IEEE North Jersey Section

[] Internet traffic modeling and analysis is critical for network design and for cybersecurity. Internet traffic differs from Telephone traffic insofar as it characterized by long range dependent scale-free temporal dynamics. In this talk, we will describe multiscale analysis as a state-of-the-art tool to assess and quantify scale-free dynamics. We will also that show that wavelet analysis mut be combined with random projection strategies to permit a statistical characterization of Internet background traffic both accurate and robust to anomalies. In turn, these random projections can be further involved into automated anomaly detection and into the identification of the IP addresses involved. However, scale-free analysis remained so far mostly univariate, applied independently to directional counts of either bytes or packets, while challenges in cybersecurity naturally call for multivariate analysis. Elaborating on recent theoretical developments on eigenvalue-based multivariate self-similarity analysis, this talk will provide evidence for multivariate self-similarity in 17 years of Internet traffic data from the MAWI repository and will discuss the potential use of multivariate self-similarity for low volume anomaly detection. Co-sponsored by: Fairleigh Dickinson University Speaker(s): Dr. Patrice Abry Agenda: Fairleigh Dickinson University 1000 River Road, Building: Muscarelle Center, Room Number: 105 Teaneck, New Jersey, United States 07666 For additional information about the venue and parking, please contact Dr. Hong Zhao zhao@fdu.edu Virtual: https://events.vtools.ieee.org/m/469441

Abstract: The rapid evolution of Artificial Intelligence (AI) in cybersecurity is reshaping how digital threats are detected, mitigated, and prevented. AI-driven security solutions enhance threat intelligence, automate responses, and strengthen cyber defense mechanisms. However, as AI becomes more embedded in cybersecurity frameworks, it raises critical concerns about human rights, privacy, and ethical governance. The use of AI in surveillance, data monitoring, and decision-making has sparked debates about its potential to infringe on fundamental freedoms, leading to questions about accountability, fairness, and transparency. This webinar will explore the intersection of cybersecurity, AI, and human rights, addressing how AI-driven security measures impact digital privacy, freedom of expression, and the ethical responsibilities of organizations and governments. Experts from cybersecurity, law, and ethics will discuss key challenges such as algorithmic bias, the risks of AI-powered surveillance, and the implications of cybersecurity policies on human rights. Additionally, the session will examine regulatory frameworks and best practices to ensure AI technologies are deployed responsibly while upholding democratic values and societal trust. Speaker Bio: Mr. Sheshananda Reddy Kandula is a seasoned Application Security professional with 15 years of experience, currently working at Adobe, where he specializes in securing web, mobile, and API ecosystems. His expertise lies in identifying and mitigating vulnerabilities in alignment with OWASP Top 10 security standards. He holds industry-recognized certifications, including OSWE, OSCP, and CISSP, and has extensive hands-on experience addressing real-world security challenges. Prior to his role at Adobe, he contributed to global security initiatives at Mastercard, leading efforts in vulnerability management and secure software development. Passionate about advancing cybersecurity, Mr. Kandula actively contributes to the security community by sharing insights on secure coding, threat modeling, and application security best practices. His commitment extends to mentorship, technical leadership, and research, fostering a security-first mindset across organizations and professionals. Through his work, he strives to empower security practitioners, promote awareness, and strengthen digital resilience in an evolving threat landscape. Virtual: https://events.vtools.ieee.org/m/470549

Distributed Machine Learning for FPGAs in the Cloud Machine Learning (ML) is a growing area in both research and applications. Trends include larger and larger ML models and the interest in getting results from ML with low latency and high throughput. To address these trends, researchers are increasingly looking at accelerators (such as Graphics Processing Units (GPUs) and Field Programmable Gate Arrays (FPGAs), especially those that are directly connected to the network to achieve low latency access to data. In this talk, I will introduce the Open Cloud Testbed (OCT): https://octestbed.org/ OCT is available to researchers who are interested in conducting cloud research with accelerators. We provide GPUS, FPGAs, and AI engines from AMD. The FPGAs and AI engines are directly connected to the network. I will discuss experiments on using OCT for distributed ML using multiple network connected FPGAs. Specifically I will present results for running Resnet50 inference on the imagenet dataset. No hardware knowledge is assumed for this webinar. Speaker(s): Miriam Virtual: https://events.vtools.ieee.org/m/473027

This lecture will provide an introduction to electronic warfare (EW) concepts and principles. The intent is familiarize the audience with EW concepts and achieve an understanding of how EW is used to interrupt radar processing chains. This will include a general discussion on the EW field, including applications outside radar specific uses and terminology widely used within the field. A historical development of the EW field will be presented to motivate importance and historical use. Basic EW techniques (e.g. noise, range/velocity techniques, etc.) with associated effects on nominal radars will be presented/discussed to ensure an understanding of the technical underpinnings of EW. Building on the basic techniques, a brief discussion on concepts in advanced EW systems and current research will be presented. The discussion will conclude by briefly presenting the revolutionary impact of cognitive and AI/ML processes on EW, which will serve as a lead in to Karen Haigh's discussion on Cognitive EW. Co-sponsored by: IEEE North Jersey Section Speaker(s): David Brown, Agenda: Please RSVP to (mailto:nicole.zaretski@l3harris.com?subject=RSVP%20AOC%2024%20Jan%20L+L) (President, AOC Garden State Chapter), and indicate if you plan to attend in person or virtually, by COB Friday, 18 April to secure your place. The online presentation will begin promptly at 12:00 noon, but virtual attendees should sign in early to ensure they are able to connect to the web event. Bldg: Auditorium, L3 Harris Technologies, 77, River Road, Clifton, New Jersey, United States