ECCouncil Updated 312-50v13 Exam Questions and Answers by enid

In a Windows network, nongovernmental organization (New Technology) local area network Manager (NTLM) could be a suite of Microsoft security protocols supposed to produce authentication, integrity, and confidentiality to users.NTLM is that the successor to the authentication protocol in Microsoft local area network Manager (LANMAN), Associate in Nursing older Microsoft product. The NTLM protocol suite is enforced in an exceedingly Security Support supplier, which mixes the local area network Manager authentication protocol, NTLMv1, NTLMv2 and NTLM2 Session protocols in an exceedingly single package. whether or not these protocols area unit used or will be used on a system is ruled by cluster Policy settings, that totally different|completely different} versions of Windows have different default settings. NTLM passwords area unit thought-about weak as a result of they will be brute-forced very simply with fashionable hardware.

NTLM could be a challenge-response authentication protocol that uses 3 messages to authenticate a consumer in an exceedingly affiliation orientating setting (connectionless is similar), and a fourth extra message if integrity is desired.

First, the consumer establishes a network path to the server and sends a NEGOTIATE_MESSAGE advertising its capabilities.

Next, the server responds with CHALLENGE_MESSAGE that is employed to determine the identity of the consumer.

Finally, the consumer responds to the challenge with Associate in Nursing AUTHENTICATE_MESSAGE.

The NTLM protocol uses one or each of 2 hashed word values, each of that are keep on the server (or domain controller), and that through a scarcity of seasoning area unit word equivalent, that means that if you grab the hash price from the server, you’ll evidence while not knowing the particular word. the 2 area unit the lm Hash (a DES-based operate applied to the primary fourteen chars of the word born-again to the standard eight bit laptop charset for the language), and also the nt Hash (MD4 of the insufficient endian UTF-16 Unicode password). each hash values area unit sixteen bytes (128 bits) every.

The NTLM protocol additionally uses one among 2 a method functions, looking on the NTLM version. National Trust LanMan and NTLM version one use the DES primarily based LanMan a method operate (LMOWF), whereas National TrustLMv2 uses the NT MD4 primarily based a method operate (NTOWF).

DNS poisoning (also known as DNS cache poisoning) occurs when a malicious actor injects false DNS data into a DNS resolver's cache. The poisoned entry will persist for the duration of its TTL (Time To Live), which is defined in the DNS SOA (Start of Authority) record.

The SOA record contains several fields including:

Serial number

Refresh

Retry

Expire

Minimum TTL

The Minimum TTL value in the SOA record determines how long a DNS resolver should cache the DNS data — including any potentially poisoned data.

From CEH v13 Official Courseware:

Module 3: Scanning Networks

Topic: DNS Enumeration & Poisoning

CEH v13 Study Guide states:

“The SOA record includes a minimum TTL value that dictates how long DNS information should be cached by other DNS servers. If DNS cache poisoning occurs, the false information will persist until the TTL expires.”

Incorrect Options:

A: MX (Mail Exchange) defines mail servers, not TTLs.

C: NS (Name Server) specifies authoritative servers, not caching durations.

D: TIMEOUT is not a valid DNS resource record.

YARA rules are a powerful way to detect and classify malware based on patterns, signatures, and behaviors. They can be used to complement Snort rules, which are mainly focused on network traffic analysis. However, writing YARA rules manually can be time-consuming and error-prone, especially when dealing with large and diverse malware samples. Therefore, using a tool that can automate or assist the generation of YARA rules can be very helpful for ethical hackers.

Among the four options, yarGen is the best choice for this purpose, because it generates YARA rules from strings identified in malware files while removing strings that also appear in goodware files. This way, yarGen can reduce the false positives and increase the accuracy of the YARA rules. yarGen also supports various features, such as whitelisting, scoring, wildcards, and regular expressions, to improve the quality and efficiency of the YARA rules.

The other options are not as suitable as yarGen for this purpose. AutoYara is a tool that automates the generation of YARA rules from a set of malicious and benign files, but it does not perform any filtering or optimization of the strings, which may result in noisy and ineffective YARA rules. YaraRET is a tool that helps in reverse engineering Trojans to generate YARA rules, but it is limited to a specific type of malware and requires manual intervention and analysis. koodous is a platform that combines social networking with antivirus signatures and YARA rules to detect malware, but it is not a tool for generating YARA rules, rather it is a tool for sharing and collaborating on YARA rules. References:

yarGen - A Tool to Generate YARA Rules

YARA Rules: The Basics

Why master YARA: from routine to extreme threat hunting cases

In CEH v13 Module 02: Footprinting and Reconnaissance, Dark Web Footprinting is discussed as an advanced form of reconnaissance where attackers access hidden services and data using anonymity networks such as Tor (The Onion Router), I2P, or Freenet. These networks enable access to the deep web and dark web, where unindexed, and often illicit, content resides.

Key points relevant to this scenario:

The attacker encrypted browsing traffic and navigated anonymously, which strongly implies the use of tools like Tor or VPNs to mask identity.

The attacker used specialized tools/search engines like:

Torch

Ahmia

DarkSearch

Candle

The goal was to find sensitive or hidden information in government or federal systems — a common dark web footprinting objective.

The final step involved an attack that left no trace, which aligns with using the dark web for anonymity and obfuscation.

Option Analysis:

A. Dark web footprinting

Correct. This matches the behavior described: encrypted, anonymous access to sensitive information through dark web tools.

B. VoIP footprinting ❌

Incorrect. VoIP footprinting relates to identifying vulnerabilities or metadata in Voice over IP systems, not anonymous browsing or dark web activities.

C. VPN footprinting ❌

Incorrect. While VPNs may be used as part of anonymity, VPN footprinting refers to identifying systems using VPNs — not the act of gathering data anonymously.

D. Website footprinting ❌

Incorrect. Website footprinting involves gathering information from public-facing websites, like WHOIS data, robots.txt, and HTML metadata — not hidden dark web content.

Reference from CEH v13 Study Guide and Courseware:

Module 02 – Footprinting and Reconnaissance, Section: Footprinting through Dark Web and Deep Web

CEH v13 iLabs: Using Tor and Dark Web Search Engines for Reconnaissance

CEH Engage – Phase 1 (Reconnaissance): Dark Web Intelligence Gathering