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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Malware Threats | 7% | - APT & Fileless Malware - Malware Analysis & Countermeasures - Malware Types: Trojans, Viruses, Worms - AI-Powered Malware |
| Topic 2: IoT & OT Security | 4% | - Attacks on IoT & OT Systems - IoT/OT Architecture & Risks - Security Controls |
| Topic 3: Session Hijacking | 4% | - Session Hijacking Concepts - Application & Network Level Hijacking - Countermeasures - Hijacking Techniques |
| Topic 4: Sniffing | 5% | - Sniffing Countermeasures - Sniffing Tools & Techniques - Packet Sniffing Concepts - MITM Attacks |
| Topic 5: Social Engineering | 6% | - Identity Theft - Phishing, Pretexting, Baiting - Countermeasures & Awareness - Social Engineering Concepts |
| Topic 6: Denial-of-Service | 4% | - DoS & DDoS Concepts - Defense Mechanisms - Attack Techniques & Botnets - DDoS Tools |
| Topic 7: Footprinting and Reconnaissance | 7% | - Reconnaissance Countermeasures - DNS, WHOIS, Network Mapping - OSINT Techniques - Reconnaissance Concepts |
| Topic 8: Cloud Computing | 5% | - AWS, Azure, GCP Attacks - Cloud Security Risks - Cloud Models & Services - Cloud Security Best Practices |
| Topic 9: Wireless Networks | 5% | - Security Best Practices - Wireless Hacking Tools - Wireless Encryption: WEP, WPA2, WPA3 - Wireless Threats & Attacks |
| Topic 10: Scanning Networks | 8% | - Scanning Countermeasures - AI-Assisted Scanning - Host & Port Discovery - Scanning Beyond IDS/Firewall - Network Scanning Basics - Service & OS Fingerprinting |
| Topic 11: Cryptography | 5% | - Public Key Infrastructure - Encryption Concepts & Algorithms - Cryptography in Practice - Cryptanalysis & Attacks |
| Topic 12: Web Server & Application Attacks | 8% | - SQL Injection & Command Injection - Web Application Attacks: XSS, CSRF - API Security Risks - Web Server Vulnerabilities - Web Security Countermeasures |
| Topic 13: Evading IDS, Firewalls, and Honeypots | 5% | - Evasion Techniques - Honeypot Concepts & Detection - IDS, IPS, Firewall Technologies |
| Topic 14: Mobile Platforms | 4% | - Mobile Device Security - Mobile Attack Vectors - Android & iOS Vulnerabilities |
| Topic 15: Introduction to Ethical Hacking | 5% | - Ethical Hacking Methodology - Legal and Ethical Compliance - Information Security Concepts - Cyber Kill Chain & MITRE ATT&CK |
| Topic 16: Enumeration | 7% | - AI-Driven Enumeration - Enumeration Countermeasures - Enumeration Concepts - NetBIOS, SNMP, LDAP Enumeration - DNS, SMTP, NFS Enumeration |
| Topic 17: System Hacking | 8% | - Gaining Access: Password Attacks - Maintaining Access - Clearing Tracks & Logs - Privilege Escalation |
| Topic 18: Vulnerability Analysis | 8% | - Vulnerability Research & Databases - Vulnerability Assessment Lifecycle - Scanning & Analysis Tools - Vulnerability Classification & Scoring |
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NEW QUESTION # 857
Emma, an ethical hacker at a Chicago-based healthcare provider, is performing a penetration test on the organization ' s patient record system following a recent data breach. During her investigation, she discovers that attackers gained access to a large volume of encrypted patient records but had no knowledge of the original data or encryption keys. Emma observes that the system uses a block cipher and suspects the attackers may have applied a cryptanalytic method that examines encrypted outputs in bulk to detect structural or statistical patterns in the encrypted data.
Which cryptanalysis technique should Emma investigate to assess the system ' s vulnerability in this scenario?
Answer: D
Explanation:
The correct answer is D, ciphertext-only attack, because the attackers possess only encrypted data and have neither the encryption key nor any confirmed samples of the original plaintext. In CEH-aligned cryptography concepts, cryptanalytic attacks are categorized by what information and capabilities the attacker has. A ciphertext-only attack is the most constrained model: the attacker attempts to infer the plaintext or key by analyzing ciphertext alone, often leveraging statistical properties, repetitions, formatting assumptions, and structural patterns that may leak information when encryption is misused.
The scenario highlights that the attacker "examines encrypted outputs in bulk" to detect "structural or statistical patterns." That wording maps directly to ciphertext-only analysis: looking for patterns caused by weak modes of operation, poor IV/nonce handling, deterministic encryption, repeated blocks (common in ECB mode), predictable headers, or consistent record templates. With block ciphers, large datasets increase the chance that repeated plaintext blocks produce repeated ciphertext blocks if the system uses an unsafe mode or reuses IVs, allowing an attacker to correlate records, identify fields, or sometimes recover content when combined with protocol or file-format knowledge.
The other options require more attacker capability than the prompt provides. Known-plaintext attacks require some plaintext-ciphertext pairs. Chosen-plaintext attacks require the ability to encrypt attacker-chosen inputs.
Chosen-ciphertext attacks require a decryption oracle or the ability to submit ciphertext for decryption. Since none of those are present here, Emma should evaluate ciphertext-only exposure and specifically validate the cipher mode, IV/nonce uniqueness, padding behavior, and whether encryption is semantically secure for stored patient records.
