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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Wireless Networks | 5% | - Security Best Practices - Wireless Encryption: WEP, WPA2, WPA3 - Wireless Threats & Attacks - Wireless Hacking Tools |
| Topic 2: Mobile Platforms | 4% | - Mobile Attack Vectors - Android & iOS Vulnerabilities - Mobile Device Security |
| Topic 3: Scanning Networks | 8% | - Scanning Beyond IDS/Firewall - Host & Port Discovery - Service & OS Fingerprinting - Scanning Countermeasures - Network Scanning Basics - AI-Assisted Scanning |
| Topic 4: Enumeration | 7% | - DNS, SMTP, NFS Enumeration - Enumeration Concepts - Enumeration Countermeasures - AI-Driven Enumeration - NetBIOS, SNMP, LDAP Enumeration |
| Topic 5: Session Hijacking | 4% | - Hijacking Techniques - Application & Network Level Hijacking - Session Hijacking Concepts - Countermeasures |
| Topic 6: Footprinting and Reconnaissance | 7% | - Reconnaissance Concepts - OSINT Techniques - DNS, WHOIS, Network Mapping - Reconnaissance Countermeasures |
| Topic 7: Vulnerability Analysis | 8% | - Vulnerability Classification & Scoring - Vulnerability Research & Databases - Scanning & Analysis Tools - Vulnerability Assessment Lifecycle |
| Topic 8: Denial-of-Service | 4% | - Defense Mechanisms - Attack Techniques & Botnets - DDoS Tools - DoS & DDoS Concepts |
| Topic 9: IoT & OT Security | 4% | - Security Controls - Attacks on IoT & OT Systems - IoT/OT Architecture & Risks |
| Topic 10: Introduction to Ethical Hacking | 5% | - Information Security Concepts - Cyber Kill Chain & MITRE ATT&CK - Ethical Hacking Methodology - Legal and Ethical Compliance |
| Topic 11: Sniffing | 5% | - Packet Sniffing Concepts - Sniffing Tools & Techniques - Sniffing Countermeasures - MITM Attacks |
| Topic 12: Malware Threats | 7% | - APT & Fileless Malware - Malware Types: Trojans, Viruses, Worms - AI-Powered Malware - Malware Analysis & Countermeasures |
| Topic 13: Evading IDS, Firewalls, and Honeypots | 5% | - IDS, IPS, Firewall Technologies - Evasion Techniques - Honeypot Concepts & Detection |
| Topic 14: Web Server & Application Attacks | 8% | - Web Server Vulnerabilities - SQL Injection & Command Injection - Web Application Attacks: XSS, CSRF - Web Security Countermeasures - API Security Risks |
| Topic 15: Cloud Computing | 5% | - Cloud Security Best Practices - AWS, Azure, GCP Attacks - Cloud Security Risks - Cloud Models & Services |
| Topic 16: Social Engineering | 6% | - Identity Theft - Countermeasures & Awareness - Social Engineering Concepts - Phishing, Pretexting, Baiting |
| Topic 17: Cryptography | 5% | - Encryption Concepts & Algorithms - Cryptanalysis & Attacks - Cryptography in Practice - Public Key Infrastructure |
| Topic 18: System Hacking | 8% | - Privilege Escalation - Clearing Tracks & Logs - Gaining Access: Password Attacks - Maintaining Access |
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NEW QUESTION # 310
What is lateral movement?
Answer: D
Explanation:
The correct answer is D, Network traversal. In CEH system hacking and post-exploitation concepts, lateral movement refers to an attacker moving across a compromised environment from one host, account, or resource to another. After gaining initial access, the attacker may enumerate systems, harvest credentials, abuse trust relationships, access shared resources, or use remote administration protocols to reach additional machines. The uploaded hacking methodology material describes the "takeover" phase as expanding control from a single host to other hosts in the network, which directly aligns with lateral movement. Pivoting is closely related, but it is more accurately a technique used to route traffic through a compromised system to reach otherwise inaccessible networks. Privilege escalation means increasing permissions on a system, and data exfiltration means stealing or transferring data out of the environment. Since the question asks what lateral movement is, the most precise answer is network traversal.
