What is social engineering in cybersecurity?

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Supervised and Unsupervised Learning are two primary types of machine learning, differing mainly in how they process and learn from data.

Social engineering in cybersecurity is the practice of manipulating people into revealing confidential information or performing actions that compromise security, rather than directly hacking into systems.

Instead of attacking software, attackers exploit human psychology—such as trust, fear, urgency, or curiosity—to trick their targets.

Common examples include:

  • Phishing – Fake emails or messages that look legitimate, tricking you into clicking malicious links or giving away passwords.

  • Pretexting – Inventing a believable scenario (e.g., pretending to be IT support) to obtain sensitive information.

  • Baiting – Leaving infected USB drives or offering “free” downloads to lure victims into installing malware.

  • Tailgating – Following someone into a secure building without authorization.

  • Vishing – Voice phishing via phone calls pretending to be from banks, tech support, or authorities.

Why it’s dangerous:

  • It bypasses technical defenses by targeting the weakest link—people.

  • Can lead to identity theft, financial fraud, or system compromise.

  • Often serves as the first step in larger cyberattacks.

Think of it as “hacking the human” rather than the computer.

Read More

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