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Security+ 101 > Malware > Flashcards

Malware Flashcards

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1
Q

Malware

A

broad term used to describe any type of malicious software designed to infiltrate, damage, or gain unauthorized access to computer systems or networks.

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2
Q

Identifying Ransomware:

A

Encrypted files with a ransom note demanding payment in exchange for decryption keys.

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3
Q

Identifying Trojans:

A

Unexpected or unauthorized access, changes in system settings, or suspicious network activity.

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4
Q

Identifying Worms:

A

Rapid self-replication and spreading across a network or multiple systems.

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5
Q

Identifying Potentially Unwanted Programs (PUPs):

A

Unwanted toolbars, adware, or browser extensions installed without user consent.

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6
Q

Identifying Fileless Virus:

A

Unusual or suspicious processes running in memory without traditional executable files.

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7
Q

Identifying Command and Control (C2):

A

Communication with external C2 servers, often via unusual or non-standard network ports.

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8
Q

Identifying Bots:

A

A network of compromised devices (botnet) controlled by a central command center.

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9
Q

Identifying Cryptomalware:

A

Encrypted files with a ransom demand or ransom payment address.

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10
Q

Identifying Logic Bombs:

A

Unexpected system or application behavior triggered by specific conditions or dates.

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11
Q

Identifying Spyware:

A

Unusual network traffic, unauthorized access to sensitive data, or suspicious system activity.

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12
Q

Identifying Keyloggers:

A

Unauthorized access to sensitive data or evidence of keystroke recording.

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13
Q

Identifying Remote Access Trojan (RAT)

A

Suspicious or unauthorized remote access or control of a system.

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14
Q

Identifying Rootkit:

A

Concealed processes or files, unusual system behavior, or unauthorized access.

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15
Q

Identifying Backdoor:

A

Unauthorized access, unusual network traffic, or the presence of hidden pathways.

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16
Q

Password Attacks

A

Password attacks are attempts by malicious actors to gain unauthorized access to a system or account by guessing or cracking passwords.

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17
Q

Identifying Password Spraying:

A

Multiple login attempts with the same password against multiple user accounts.

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18
Q

Identifying Dictionary Attack:

A

Repeated login attempts using words from a dictionary or common passwords.

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19
Q

Identifying Brute Force Attack (Online):

A

Continuous and rapid login attempts without delay between each attempt.

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20
Q

Identifying Brute Force Attack (Offline):

A

Theft or possession of hashed password data (e.g., from a database breach).

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21
Q

Identifying Rainbow Table Attack:

A

Rapid password cracking with the use of precomputed rainbow tables.

22
Q

Identifying Plaintext/Unencrypted Password Attack:

A

Passwords stored in plaintext format.

23
Q

Physical Attacks:

A
  • Malicious USB Cable
  • Malicious Flash Drive
  • Card Cloning
  • Skimming
24
Q

Identifying Malicious USB Cable:

A

The presence of a suspicious or unknown USB cable connected to a device, computer, or network.

25
Q

Identifying Malicious Flash Drive:

A

Discovery of unknown or unverified USB flash drives in the organization, especially in public areas or near workstations.

26
Q

Identifying Card Cloning:

A

Unusual or unauthorized transactions on payment cards or access control systems.

27
Q

Identifying Skimming:

A

Suspicious or unusual devices attached to card readers, ATMs, or payment terminals.

28
Q

Adversarial Artificial Intelligence (AI):

A
  • Unusual Model Behavior
  • Misclassification or Misbehavior
  • Anomalies in Model Confidence
  • Increased False Positives/Negatives
29
Q

Identifying Unusual Model Behavior:

A

If an AI model exhibits unexpected or erratic behavior, it could be an indicator of an adversarial AI attack. Adversaries may manipulate inputs to exploit vulnerabilities in the model.

30
Q

Identifying Misclassification or Misbehavior:

A

Frequent misclassification of inputs or outputs that do not align with the model’s intended behavior may indicate adversarial interference.

31
Q

Identifying Anomalies in Model Confidence:

A

If the model’s confidence scores fluctuate widely or show inconsistencies, it could suggest adversarial attempts to undermine the model’s accuracy.

32
Q

Identifying Increased False Positives/Negatives:

A

A noticeable increase in false positives or false negatives in AI-based security systems, such as intrusion detection or spam filters, might indicate adversarial attacks.

33
Q

Tainted Training Data for Machine Learning (ML):

A
  • Data Inconsistencies
  • Unusual Model Performance
  • Unexpected Bias
  • Data Source Anomalies
34
Q

Identifying Data Inconsistencies:

A

Analyze the training data for inconsistencies, inaccuracies, or anomalies that could indicate tampering or poisoning.

35
Q

Identifying Unusual Model Performance:

A

If the ML model exhibits poor or erratic performance, it may be a sign of tainted training data.

36
Q

Identifying Unexpected Bias:

A

Check for unexpected biases or discriminatory behavior in the ML model, which can be introduced through malicious data manipulation.

37
Q

Identifying Data Source Anomalies

A

Investigate the sources of training data for any signs of compromise, such as unauthorized access or alterations.

38
Q

Security of Machine Learning Algorithms:

A
  • Model Evasion
  • Unauthorized Access
  • Model Stealing
  • Abnormal Resource Usage
39
Q

Supply-Chain Attacks:

A
  • Unusual Network Activity
  • Unauthorized Access
  • Vendor Alerts
40
Q

Identifying Unusual Network Activity:

A

An increase in network traffic or unusual data transfers between systems within your supply chain may indicate a supply-chain attack.

41
Q

Identifying Unauthorized Access:

A

Suspicious login attempts or unauthorized access to systems or applications within your supply chain can be indicative of an attack.

42
Q

Identifying Vendor Alerts:

A

Notifications or alerts from your suppliers or vendors about a security breach or compromise on their end may signal a supply-chain attack.

43
Q

Cloud-Based vs. On-Premises Attacks:

A
  • Anomalous Cloud Activity
  • On-Premises Intrusion
  • Logs and Alerts
44
Q

Identifying Anomalous Cloud Activity:

A

In a cloud-based attack, you might observe unusual or unauthorized activities in your cloud services, such as accessing sensitive data, changing configurations, or spinning up new instances.

45
Q

Identifying On-Premises Intrusion:

A

In an on-premises attack, signs may include unusual system or network activity, unauthorized access to physical premises, or signs of tampering with hardware or servers.

46
Q

Identifying Logs and Alerts:

A

Monitor logs and security alerts from both cloud-based and on-premises systems to detect suspicious activities and breaches.

47
Q

Cryptographic Attacks:

A
  • Birthday Cryptographic Attack
  • Collision Cryptographic Attack
  • Downgrade Cryptographic Attack
48
Q

Identifying Birthday Cryptographic Attack:

A
  • A sudden increase in collisions in hash functions or unexpected hash collisions in your system logs.
  • Difficulty in verifying data integrity or authenticity due to hash collisions.
49
Q

Identifying Collision Cryptographic Attack:

A
  • Instances where two different inputs produce the same cryptographic hash.
  • Repeated failures in verifying digital signatures or certificates.
50
Q

Identifying Downgrade Cryptographic Attack:

A
  • Errors or issues with the negotiation of cryptographic protocols during secure communications.
  • Unexpectedly weak encryption algorithms being used in secure connections.

Security+ 101 (6 decks)

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