challenging conversations in any context.

Deepak Prabhala MS Thesis Defense
2023-12-12 11:00 - 17:00
Title: “Smart Microwave Devices with Programable Printed Circuit Board (PPCB): Design with Liquid Crystal Elastomer Polymers in Transmission Lines and Circulators”
Committee Members:
1) Professor Nian X. Sun (Advisor)
2) Professor Marvin Onabajo
3) Professor Yongmin Liu
Abstract:
This study explores the innovative application of liquid crystal elastomer (LCE) polymers in the design and implementation of microwave transmission lines and circulators. Liquid crystal elastomers, known for their unique combination of liquid crystalline and elastomeric properties, offer a unique approach to developing flexible and tunable microwave devices. The research focuses on a thorough study of the electro-mechanical properties of LCEs to achieve novel functionalities in the design of transmission lines and circulators for microwave communication systems in HFSS simulations. The first part of the study delves into the characterization of the dielectric and mechanical properties of the chosen LCE polymer. Subsequently, the design and fabrication of a flexible and tunable transmission line using LCE are discussed. The LCE-based transmission line aims to measure the insertion loss and return loss with different widths, lengths, and thicknesses of the LCE polymer. The study investigates the impact of temperature on the transmission line’s performance, offering insights into potential applications in reconfigurable microwave systems. The second phase of the research explores the utilization of LCE in the development of a microwave circulator, a vital component in microwave communication networks. The circulator design incorporates the unique properties of LCE by using a stepped dielectric variation approach for broadband isolation. This innovation holds promise for enhancing the efficiency and adaptability of microwave systems in communication and radar applications. The findings of this research contribute to offering a pathway for integrating liquid crystal elastomers into flexible and reconfigurable microwave devices. This thesis aims to advance the understanding of smart microwave devices and inspire further exploration into the application of liquid crystal elastomer polymers in cutting-edge technologies.

Durga Suresh PhD Proposal Review
2023-12-12 09:00 - 10:00
Title: Network Security Management and Threat Mitigation in the Open Cloud

Committee Members:
Prof. Miriam Leeser (Advisor)
Prof. Michael Zink
Prof. Xiaolin Xu
Abstract:
Cloud computing and advanced cyberinfrastructures are increasingly vital to the functioning of Internet systems. Every day, more devices are added to the cloud, to provide greater resource utilization, availability, and scalability. Due to the expanding reliance on cloud computing, securing the cloud is paramount. Tackling the issue of securing the cloud is crucial not only for preserving the functionality and reliability of cloud-based systems but also for protecting the critical data and services that depend on these platforms.
Cloud computing models include public clouds, private clouds, community clouds, and hybrid clouds. Private, community, and hybrid clouds provide security, but with an important trade-off; namely, user access restriction in the cloud. The proposed research uses the Open Cloud Testbed (OCT) which is part of the National Science Foundation’s (NSF) Computer and Information Systems Engineering(CISE) Community Research Infrastructure(CRI) program. OCT is an example of a public cloud that allows users two things: 1) an isolated set of nodes to perform experiments with bare metal access, which can potentially lead to security issues, and 2) the ability to test out the solutions for both using the cloud and adding security to it. The proposed research aims to target a system like the OCT, specifically targeting a public cloud environment.
This system will be designed to allow access to the switch, enabling control and management of traffic within the cloud network. This research aims to mitigate network security threats in the public cloud network. The aim of this research is multifold. First, we identify and classify the behavior of users in the cloud. We then provide an approach to creating a network security management policy that will deal with 1)detecting network intruders that scan the cloud network and remove their access to the network, and 2) managing heavy hitters that can cause Denial of Service (DOS) and Distributed Denial of Service (DDOS) attacks in the cloud network by using the heavy hitter detection system and prevent them from putting more traffic on the network. Both network intruder detection and heavy hitter management systems use Access Control Lists (ACL)as a means to prevent the user from putting traffic on the cloud network. Lastly, we perform experiments to handle these threats and measure the success of the experimental setup concerning network attacks. The proposed approach will ensure network security by creating a framework for network security management policy to minimize threats in the cloud network and other resources directly attached to the network. The proposed research aims to enhance cybersecurity by employing network intruder detection techniques to identify potential threats, implementing heavy hitter management to mitigate threats effectively, and developing and enforcing a network security management policy to prevent future threats.

