Journal on Information Security publishes original research and surveys addressing security and privacy challenges in which signal processing and data-centric methodologies play a central role. The Journal serves as a forum for work that combines theoretical foundations, algorithmic development, and system-level evaluation, particularly in settings involving noisy, partial, or adversarial data. The journal seeks contributions that advance the state of the art in areas including, but not limited to: Signal processing methods for security and privacy Adversarial detection, inference, and classification Robust and trustworthy machine learning grounded in signal and data representations Security and privacy of sensing systems and cyber-physical systems Biometrics, multimedia security, and forensic signal analysis Side channel analysis and signal level countermeasures Privacy-preserving signal and data analysis techniques Measurement-based security and large-scale data-driven security analysis Submissions are expected to demonstrate clear methodological contributions, sound experimental validation, and relevance to contemporary security or privacy problems. Out of Scope The Journal does not consider submissions that: Lack a clear connection between signal or data-centric methods and security or privacy objectives Are purely theoretical cryptography without signal or system-level components Rely primarily on chaos theory or chaos-based constructions, including chaos-based encryption or communication schemes, without demonstrated relevance to modern security models or real-world threat settings.