312-50v13 Exam Dumps - Certified Ethical Hacker Exam (CEHv13)

SYN/FIN scanning using IP fragments is a process of scanning that was developed to avoid false positives generated by other scans because of a packet filtering device on the target system. The TCP header splits into several packets to evade the packet filter. For any transmission, every TCP header must have the source and destination port for the initial packet (8-octet, 64-bit). The initialized flags in the next packet allow the remote host to reassemble the packets upon receipt via an Internet protocol module that detects the fragmented data packets using field-equivalent values of the source, destination, protocol, and identification.

https://www.infocyte.com/blog/2019/02/16/cybersecurity-101-what-you-need-to-know-about-false-positives-and-false-negatives/

False positives are mislabeled security alerts, indicating there is a threat when in actuality, there isn’t. These false/non-malicious alerts (SIEM events) increase noise for already over-worked security teams and can include software bugs, poorly written software, or unrecognized network traffic.

False negatives are uncaught cyber threats — overlooked by security tooling because they’re dormant, highly sophisticated (i.e. file-less or capable of lateral movement) or the security infrastructure in place lacks the technological ability to detect these attacks.

The most plausible reason for the situation is that the secure LDAP connection was not properly initialized due to a lack of ‘use_ssl = True’ in the server object creation. To use secure LDAP (LDAPS), the CEH needs to specify the use_ssl parameter as True when creating the server object with the ldap3 library in Python. This parameter tells the library to use SSL/TLS encryption for the LDAP communication. If the parameter is omitted or set to False, the library will use plain LDAP, which may not be accepted by the target system that only allows secure LDAP connections12. For example, the CEH can use the following code to create a secure LDAP server object:

from ldap3 import Server, Connection, ALL

server = Server('ldaps://', use_ssl=True, get_info=ALL)

connection = Connection(server, user='<username>', password='')

connection.bind()

The other options are not as plausible as option B for the following reasons:

A. The Python version installed on the CEH’s machine is incompatible with the ldap3 library: This option is unlikely because the ldap3 library supports Python versions from 2.6 to 3.9, which covers most of the commonly used Python versions3. Moreover, if the Python version was incompatible, the CEH would not be able to install the library or import it in the code, and would encounter errors before establishing the connection.

C. The enumeration process was blocked by the target system’s intrusion detection system: This option is possible but not very plausible because the CEH was able to establish a connection with the target, which means the intrusion detection system did not block the initial handshake. Moreover, the enumeration process would not affect the response of the target system, but rather the visibility of the results. If the intrusion detection system detected and blocked the enumeration, the CEH would receive an error message or a blank response, not an unexpected response.

D. The system failed to establish a connection due to an incorrect port number: This option is incorrect because the CEH was able to establish a connection with the target, which means the port number was correct. If the port number was incorrect, the CEH would not be able to connect to the target system at all, and would receive a connection refused error.

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

The Snort rule in the image is detecting suspicious bind attempts over DCERPC (Distributed Computing Environment/Remote Procedure Call), specifically targeting ports 135 (RPC) and 445 (SMB) with crafted content. The rule references CVE CAN-2003-0352.

CVE-2003-0352 is associated with the DCOM RPC vulnerability in Microsoft Windows that was exploited by the MS Blaster (also known as Lovsan) worm in 2003.

Key Indicators from the Snort Rule:

alert tcp $EXTERNAL_NET any -> $HOME_NET 135

content includes DCERPC binding pattern (|05| and |0b| with specific binary patterns)

Reference to CVE-2003-0352

Class type: attempted-admin

The MS Blaster worm exploited this vulnerability by sending a specially crafted RPC request to port 135, allowing remote code execution.

From CEH v13 Courseware:

Module 6: Malware Threats

Module 11: Session Hijacking

Discussion of historic worms and their exploit signatures, including MS Blaster.

Incorrect Options:

A. WebDav: Typically uses HTTP/HTTPS and was exploited by Nimda.

B. SQL Slammer: Targeted UDP port 1434 (SQL Server), not TCP 135/445.

D. MyDoom: Spread via email and exploited Windows file-sharing mechanisms (port 3127), not DCERPC.

Encrypting all sensitive data stored on the device is the best measure to ensure the security of this data, because it protects the data from unauthorized access or disclosure, even if the device is lost, stolen, or compromised. Encryption is a process of transforming data into an unreadable format using a secret key or algorithm. Only authorized parties who have the correct key or algorithm can decrypt and access the data. Encryption can be applied to data at rest, such as files or databases, or data in transit, such as network traffic or messages. Encryption can prevent attackers from stealing or tampering with the customer data stored on the device, such as credit card details and PINs, which can cause financial or identity fraud.

The other options are not as effective or sufficient as encryption for securing the customer data stored on the device. Implementing biometric authentication for app access may provide an additional layer of security, but it does not protect the data from being accessed by other means, such as malware, physical access, or backup extraction. Enabling GPS tracking for all devices using the app may help locate the device in case of loss or theft, but it does not prevent the data from being accessed by unauthorized parties, and it may also pose privacy risks. Regularly updating the app to the latest version may help fix bugs or vulnerabilities, but it does not guarantee the security of the data, especially if the app does not use encryption or other security features. 

In CEH v13 Module 01: Information Security Governance, PCI-DSS (Payment Card Industry Data Security Standard) is introduced as the mandatory compliance framework for organizations handling credit card transactions.

PCI-DSS Requirements Include:

Encrypting cardholder data.

Maintaining secure systems and applications.

Regular vulnerability testing and audits.

Restricting access to sensitive data.

Option Clarification:

A. FISMA: Applies to U.S. federal information systems.

B. HITECH: Related to health information privacy and HIPAA.

C. PCI-DSS: Correct for credit card companies and merchant environments.

D. SOX (Sarbanes-Oxley): Focuses on financial reporting, not card data.

Comprehensive and Detailed Explanation:

IDS cannot easily detect malicious content in encrypted traffic. Since the payload is encrypted (e.g., HTTPS, SSL/TLS), it is unreadable without decryption.

Statement B is therefore FALSE.

From CEH v13 Courseware:

Module 13: IDS, Firewalls and Honeypots → Limitations of IDS