Poster Design and Editing: A CommLab Workshop Series
2023-12-11 15:00 - 16:30, 2023-12-04 15:00 - 16:30 | 344 CSC, 360 Huntington Ave, Boston, MA, 02115, United States
COE Graduate students are invited to learn techniques, develop, edit and practice delivering your poster. Join the CommLab’s weekly workshop to improve your poster design and communication skills. Drop in for as many workshops as you want to get your poster ready. You can also join any time between 3:00-4:30pm ET. This is a hybrid opportunity. Join us on Zoom or in 344 Curry Student Center. We will be in room 435 on Monday 9/11 and 9/30. We will meet virtually only on Monday, 11/6.

CommLab Writing Group
2023-12-11 14:00 - 16:00, 2023-12-04 14:00 - 16:00
Setting and sticking to a consistent writing schedule is key to improving skills and accomplishing your writing tasks (don’t just take our word for it).
COE graduate students are invited to join the CommLab writing group to share best practices, get feedback from your peers, and work on any writing project (dissertation, thesis, manuscript, fellowship, poster, presentation, or other forms of technical communications). We will meet weekly in 306 Egan for 2 hours; you do not have to attend all the sessions or participate for the full 2 hours. (Note we will be in 336 CSC on 9/18 and we will not meet on Monday, Nov 6).

Cookies with the Dean: Holiday Edition
2023-12-11 13:00 - 14:30 | The Tents at Robinson Quad, 336 Huntington Ave, Boston, MA, 02115, United States
Join Dean Gregory Abowd for a holiday-themed Cookies with the Dean on Monday, December 11th from 1:00-2:30pm in the Robinson Quad Tent near Curry Student Center.
We will have holiday cookies, hot cocoa, and free Swag available! Swing by for food, fun, and a chance to talk to the Dean.
We hope to see you there!
This is a 100% compostable event. Please place all waste into the marked composting bins for their disposal.

Graduate School of Engineering Campus Tour (In Person)
2023-12-09 10:00 - 11:00
Want to learn more about the Graduate School of Engineering on the Boston campus? Then we welcome you to sign up for a Graduate School of Engineering campus tour! Led by one of our expert Graduate Student Ambassadors, we’ll show you key locations on campus, in addition to resources specific to Engineering, and answer your questions about Boston. Please complete the registration form linked below to select the date and time that works best for you. Tours are open to both newly admitted and prospective students. We can’t wait to meet you!

Ate Darabi PhD Proposal Review
2023-12-08 12:00 - 12:30
Title:
Complex Delayed Networks and Their Application in Epidemic Analysis: Modeling, Analysis, and Strategic Management
Committee Members:
Prof. Milad Siami (Advisor)
Prof. Bahram Shafai
Prof. Rozhin Hajian
Abstract:
In the face of crowd-related disasters like pandemics and mass attacks, the complex dynamics of human interactions demand comprehensive modeling approaches. This proposal adopts a network-based perspective, leveraging the delayed Susceptible-Infected-Susceptible (SIS) model for epidemics and the Predator-Swarm-Guide (PSG) model for crowd movement, to gain insights into the dynamics of these critical situations.
In epidemic networks, time delays and uncertainties can significantly change the epidemic behavior and result in successive echoing waves of the spread between various population clusters. We examine these effects on linear SIS dynamics, evaluating network stability and performance loss. We prove that network performance loss is correlated with the structure of the underlying graph, intrinsic time delays, epidemic characteristics, and external shocks. This performance measure is then used to develop an optimal traffic restriction algorithm for network performance enhancement, resulting in reduced infection in the metapopulation. An epidemic-based centrality index is also proposed to evaluate the impact of every subpopulation on network performance, and its asymptotic behavior is investigated. This index converges to local or eigenvector centralities under specific parameters. Moreover, given that epidemic-based centrality depends on the epidemic properties of the disease, it may yield distinct node rankings as the disease characteristics slowly change over time or as different types of infections spread. This unique characteristic of epidemic-based centrality enables it to adjust to various epidemic features. The derived centrality index is then adopted to improve the network robustness against external shocks on the epidemic network.
The PSG model addresses mass attack scenarios, considering individuals’ efforts to evade adversaries and seek guidance. Environmental factors like impermeable walls and psychological elements are incorporated into this model. The preliminary results highlight the role of coordinated cooperation in minimizing casualties. The objective is to reduce casualties through a hybrid motion optimization approach for individuals and the guiding agent.

