Tech

Transformer Models Outperform Traditional Algorithms in Log Anomaly Detection

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Table of links Abstract 1 Introduction 2 Background and Related Work 2.1 Different Formulations of the Log-based Anomaly Detection Task 2.2 Supervised v.s. Unsupervised 2.3 Information within Log Data 2.4 Fix-Window Grouping 2.5 Related Works 3 A Configurable Transformer-based Anomaly Detection Approach 3.1 Problem Formulation 3.2 Log Parsing and Log Embedding 3.3 Positional & Temporal … Read more

How Transformer Models Detect Anomalies in System Logs

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Table of links Abstract 1 Introduction 2 Background and Related Work 2.1 Different Formulations of the Log-based Anomaly Detection Task 2.2 Supervised v.s. Unsupervised 2.3 Information within Log Data 2.4 Fix-Window Grouping 2.5 Related Works 3 A Configurable Transformer-based Anomaly Detection Approach 3.1 Problem Formulation 3.2 Log Parsing and Log Embedding 3.3 Positional & Temporal … Read more

Transformer-Based Anomaly Detection Using Log Sequence Embeddings

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Table of links Abstract 1 Introduction 2 Background and Related Work 2.1 Different Formulations of the Log-based Anomaly Detection Task 2.2 Supervised v.s. Unsupervised 2.3 Information within Log Data 2.4 Fix-Window Grouping 2.5 Related Works 3 A Configurable Transformer-based Anomaly Detection Approach 3.1 Problem Formulation 3.2 Log Parsing and Log Embedding 3.3 Positional & Temporal … Read more

An Overview of Log-Based Anomaly Detection Techniques

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Table of links Abstract 1 Introduction 2 Background and Related Work 2.1 Different Formulations of the Log-based Anomaly Detection Task 2.2 Supervised v.s. Unsupervised 2.3 Information within Log Data 2.4 Fix-Window Grouping 2.5 Related Works 3 A Configurable Transformer-based Anomaly Detection Approach 3.1 Problem Formulation 3.2 Log Parsing and Log Embedding 3.3 Positional & Temporal … Read more

A Transformer Approach to Log-Based Anomaly Detection

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:::info Authors: Xingfang Wu Heng Li Foutse Khomh ::: Table of links Abstract 1 Introduction 2 Background and Related Work 2.1 Different Formulations of the Log-based Anomaly Detection Task 2.2 Supervised v.s. Unsupervised 2.3 Information within Log Data 2.4 Fix-Window Grouping 2.5 Related Works 3 A Configurable Transformer-based Anomaly Detection Approach 3.1 Problem Formulation 3.2 … Read more

Coca-Cola’s new AI holiday ad is a sloppy eyesore

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Look at all those blobby bunnies. Coca-Cola is once again using generative AI to reimagine its classic Coke caravan holiday commercials, and in doing so, killing some of the festive joy you have for the brand. After receiving backlash for airing three AI-generated holiday commercials last year that featured gliding wheels and uncanny-looking faces, the … Read more

Meet Cwallet: HackerNoon Company of the Week

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Meet Cwallet: HackerNoon’s Company of the Week At HackerNoon, we celebrate tech companies that are shaping the future. This week, we’re featuring Cwallet from our evergreen tech company database. Cwallet is a pioneering fintech platform bridging the gap between crypto and real-world spending, and their latest upgrade proves they’re serious about redefining how people use … Read more

The AI industry is running on FOMO

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For Big Tech, a penny invested in AI is a penny earned… Maybe. After an indeterminate amount of time. Investors hope. On earnings calls last week, Amazon, Google, Microsoft, and Meta reported more than $350 billion this year on capital expenditures, or longer-tail investments in a company’s future. All four told investors to expect the … Read more

npm’s New Token Limits Won’t Stop the Attacks That Actually Happen

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npm’s new token lifetime limits (90-day max, 7-day default) and mandatory WebAuthn are good security hygiene, but they don’t address how attacks actually happen. The September 2025 breach that compromised 18 packages with 2.6B weekly downloads succeeded via phishing—the attacker had full account access and could generate tokens at will. The XZ Utils backdoor involved … Read more