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| Section | Weight | Objectives |
|---|---|---|
| Information Security and Ethical Hacking Overview | 6% | - Ethical Hacking Overview
|
| System Hacking | 17% | - System Hacking Methodologies
|
| Sniffing and Evasion | 10% | - Social Engineering
|
| Reconnaissance Techniques | 21% | - Footprinting and Reconnaissance
|
| Web Application Attacks | 19% | - Hacking Web Servers and Web Applications
|
| Cloud and Container Attacks | 10% | - Cloud Computing Concepts
|
| Vulnerability Analysis | 7% | - Vulnerability Assessment Concepts
|
| Enumeration | 15% | - Enumeration Concepts
|
| Cryptography and Post-Exploitation | 13% | - Post-Exploitation Techniques
|
| Wireless Network Attacks | 9% | - Wireless Network Concepts
|
| Malware Threats | 8% | - Malware Analysis and Distribution
|
| Mobile Platform and IoT Attacks | 7% | - IoT and OT Attacks
|
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NEW QUESTION # 649
During a penetration test at Rocky Mountain Insurance in Denver, ethical hacker Sophia Nguyen attempts to evade detection by fragmenting malicious traffic into smaller packets. The IT security team counters her strategy with a system that monitors traffic for deviations from established baselines, flagging behavior that does not match normal network activity. This allows them to stop Sophia's evasion attempts in real time. Which detection technique is the IT team most likely using in this case?
Answer: B
Explanation:
The system identifies traffic that deviates from normal patterns, such as fragmented or unusual packet sequences, by comparing activity against established baselines. This approach of detecting abnormal behavior is characteristic of anomaly-based detection.
NEW QUESTION # 650
At a cybersecurity consultancy firm in Boston, senior analyst Amanda Liu is called in to assess a malware outbreak affecting a regional healthcare provider. Despite using updated antivirus tools, the security team notices inconsistent detection across infected endpoints. Amanda discovers that while the malicious behavior is consistent, system file tampering and suspicious outbound traffic, each malware sample has a slightly different code structure and fails traditional hash-based comparison. Static analysis reveals that the underlying logic remains unchanged, but the code patterns vary unpredictably across infections. What type of virus is most likely responsible for this behavior?
Answer: C
Explanation:
A polymorphic virus is specifically designed to change its code appearance while keeping the same underlying functionality, which aligns exactly with the scenario. In CEH terms, polymorphism allows malware to mutate its decryptor routine, instruction ordering, register usage, junk code insertion, and other syntactic elements every time it propagates or executes. This causes each instance to look different at the binary level, producing different hashes and signatures, even though the malicious payload and behavior remain the same. That is why the security team sees inconsistent antivirus detection and why "traditional hash- based comparison" fails. The key indicator is that static analysis shows the "underlying logic remains unchanged," but "code patterns vary unpredictably," which is the hallmark of polymorphism: behavior stays consistent, signature changes.
The other options do not fit as well. A cavity virus typically hides by inserting itself into unused spaces within legitimate executable files to avoid changing the overall file size, but it does not inherently generate unpredictable code variants per infection. A macro virus primarily targets macro-enabled documents and spreads through document templates and user actions, which is not suggested here. A stealth virus focuses on evading detection by intercepting system calls and hiding its presence, such as returning "clean" file reads, but it does not necessarily produce many structurally different binaries that break hash matching. Therefore, the most likely cause of the described outbreak is a polymorphic virus.
NEW QUESTION # 651
A financial institution in Chicago deploys an internal HTTPS-based customer portal that uses response compression to optimize bandwidth. During an authorized security assessment, a tester gains a vantage point along the communication path between internal clients and the gateway device.
By repeatedly initiating controlled requests and analyzing subtle differences in encrypted response sizes, the tester correlates variations in compressed output with specific input patterns.
Over time, this analysis enables extraction of portions of a protected authentication value transmitted within the secure channel.
Which session hijacking technique best describes this activity?
Answer: B
Explanation:
The scenario describes exploiting HTTPS compression by analyzing differences in encrypted response sizes to infer sensitive data such as authentication values. This matches the CRIME attack, which leverages compression side-channel information leakage within secure communication channels.
NEW QUESTION # 652
A financial institution in New York deploys an AI-enabled security system that analyzes historical attack data and ongoing network behavior. Over several months, the platform modifies its detection models to recognize emerging attack patterns without requiring security engineers to manually rewrite detection rules.
As new exploit techniques appear, the system adjusts its internal logic to improve future detection accuracy.
Which capability of AI-driven ethical hacking is demonstrated in this scenario?
Answer: A
Explanation:
Adaptive Learning describes an AI system ' s ability to improve or modify its behavior as it receives new information and en counters changing attack patterns. The key clue is that the platform continually adjusts its detection model rather than depending on security engineers to manually rewrite static rules. Continuous monitoring refers primarily to persistent observation of systems and events. Predictive analysis uses historical information to forecast likely future events, whereas advanced threat detection describes the broader outcome of identifying sophisticated attacks. Here, the distinctive capability is the model ' s capacity to learn from new exploit behavior and refine subsequent detection decisions. EC-Council specifically positions AI and machine learning as a major CEH v13 technology update, and its CEH v13 training material explicitly lists Adaptive Learning among the AI-driven cybersecurity capabilities.
NEW QUESTION # 653
Attempting an injection attack on a web server based on responses to True/False questions is called which of the following?
Answer: C
Explanation:
https://en.wikipedia.org/wiki/SQL_injection#Blind_SQL_injection
Blind SQL injection is used when a web application is vulnerable to an SQL injection but the results of the injection are not visible to the attacker. The page with the vulnerability may not be one that displays data but will display differently depending on the results of a logical statement injected into the legitimate SQL statement called for that page. This type of attack has traditionally been considered time-intensive because a new statement needed to be crafted for each bit recovered, and depending on its structure, the attack may consist of many unsuccessful requests. Recent advancements have allowed each request to recover multiple bits, with no unsuccessful requests, allowing for more consistent and efficient extraction.
NEW QUESTION # 654
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