The Exposure Convergence: Why Identity, Infrastructure, and Intelligence Are Converging

Running short on time but still want to stay in the know? Well, we’ve got you covered! We’ve condensed all the key takeaways into a handy audio summary. Our AI-driven podcasts are fit for on the go. The cybersecurity industry is experiencing a fundamental convergence around "exposure management" ...

Free Privacy Protection for Wireless Federated Learning: Enjoy It or Suffer from It?

Inherent communication noises have the potential to preserve privacy for wireless federated learning WFL but have been overlooked in digital communication systems predominantly using floating-point number standards, e.g., IEEE 754, for data storage and transmission. This is due to the potentially...

Bridging Unsupervised and Semi-Supervised Anomaly Detection: a Theoretically-Grounded and Practical Framework with Synthetic Anomalies

Anomaly detection AD is a critical task across domains such as cybersecurity and healthcare. In the unsupervised setting, an effective and theoretically-grounded principle is to train classifiers to distinguish normal data from synthetic anomalies. We extend this principle to semi-supervised AD,...

PDLRecover: Privacy-preserving Decentralized Model Recovery with Machine Unlearning

Decentralized learning is vulnerable to poison attacks, where malicious clients manipulate local updates to degrade global model performance. Existing defenses mainly detect and filter malicious models, aiming to prevent a limited number of attackers from corrupting the global model. However,...

SecureFed: a Two-Phase Framework for Detecting Malicious Clients in Federated Learning

Federated Learning FL protects data privacy while providing a decentralized method for training models. However, because of the distributed schema, it is susceptible to adversarial clients that could alter results or sabotage model performance. This study presents SecureFed, a two-phase FL...

GeoClip: Geometry-Aware Clipping for Differentially Private SGD

Differentially private stochastic gradient descent DP-SGD is the most widely used method for training machine learning models with provable privacy guarantees. A key challenge in DP-SGD is setting the per-sample gradient clipping threshold, which significantly affects the trade-off between privac...

A Geometric Square-Based Approach to RSA Integer Factorization

We present a new approach to RSA factorization inspired by geometric interpretations and square differences. This method reformulates the problem in terms of the distance between perfect squares and provides a recurrence relation that allows rapid convergence when the RSA modulus has closely spac...

Private Rate-Constrained Optimization with Applications to Fair Learning

Many problems in trustworthy ML can be formulated as minimization of the model error under constraints on the prediction rates of the model for suitably-chosen marginals, including most group fairness constraints demographic parity, equality of odds, etc.. In this work, we study such constrained...

CVE-2023-21848

Vulnerability in the Oracle Communications Convergence product of Oracle Communications Applications component: Admin Configuration. The supported version that is affected is 3.0.3.1.0. Easily exploitable vulnerability allows low privileged attacker with network access via HTTP to compromise Orac...

Optimal Client Sampling in Federated Learning with Client-Level Heterogeneous Differential Privacy

Federated Learning with client-level differential privacy DP provides a promising framework for collaboratively training models while rigorously protecting clients' privacy. However, classic approaches like DP-FedAvg struggle when clients have heterogeneous privacy requirements, as they must...

Private Statistical Estimation Via Truncation

We introduce a novel framework for differentially private DP statistical estimation via data truncation, addressing a key challenge in DP estimation when the data support is unbounded. Traditional approaches rely on problem-specific sensitivity analysis, limiting their applicability. By leveragin...

Coded Robust Aggregation for Distributed Learning under Byzantine Attacks

In this paper, we investigate the problem of distributed learning DL in the presence of Byzantine attacks. For this problem, various robust bounded aggregation RBA rules have been proposed at the central server to mitigate the impact of Byzantine attacks. However, current DL methods apply RBA rul...

Enhancing Blockchain Cross Chain Interoperability: a Comprehensive Survey

Blockchain technology, introduced in 2008, has revolutionized data storage and transfer across sectors such as finance, healthcare, intelligent transportation, and the metaverse. However, the proliferation of blockchain systems has led to discrepancies in architectures, consensus mechanisms, and...

From Texts to Shields: Convergence of Large Language Models and Cybersecurity

This report explores the convergence of large language models LLMs and cybersecurity, synthesizing interdisciplinary insights from network security, artificial intelligence, formal methods, and human-centered design. It examines emerging applications of LLMs in software and network security, 5G...

Crypto-NcRNA: Non-Coding RNA (NcRNA) Based Encryption Algorithm

In the looming post-quantum era, traditional cryptographic systems are increasingly vulnerable to quantum computing attacks that can compromise their mathematical foundations. To address this critical challenge, we propose crypto-ncRNA-a bio-convergent cryptographic framework that leverages the...

The vulnerability of the Ethernet Frame Handler component in Cisco IOS XR software on various Cisco Network Convergence System (NCS) platforms allows a attacker to trigger a Denial-of-Service Attack (DoS).

The vulnerability of the Cisco IOS XR software’s Ethernet Frame Handler component across various Cisco Network Convergence System NCS platforms is related to errors in the representation of certain functions. Exploiting this vulnerability allows a remote attacker to trigger a Denial-of-Service...

Cisco IOS XR Software Network Convergence System DoS (cisco-sa-l2services-2mvHdNuC)

According to its self-reported version, Cisco IOS XR is affected by a vulnerability. - A vulnerability in the handling of specific Ethernet frames by Cisco IOS XR Software for various Cisco Network Convergence System NCS platforms could allow an unauthenticated, adjacent attacker to cause critica...

CVE-2022-20845 Cisco Network Convergence System 4000 Series TL1 Denial of Service Vulnerability

A vulnerability in the TL1 function of Cisco Network Convergence System NCS 4000 Series could allow an authenticated, local attacker to cause a memory leak in the TL1 process. This vulnerability is due to TL1 not freeing memory under some conditions. An attacker could exploit this vulnerability b...

CVE-2022-20845 Cisco Network Convergence System 4000 Series TL1 Denial of Service Vulnerability

A vulnerability in the TL1 function of Cisco Network Convergence System NCS 4000 Series could allow an authenticated, local attacker to cause a memory leak in the TL1 process. This vulnerability is due to TL1 not freeing memory under some conditions. An attacker could exploit this vulnerability b...

The vulnerability of the Routed PON Controller Software component in the Cisco IOS XR operating system of Cisco NCS 540 Series Routers, NCS 5500 Series Routers, and NCS 5700 Series Routers allows attackers to execute arbitrary commands.

The vulnerability of the Routed PON Controller Software in Cisco IOS XR routers from the Cisco NCS 540 Series, NCS 5500 Series, and NCS 5700 Series routers exists due to the lack of measures taken to neutralize specific elements used in the operating system commands. Exploiting this vulnerability...