·
DataBubble
  • Home
  • Models
  • News
  • Compare
  • Boards
  • Pricing
  • About
  • Newsletter
  • Methodology
  • Contact
Latest
Data centers expected to use 4x more electricity by 20351h◆Google releases three new Gemini models — but no 3.5 Pro2h◆Introducing the ChatGPT for small business program2h◆Anthropic’s $1.5 billion book piracy settlement approved by judge2h◆US threatens sanctions against Chinese AI models over IP theft4h◆Google launches a cheaper alternative to large AI security models like Mythos4h◆Music streamer Deezer says more than 50% of daily uploads are AI-generated6h◆Halliday’s latest smart glasses feature a much-improved display6h◆America needs to stop getting shocked by Chinese AI8h◆Advancing next-gen AI with materials science innovation9h◆Gritt exits stealth with $32 million for robots to build solar plants — then, everything else9h◆Capacity and Redundancy Trade-offs in Multi-Task Learning15h◆Predictive Training with Latent Imagination for Visual Quadruped Navigation15h◆Where Not to Learn: Prior-Aligned Training with Subset-based Attribution Constraints for Reliable Decision-Making15h◆Did We Actually Fix It? An Independent Adversarial Stress-Test of Post-Point-Adjustment Evaluation Metrics for Time-Series Anomaly Detection15h◆Supervised Reward Inference15h◆PPO-HSC: An Exploratory Reinforcement Learning Framework Based on Wide-Area Policy Coverage Optimization15h◆Is Progressive Disclosure All You Need for Long-Context Agents?15h◆It Depends on the Dataset: When a Brain-Encoding Model's Predicted Responses Beat Their Visual Backbone for Video Memorability15h◆DMFNet: Dual-Backbone Multiscale Fusion Network for Urban Scene Classification15h◆Data centers expected to use 4x more electricity by 20351h◆Google releases three new Gemini models — but no 3.5 Pro2h◆Introducing the ChatGPT for small business program2h◆Anthropic’s $1.5 billion book piracy settlement approved by judge2h◆US threatens sanctions against Chinese AI models over IP theft4h◆Google launches a cheaper alternative to large AI security models like Mythos4h◆Music streamer Deezer says more than 50% of daily uploads are AI-generated6h◆Halliday’s latest smart glasses feature a much-improved display6h◆America needs to stop getting shocked by Chinese AI8h◆Advancing next-gen AI with materials science innovation9h◆Gritt exits stealth with $32 million for robots to build solar plants — then, everything else9h◆Capacity and Redundancy Trade-offs in Multi-Task Learning15h◆Predictive Training with Latent Imagination for Visual Quadruped Navigation15h◆Where Not to Learn: Prior-Aligned Training with Subset-based Attribution Constraints for Reliable Decision-Making15h◆Did We Actually Fix It? An Independent Adversarial Stress-Test of Post-Point-Adjustment Evaluation Metrics for Time-Series Anomaly Detection15h◆Supervised Reward Inference15h◆PPO-HSC: An Exploratory Reinforcement Learning Framework Based on Wide-Area Policy Coverage Optimization15h◆Is Progressive Disclosure All You Need for Long-Context Agents?15h◆It Depends on the Dataset: When a Brain-Encoding Model's Predicted Responses Beat Their Visual Backbone for Video Memorability15h◆DMFNet: Dual-Backbone Multiscale Fusion Network for Urban Scene Classification15h◆
News/Beyond Gradient-Based Attacks: Adversarial Robustness and Explainability Stability in Cybersecurity Classifiers
arxiv
PublishedJuly 3, 2026 at 4:00 AM
—neutral

Beyond Gradient-Based Attacks: Adversarial Robustness and Explainability Stability in Cybersecurity Classifiers

Source
arxiv.orgfull article ↗
Read on arxiv→
Publisher summary· verbatim

arXiv:2607.01679v1 Announce Type: cross Abstract: Adversarial attacks on cybersecurity classifiers pose a dual threat: degrading predictions and destabilising the SHAP-based explanations that security analysts rely on to understand and triage alerts. We extend our prior MLP conference study to Rando

Stay posted· Newsletter

A 5-min weekly brief — top movers, price watch, story of the week.

// no spam · unsubscribe one-click · free forever

Discussion
Source
↗
arxiv
Read original ↗All from arxiv →

No replies yet. Be first.

Source
↗
arxiv
Read original ↗All from arxiv →

Related coverage

More from ARXIV
arxivCapacity and Redundancy Trade-offs in Multi-Task Learning15harxivPredictive Training with Latent Imagination for Visual Quadruped Navigation15harxivWhere Not to Learn: Prior-Aligned Training with Subset-based Attribution Constraints for Reliable Decision-Making15harxivDid We Actually Fix It? An Independent Adversarial Stress-Test of Post-Point-Adjustment Evaluation Metrics for Time-Series Anomaly Detection15h
The Bubble Brief
WEEKLY

Read AI insights every Tuesday — top movers, new releases, story of the week.

// no spam · unsubscribe one-click · free forever

Originally published on arxiv ↗
HomeModelsNews