Yisi Liu MS Thesis Defense
2023-12-07 14:00 - 15:00
Title: Experimental research on the Nonlinear Magnetoelectric Effect of the VLF ME Antennas
Committee Members:
Prof. Nian Sun (Advisor)
Prof. Yongmin Liu
Prof. Xufeng Zhang
Abstract:
Magnetoelectric (ME) coupling effects in ferromagnetic and piezoelectric composites involve the control of electric polarization (P) by applying a magnetic field (H) (direct ME effect), or the manipulation of magnetization (M) through an electric field (E) (converse ME effect) . These effects are facilitated by the mechanical deformation in the ferroic phases resulting from the combination of magnetostriction and piezoelectricity. In single-phase materials, the breakthrough in achieving large ME coefficients has further advanced the development of ME materials and devices. Consequently, numerous multifunctional ME devices, such as mechanical antennas, magnetic sensors, tunable inductors, and filters, have been developed. This thesis has provides a summary and categorization of these devices based on their physical mechanism and type of ME effects. The inclusion of mechanical ME antennas based on piezoelectric/magnetostrictive heterostructures with acoustic actuation reflects the significant interest in this topic. Notably, a maximum communication distance of 120 m for a very low frequency (VLF) communication system has been achieved using a pair of mechanical ME antennas. Subsequently, we will focus on introducing and reviewing the materials and devices related to the ME effect, as well as the application of ME mechanical antennas in very low frequency (VLF) communication systems.
In addition to that, we developed a transmitter with a Metglas/PZT/Metglas structure antenna. Our study focuses on investigating the transmission effects of this antenna when employing direct antenna modulation techniques to enhance data transmission. Through our research, we have introduced a novel modulation method by modulating the antenna. We observed that this modulation method produces a more stable and stronger signal.

Mauro Belgiovine PhD Proposal Review
2023-12-07 10:00 - 11:00
Title: Wireless Intelligence: A Comprehensive Exploration of AI-Driven Solutions in Channel Estimation, Beam Refinement, and Protocol Classification for Next Generation Networks
Committee Members:
Prof. Kaushik Chowdhury (advisor)
Prof. Stratis Ioannidis
Dr. Chris Dick
Abstract:
his thesis explores the transformative impact of artificial intelligence (AI) on wireless systems through model-driven simulations and real-world datasets, with a focus on enhancing both local and cellular wireless networks through the deployment of highly customized deep learning solutions that target specific bottlenecks affecting traditional signal processing based communication.
The research delves into three key areas that address critical challenges in the current wireless landscape. The first focal point of the investigation involves channel estimation using deep learning techniques to denoise pilots and expedite the accurate estimation of Channel State Information (CSI). By leveraging deep learning methodologies, the proposed solution aims to enhance the reliability and computation for MIMO and massive MIMO channel estimation, thereby contributing to improved communication efficiency and reduced errors. The second major topic encompasses the application of reinforcement learning for 5G New Radio (NR) millimeter-wave (mmWave) beam refinement. The study aims to develop a Deep Reinforcement Learning algorithm capable of adjusting beamsteering angles, starting from a coarse beam scanning procedure and further refining them for higher transmission efficiency. This innovation is expected to substantially decrease traffic overhead while simultaneously enhancing beam steering precision, thus optimizing the performance of mmWave communication. The third and final area of focus introduces a transformer-based WiFi multi-protocol classifier, strategically deployed on a DeepWave Air-T edge device, which is equipped with Module on Chip (MoC) low power CPU-GPU and programmable Software Defined Radio (SDR). This classifier outperforms existing modulation classification models and legacy methods under lower SNR conditions, leveraging TensorRT’s model compression capabilities to efficiently process extended sequences of raw IQ samples, ensuring high performance at a low computational cost. The proposed solution addresses the growing demand for efficient and adaptable wireless communication systems, paving the way for advancements in edge-based processing and intelligent protocol classification.
This work seeks to contribute significantly to the ongoing AI revolution in wireless systems by addressing crucial issues in channel estimation, beam refinement, and protocol classification. The outcomes of this research hold the potential to redefine the landscape of wireless communication, offering enhanced performance, reduced overhead, and increased adaptability in both local and cellular networks.