NEW QUESTION # 858
Ethical hacker jane Smith is attempting to perform an SQL injection attach. She wants to test the response time of a true or false response and wants to use a second command to determine whether the database will return true or false results for user IDs. which two SQL Injection types would give her the results she is looking for?
Answer: A
Explanation:
"Boolean based" we mean that it is based on Boolean values, that is, true or false / true and false. AND Time- based SQL Injection is an inferential SQL Injection technique that relies on sending an SQL query to the database which forces the database to wait for a specified amount of time (in seconds) before responding. The response time will indicate to the attacker whether the result of the query is TRUE or FALSE.
Boolean-based (content-based) Blind SQLi
Boolean-based SQL Injection is an inferential SQL Injection technique that relies on sending an SQL query to the database which forces the application to return a different result depending on whether the query returns a TRUE or FALSE result.
Depending on the result, the content within the HTTP response will change, or remain the same. This allows an attacker to infer if the payload used returned true or false, even though no data from the database is returned. This attack is typically slow (especially on large databases) since an attacker would need to enumerate a database, character by character.
Time-based Blind SQLi
Time-based SQL Injection is an inferential SQL Injection technique that relies on sending an SQL query to the database which forces the database to wait for a specified amount of time (in seconds) before responding.
The response time will indicate to the attacker whether the result of the query is TRUE or FALSE.
Depending on the result, an HTTP response will be returned with a delay, or returned immediately. This allows an attacker to infer if the payload used returned true or false, even though no data from the database is returned. This attack is typically slow (especially on large databases) since an attacker would need to enumerate a database character by character.
https://www.acunetix.com/websitesecurity/sql-injection2/
NEW QUESTION # 859
During a late-night shift at IronWave Logistics in Seattle, cybersecurity analyst Marcus Chen notices a pattern of high-port outbound traffic from over a dozen internal machines to a previously unseen external IP. Each system had recently received a disguised shipping report, which, when opened, initiated a process that spread autonomously to other workstations using shared folders and stolen credentials. Upon investigation, Marcus discovers that the machines now contain hidden executables that silently accept remote instructions and occasionally trigger coordinated background tasks. The compromised endpoints are behaving like zombies, and malware analysts confirm that the payload used worm-like propagation to deliver a backdoor component across the network.
Which is the most likely objective behind this attack?
Answer: C
Explanation:
The strongest indicator in this scenario is that multiple compromised hosts are "behaving like zombies," communicating outbound to a single unfamiliar external IP over high ports, and "silently accept remote instructions" while performing "coordinated background tasks." In CEH-aligned malware terminology, these are hallmark characteristics of a botnet: a collection of infected endpoints (bots/zombies) under centralized or semi-centralized command-and-control. Worm-like propagation explains how the compromise rapidly expanded across the internal network-using shared folders and stolen credentials for lateral spread-while the "backdoor component" provides persistent remote control functionality once a system is infected. The observed coordination across many hosts strongly suggests the attacker's goal is not merely individual surveillance of a single machine, but scalable remote control of many machines at once.
Option A, data exfiltration, is plausible in many intrusions, but the question emphasizes orchestration and remote tasking across many endpoints rather than targeted theft from specific repositories. Option B is inconsistent because there is no mention of encryption, ransom notes, or disruption-focused behavior. Option D, a RAT, typically describes remote control of a host, but the scenario's defining feature is the creation of many "zombies" with coordinated behavior-this aligns more precisely with building a botnet for command and control, which can later be used for data theft, DDoS, spam, or further intrusion operations.
CEH defensive guidance includes monitoring egress traffic anomalies, detecting C2 patterns, segmenting networks to limit worm spread, disabling unnecessary shares, enforcing strong credential hygiene, and using EDR to identify backdoors and lateral movement behaviors.
NEW QUESTION # 860
A cloud service provider in Singapore is refining its defensive monitoring strategy to identify large-scale denial-of-service attempts against hosted applications. The security engineering team wants a detection mechanism that continuously evaluates incoming traffic streams and statistically determines the exact moment when normal behavior shifts into anomalous activity.
Rather than relying solely on static baselines or historical comparisons, the team prefers an approach that detects abrupt deviations in real time by identifying structural breaks in traffic metrics as they occur.
Which DDoS detection technique best fits this requirement?
Answer: C
Explanation:
The correct answer is A. Sequential Change-Point Detection.
The scenario asks for a technique that detects the exact point where traffic behavior changes from normal to anomalous. This directly matches Sequential Change-Point Detection.
CEH DoS/DDoS detection material explains that change-point detection is used to detect DoS attacks and uses a non-parametric CUSUM algorithm to detect traffic patterns. It also notes that this method has low computational overhead and high accuracy .
Option B. Wavelet-Based Signal Analysis is incorrect because wavelet analysis focuses on signal decomposition and volume-based anomaly detection, not specifically identifying the exact statistical change point.
Option C. Traffic Pattern Analysis is too general and does not specifically describe structural-break detection.
Option D. Activity Profiling compares behavior against average traffic rates or baselines, while the question asks for abrupt real-time change-point detection.
Therefore, the best answer is A. Sequential Change-Point Detection.
NEW QUESTION # 861
Security administrator John Smith has noticed abnormal amounts of traffic coming from local computers at night. Upon reviewing, he finds that user data have been exfiltrated by an attacker.
AV tools are unable to find any malicious software, and the IDS/IPS has not reported on any non- whitelisted programs. What type of malware did the attacker use to bypass the company's application whitelisting?
Answer: C
NEW QUESTION # 862
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