NEW QUESTION # 311
A security consultant is performing an authorized assessment of a regional healthcare provider's patient portal in Portland, Oregon. During testing, he observes that authenticated users are assigned session identifiers embedded within URL parameters after login.
To evaluate the robustness of the session management implementation, he initiates multiple authentication requests in rapid succession using controlled test accounts. He then compares the issued identifiers and notices that although parts of the value remain constant, certain segments change in a predictable progression over time.
By analyzing the incremental pattern across a controlled batch of issued identifiers generated within the same time window, he is able to anticipate future valid identifiers without capturing traffic from other users.
Which token prediction mechanism best explains the weakness identified in this scenario?
Answer: A
Explanation:
The correct answer is C. Sequential Tokens.
The scenario states that session identifiers contain constant portions and other portions that change in a predictable progression. The tester can anticipate future valid identifiers by analyzing an incremental pattern.
That is the defining characteristic of sequential token generation.
CEH web application material identifies session token prediction as a session management attack used to impersonate legitimate users when predictable session values can be guessed or derived .
Option A is incorrect because small token space means there are too few possible token values, making brute- force guessing easier.
Option B is incorrect because timestamp-based tokens are derived mainly from time values. The scenario emphasizes an incremental sequence, not direct timestamp derivation.
Option D is incorrect because a weak PRNG may produce predictable values, but the question specifically describes a sequential pattern.
Therefore, the best answer is C. Sequential Tokens.
NEW QUESTION # 312
During an internal red team simu-lation at a global insurance provider, Joe, a senior SOC analyst, is assigned to verify a surge in anomalous SYN packets targeting the perimeter firewall. The result of spoofed traffic. The organization has ruled out DNS poisoning and malformed header issues. Joe must now analyze packet behavior in real-time to determine authenticity without relying on host-level authentication. To identify spoofed traffic using techniques aligned with best practices taught in the organization, which approach should Joe take?
Answer: D
Explanation:
In CEH-aligned traffic analysis, SYN floods frequently use spoofed source IP addresses to hide the attacker and complicate filtering. When host-level authentication or end-host validation is not available, defenders rely on characteristics in packet behavior that are difficult to forge consistently at scale. IP Identification Number monitoring is a classic technique for spotting spoofing because many operating systems generate the IPID field in a predictable manner, often incrementing sequentially or following a repeatable pattern per host or per flow.If the observed SYN packets truly originate from the same claimed source, the IPID values tend to show a coherent progression over time. In spoofed traffic, especially when the attacker randomly changes source IPs or uses multiple generators, IPID values typically appear inconsistent, jump erratically, or reflect patterns that do not match what would be expected from the alleged sender. This makes IPID monitoring useful in real- time packet analysis to differentiate legitimate traffic bursts from fabricated packets without needing to complete a handshake or query the end host.The other options are weaker fits for the specific goal. TCP flow control relates to windowing and throughput behavior, not source authenticity. IP address decoy is a deception concept rather than a packet-authenticity test. Direct TTL probes can provide hints about hop distance, but TTL values are easily adjusted by an attacker and are less reliable than correlating a consistent IPID sequence pattern. Therefore, IPID monitoring best matches CEH-style spoofed-traffic identification under these constraints.
NEW QUESTION # 313
Malware infecting multiple systems remains dormant until triggered and changes its code or encryption with each infection to evade detection. Which malware type best fits this description, and what is the most effective mitigation?
Answer: C
Explanation:
The CEH Malware Threats module defines polymorphic malware as malicious code that mutates its appearance (code, encryption, packing) each time it propagates, making signature-based detection ineffective. Dormancy and trigger-based activation are also common characteristics.
CEH emphasizes that behavior-based detection, sandboxing, and heuristic analysis are the most effective countermeasures against polymorphic threats.
Option D is correct.
Options A, B, and C do not address polymorphic evasion techniques.
NEW QUESTION # 314
The network team has well-established procedures to follow for creating new rules on the firewall. This includes having approval from a manager prior to implementing any new rules. While reviewing the firewall configuration, you notice a recently implemented rule but cannot locate manager approval for it. What would be a good step to have in the procedures for a situation like this?
Answer: A
NEW QUESTION # 315
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