The Graduate School Collaborative Webinar: Why consider Graduate School?
2023-12-06 19:30 - 21:00
Think graduate school is just for future faculty? Think again!
Join us as we demystify what it takes to be and become a graduate student (MS and PhD), as well as the many career avenues that you can take with a graduate school degree. Come learn about the following topics from some of the top Engineering Graduate Programs in the country!

What is graduate school
What to expect
Why consider graduate school
How to prepare
Thinking ahead to the application process

What: Seminar on “Why consider Graduate School?”
When: Wednesday December 6th, 2023; 7:30 – 8:30 p.m. Eastern Time
For Who: Undergraduate Freshman, Sophomore, and Juniors
Sponsored by: UC Berkeley, Georgia Tech, John’s Hopkins university, University of Michigan, Northeastern, NYU, Ohio State, University of Oklahoma, Purdue University, Rice University, University of Southern California
Where: Zoom – Register using the Link below
Questions about accessing the webinar? Contact Nina Parshall at parshall.8@osu.edu

Note: There will be breakout sessions with each school for 30mins following the presentation (8:30pm – 9:00pm Eastern). Register to join for some exclusive networking!

Suyash Pradhan MS Thesis Defense
2023-12-06 15:00 - 17:00
Title: COPILOT: Cooperative Perception using Lidar for Handoffs between Road Side Units
Committee Members:
Prof. Kaushik Chowdhury (Advisor)
Prof. Stratis Ioannidis
Prof. Jennifer Dy
Abstract:
This thesis presents COPILOT, a ML-based approach that allows vehicles requiring ubiquitous high bandwidth connectivity to identify the most suitable road side units (RSUs) through proactive handoffs. By cooperatively exchanging the data obtained from local 3D Lidar point clouds within adjacent vehicles and with coarse knowledge of their relative positions, COPILOT identifies transient blockages to all candidate RSUs along the path under study. Such cooperative perception is critical for choosing RSUs with highly directional links required for mmWave bands, which majorly degrade in the absence of LOS. COPILOT proposes three modules that operate in an inter-connected manner: (i) As an alternative to sending raw Lidar point clouds, it extracts and transmits low-dimensional intermediate features to lower the overhead of inter-vehicle messaging; (ii) It utilizes an attention-mechanism to place greater emphasis on data collected from specific vehicles, as opposed to nearest neighbor and distance-based selection schemes, and (iii) it experimentally validates the outcomes using an outdoor testbed composed of an autonomous car and Talon AD7200 60GHz routers emulating the RSUs, accompanied by the public release of the datasets. Results reveal COPILOT yields upto 69.8% and 20.42% improvement in latency and throughput compared to traditional reactive handoffs for mmWave networks, respectively

Enabling Engineering Fall Showcase
2023-12-06 13:00 - 15:00 | 002 Ell Hall, 360 Huntington Ave, Boston, MA, 02115, United States
Students will present their final design projects at the Enabling Engineering Fall Showcase. The projects that will be presented are listed below.

Accessible Golfing
Caution Radar
Accessible Plant Watering System
Switch Activated Cornhole
VR App
Switch Activated Toys
Adaptive Drum Set
Prosthetic Finger

Enabling Engineering is a Northeastern University student group that designs and builds devices to empower individuals with physical and cognitive disabilities. Our students collaborate with clients on projects that provide greater independence, reduce medical burdens, and increase social connectedness. We help family members, clinicians, and teachers care for people with disabilities. By giving students the opportunity to participate in Enabling Engineering projects, we are training the next generation of engineers to be knowledgeable about, and aware of, the needs of individuals with disabilities.
If you are unable to join in person, you can join via Zoom.

