The document discusses the importance of cyber security threat detection, highlighting evolving threats such as malware and phishing. It outlines methods for malware detection, including signature-based, heuristic analysis, and machine learning approaches, as well as techniques for phishing detection that involve inspecting email and URLs. Emphasis is placed on identifying and mitigating security threats to protect sensitive data and systems.

1. Cyber Security
Threat Detection
In today's digital age, cyber security is paramount. We face a
constant barrage of evolving threats, including malware and
phishing attempts, aimed at stealing data and compromising
systems. Understanding these threats is crucial to building a
secure digital landscape.

2. Introduction to Malware
and Phishing Threats
1 Malware
Malware is malicious
software designed to harm
computer systems or steal
sensitive data. Examples
include viruses, worms,
trojans, and ransomware.
2 Phishing
Phishing attempts to trick
users into divulging personal
information such as
passwords or credit card
details, often through
deceptive emails or
websites.
3 Cybercriminals
Cybercriminals leverage these threats to gain unauthorized access,
disrupt operations, or extort money from individuals and
organizations.

3. Malware Detection: Objectives and Approach
Objectives
Identify and isolate malicious files before they can
cause harm. Analyze suspicious files to understand
their behavior and capabilities.
Approach
Utilize a combination of static and dynamic analysis
techniques. Examine file structure and code, observe
file behavior in controlled environments, and compare
against known malware signatures.

4. Malware Detection: File Analysis Techniques
1
Signature-Based Detection
Compare file content against a database of known malware
signatures, effectively detecting known threats.
2
Heuristic Analysis
Analyze file behavior based on predefined rules and
patterns, identifying potential malware based on suspicious
actions.
3
Sandboxing
Run suspicious files in a controlled virtual environment to
observe their behavior without impacting the actual system.

5. Malware Detection using Machine Learning:
Methodology
(TEXT VECTORIZATION + MACHINE LEARNING
MODEL)
Dataset
Microsoft Malware Classification Challenge Dataset: Contains
disassembled bytecode for various malware and benign files.

6. Phishing Detection:
Objectives and Approach
Objectives Identify and block phishing
emails before they reach users.
Analyze suspicious URLs to
determine if they are legitimate
or malicious.
Approach Employ email filtering
techniques, analyze email
content and sender details for
red flags, and scrutinize URLs
for suspicious patterns.

7. Phishing Detection: Email
Inspection
Sender Verification
Check the sender's email
address and domain name for
legitimacy and potential
spoofing.
Content Analysis
Look for grammatical errors,
suspicious language, and
unusual requests for personal
information.
Link Analysis
Verify the URL embedded in the email to ensure it aligns with the
expected domain and content.

8. Phishing Detection: URL Analysis
SSL Certificate
Check for a valid SSL certificate
indicating a secure connection, ensuring
data transmission security.
Domain Reputation
Assess the domain's reputation,
considering its history of phishing
activities or malicious associations.
URL Structure
Analyze the URL structure for unusual
characters, unexpected redirects, or
unusual domain names.