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Labeling “Things”
Internet of Things (IoT) devices are in our homes. Many, unbeknownst to us, intrude on our privacy. Labeling IoT products to inform consumers, offering understandable and relevant information about the “things,” is discussed.

Inside the Windows Security Push: A Twenty-Year Retrospective 🔐
This article is a follow-up to an article on the Windows security push in the first issue of IEEE Security and Privacy (January 2003). It provides additional detail on the security push and its results, and describes the creation and evolution of the Security Development Lifecycle that integrated software security into Microsoft's development process. The article concludes with a summary of lessons learned about effective ways of creating secure software at scale.

Fuzzy Hashing on Firmwares Images: A Comparative Analysis
Due to the widespread phenomenon that different IoT firmwares reuse the same code, code similarity comparison is an important technique for IoT security analysis. Fuzzy hashing generates fingerprints of files by converting them into intermediate expressions and hashing such expressions, evaluating the fingerprint similarity and thus evaluating the similarity of files that are not identical. This article analyzes and compares today’s most widely used fuzzy hashing tools, and classify them in detail.

BitAnalysis: A Visualization System for Bitcoin Wallet Investigation
Bitcoin is gaining ever increasing popularity. However, professional skills are required if people want to check bitcoin transaction information from the blockchain. There is a lack of tools to support effective interactive investigation of bitcoin transactions. This article presents a novel visualization system, BitAnalysis, for interactive bitcoin wallet investigation. 

Deep Learning for Face Anti-Spoofing: A Survey
Face anti-spoofing (FAS) has lately attracted increasing attention due to its vital role in securing face recognition systems from presentation attacks (PAs). As more and more realistic PAs with novel types spring up, early-stage FAS methods based on handcrafted features become unreliable due to their limited representation capacity. This paper presents the first comprehensive review of recent advances in deep learning based FAS. 

Are Machine Learning Models for Malware Detection Ready for Prime Time? 🔐
This article investigates why the performance of machine learning models for malware detection observed in a lab setting often cannot be reproduced in practice.

Revisiting Binary Code Similarity Analysis Using Interpretable Feature Engineering and Lessons Learned
Binary code similarity analysis (BCSA) is widely used for diverse security applications, including plagiarism detection, software license violation detection, and vulnerability discovery. Despite the surging research interest in BCSA, it is significantly challenging to perform new research in this field for several reasons. 

How do Developers Really Feel About Bug Fixing? Directions for Automatic Program Repair
Automatic program repair (APR) is a rapidly advancing field of software engineering that aims to supplement or replace manual bug fixing with an automated tool. For APR to be successfully adopted in industry, it is vital that APR tools respond to developer needs and preferences.  This article responds to this gap by reporting on a survey of 386 software developers about their bug finding and fixing practices and experiences, and their instinctive attitudes towards APR.