Small Talk for Networking: A CommLab Workshop Series
2023-12-06 13:00 - 14:00
Looking to improve your conversational skills and effortlessly connect with others? This workshop series will equip you with the essential tools, techniques, and practice to connect with others, whether you are attending networking events, social gatherings, or simply looking to strike up conversations with new acquaintances. Join us for these fun-filled sessions where we’ll explore icebreakers, conversation starters, and strategies to keep the dialogue flowing naturally. Don’t miss out on this opportunity to boost your confidence and unlock new connections. This is a hybrid workshop series, Please register and join us on Wednesdays on Zoom from 1-2 pm ET, or join us in person in room 306 Egan, except on Wednesday, September 20, when we will be in 335 Curry Student Center.

Chemical Engineering Fall Seminar Series: Professor Cameron Abrams
2023-12-06 12:00 - 13:00 | 010 Behrakis, 360 Huntington Ave, Boston, MA, 02115, United States
Molecular Dynamics Investigations of Thermosetting Polymers
Thermosetting polymers comprise a wide variety of monomer constituents and polymerization chemistries that in principle provide the degrees of freedom necessary to tailor these materials to a broad range of applications, from structural composites, coatings and barrier materials, ballistic shielding, and even solid rocket fuels. In this talk, I will trace my group’s history in using molecular dynamics simulations to investigate conceptual links among molecular architectures, intermolecular interactions, and network structures and how they determine thermomechanical properties of polymerized materials that these applications demand. Highlights in this history include the discovery of the links between crosslink arrangements and protovoid-based toughening; toughening using partially reacted substructures; long-timescale material response through time-temperature superposition; and rationalizing improvements over petrochemically derived monomers using novel bio-based subunits. A consistent theme will be demonstration of how close collaboration with experimental groups allows for simulation predictions to be tested. I will conclude with a presentation of our group’s software package, HTPolyNet, that represents the first opensource, end-to-end generator of all-atom models of network-polymerized monomer mixtures based only on monomer structures, which should accelerate the community’s use of MD simulation to investigate thermosetting polymers.

Cameron F. Abrams is the Bartlett ’81 – Barry ’81 Professor of Chemical and Biological Engineering at Drexel University, where he has served on the faculty since 2002 and as Department Head since 2017. Abrams’ research expertise lies in advancing modern molecular simulation methods with applications in protein science, drug discovery, complex fluids, and high-performance materials. He is the recipient of an ONR Young Investigator Award, an NSF CAREER Award, and the AIChE Computational and Molecular Sciences Forum Impact Award. He received a BS in Chemical Engineering from North Carolina State University in 1995 and a PhD from the University of California, Berkeley, in 2000. He trained as a postdoc for two years in the Theory Group at the Max-Planck-Institute for Polymer Research in Mainz, Germany, before joining Drexel.

First-Year Engineering Fall Expos
2023-12-04 11:00 - 15:00, 2023-12-01 09:00 - 12:00 | Curry Student Center, 360 Huntington Ave., Boston, MA, 02115, United States
Join us for First-Year Engineering’s Fall Expos on Friday, December 1, from 9:00 AM – 12:00 PM and Monday, December 4, from 11:00 AM – 3:00 PM in the Curry Student Center Pit and Quad. Cornerstone of Engineering students will be showcasing their Fall projects. Themes include sumo robots, sustainability, carnival games, animals and the natural world, and interactive games.

