Ergebnis für "Dark" in Data Science

Mastering OSINT and Dark Web Investigations: by Eric Waldrep is an advanced guide for professionals navigating the complex worlds of Open Source Intelligence (OSINT) and the Dark Web. The book equips investigators with technical skills, methodologies, and ethical frameworks to handle cases ranging from corporate fraud to national security threats.

Phishing Dark Waters addresses the growing and continuing scourge of phishing emails, and provides actionable defensive techniques and tools to help you steer clear of malicious emails. Phishing is analyzed from the viewpoint of human decision-making and the impact of deliberate influence and manipulation on the recipient. With expert guidance, this book provides insight into the financial, corporate espionage, nation state, and identity theft goals of the attackers, and teaches you how to spot a spoofed e-mail or cloned website.

Dark Data explores the many ways in which we can be blind to missing data and how that can lead us to conclusions and actions that are mistaken, dangerous, or even disastrous. Examining a wealth of real-life examples, from the Challenger shuttle explosion to complex financial frauds, Hand gives us a practical taxonomy of the types of dark data that exist and the situations in which they can arise, so that we can learn to recognize and control for them.

Simply put, Dark Data is stored, largely non-inventoried, unstructured data not currently used for the purpose of conducting data science, but which is nevertheless maintained on a "just in case" basis - either to meet regulatory requirements, or in the hope that the data will prove useful for research purposes at some time in the future.