Cheng Gongye PhD Dissertation Defense
2023-12-04 10:30 - 11:30
Title:
Hardware Security Vulnerabilities in Deep Neural Networks and Mitigations
Date:
12/4/2023
Time:
10:30:00 AM
Committee Members:
Prof. Yunsi Fei (Advisor)
Prof. Aidong Ding
Prof. Xue Lin
Prof. Xiaolin Xu
Abstract:
In the past decade, Deep Neural Networks (DNNs) have become pivotal in numerous fields, including security-sensitive autonomous driving and privacy-critical medical diagnosis. This Ph.D. dissertation delves into the hardware security of DNNs, discovering their vulnerabilities to fault and side-channel attacks and exploring novel countermeasures essential for their safe deployment in critical applications.
Fault attacks disrupt computation or inject faults into parameters, compromising the integrity of targeted applications. This dissertation demonstrates a power-glitching fault injection attack on FPGA-based DNN accelerators, common in cloud environments, which exploits vulnerabilities in the shared power distribution network and results in model misclassification. In response to these threats, we introduce a novel, lightweight defense mechanism to protect DNN parameters from adversarial bit-flip attacks. The proposed framework incorporates a dynamic channel-shuffling obfuscation scheme coupled with a logits-based model integrity monitor. The approach effectively safeguards various DNN models against bit-flip attacks, without necessitating retraining or structural changes to the models. Furthermore, our research expands the scope of fault analysis beyond just the parameters of DNN models. We thoroughly examine the entire implementation of commercial products, defying the prevailing assumption that quantized DNNs are inherently resistant to bit-flips.
Side-channel attacks exploit information leakage of system implementations, such as power consumption and electromagnetic emanations, to reveal system secrets and therefore compromise confidentiality. This dissertation makes significant contributions to side-channel assisted model extraction of DNNs. We present a floating-point timing side-channel attack on x86 CPUs that reverse-engineers DNN model parameters in software implementations. For hardware accelerators, we target the state-of-the-art AMD-Xilinx deep-learning processor unit (DPU), a reconfigurable engine dedicated to convolutional neural networks (CNNs) and representing the most complex commercial FPGA accelerator with encrypted IPs. Our work demonstrates that electromagnetic analysis can be leveraged to recover the data flow and scheduling of the DNN accelerators, facilitating follow-on architecture and parameter extraction attacks. To mitigate EM side-channel model extraction attacks, we introduce a novel defense mechanism that devises a random importance-aware activation mask on input pixels to disrupt the operation alignment on EM traces, with minimal performance and efficiency impacts.
Overall, this dissertation significantly deepens the understanding of hardware security of DNN models. It makes important contributions in discovering novel and critical vulnerabilities of DNN inference pertaining to system implementations, and proposing effective and practical solutions for securing DNNs in mission-critical environments. The research work marks a substantial step forward in the development of resilient and secure AI systems.

Gordon Institute Virtual Information Session
2023-11-29 17:30 - 18:30
Learn how you can earn a Graduate Certificate in Engineering Leadership as a stand-alone certificate or in combination with one of twenty-three Master of Science degrees offered through Northeastern’s College of Engineering, College of Science, or Khoury College of Computer Sciences.
The National Academy of Engineering recognized The Gordon Institute of Engineering Leadership (GIEL) for its innovative curriculum that combines technical education, leadership capabilities, and the “Challenge Project”: an opportunity for students to receive master’s level credit while working in industry.
By aligning technical proficiency with leadership capabilities, GIEL accelerates the development of high-potential engineers and prepares them to lead complex projects early in their careers. Upon completing the program, more than 88% of the 2022 class reported increased leadership responsibility, while more than 50% of the 2022 class reported being promoted within one year of graduation.
Our Director of Admissions will answer your application questions for Fall 2024.
You will have the opportunity to hear from Alumni on how The Gordon Institute propelled their engineering careers. Program professors will also be present to answer curriculum questions.

Aria Masoomi PhD Proposal Review
2023-11-29 16:30 - 17:00
Title:
Making Deep Neural Network Transparent
Date:
11/29/2023
Time:
4:30:00 PM
Committee Members:-
Prof. Jennifer Dy
Prof. Eduardo Sontag
Prof. Mario Sznaier
Prof. Peter Castaldi
Abstract:
As machine learning algorithms are deployed ubiquitously to a variety of domains, it is imperative to make these often black-box models transparent. The ability to interpret and comprehend the reasoning behind machine learning models plays a pivotal role in increasing user trust. It not only offers insights into how a model functions but also opens avenues for model enhancements.
This research delves into the realm of interpretability, focusing on the dichotomy between ‘intrinsic’ and ‘post hoc’ interpretability. Intrinsic interpretability involves constraining the complexity of the machine learning model itself, resulting in models inherently interpretable due to their simplicity, such as decision trees or sparse linear regression. On the other hand, post hoc interpretability employs techniques that assess the model’s behavior after training, offering insights into the model’s outcomes. Examples of post hoc techniques include permutation feature importance and the Shapley value method for feature importance.
The core contribution of this Thesis proposal lies in the development of novel methods to enhance both intrinsic and post hoc interpretability. These methods aim to advance the field by offering new perspectives on understanding machine learning models, thereby contributing to the ongoing discourse on model transparency and user trust.
