What is Data Diddling in Cyber Security

Table of Contents

Introduction to Data Diddling

Data Diddling in Cyber Security involves altering data before it is processed by a computer, or altering how the data is entered into the system. This attack manipulates the actual input process, thus affecting the output without the need to directly alter programs or databases. It is one of the earliest forms of cyber attacks and remains relevant as it can be hard to detect until the fraudulent data has already had a significant impact.

Data Diddling is a form of cyber attack or mischievous activity involving the manipulation of data before or during its entry into a computer system. This malicious activity can result in unauthorized changes to raw data, leading to a variety of potential harms and frauds. Below, I’ll elaborate on this concept with a structured explanation.

Types of Data Diddling in Cyber Security

Types of Data Diddling
  1. Input Manipulation: The attacker changes the data input either manually or using malicious software, before it is processed by the computer.
  2. Software Tampering: Altering the software that processes data to corrupt the results, often by inserting malicious code that modifies the data as it is entered.
  3. Data Tampering during Transmission: Intercepting and altering data as it travels to its destination, often seen in network communications.

Examples

  • Payroll Fraud: Manipulating the amount on payroll before it is entered into the payroll system.
  • Grade Tampering in Educational Institutions: Altering students’ grades before they are entered into the academic records.
  • Manipulation of Transaction Data: In financial sectors, altering transaction details before they are processed to divert funds.

Real World Examples of Data Diddling

  1. Payroll Fraud:
    • An employee modifies their own salary details in the company’s payroll system to receive a higher paycheck.
    • Alternatively, an attacker gains unauthorized access to the payroll system and alters the salary information for multiple employees.
  2. Inventory Manipulation:
    • A store employee changes the stock levels in the inventory management system to hide theft or divert goods for personal gain.
    • Similarly, an external attacker might alter inventory data to create discrepancies and exploit them.
  3. Financial Transactions:
    • A bank employee modifies account balances or transaction records to inappropriate funds.
    • In electronic payment systems, an attacker may alter transaction amounts or recipient details to transfer money.
  4. Academic Records:
    • A student changes their grades in the university’s database to improve their academic standing.
    • Unauthorized access to student records can also lead to grade manipulation.
  5. Healthcare Fraud:
    • A healthcare provider manipulates medical bills by changing patient records or treatment details.
    • Fraudsters may also manipulate insurance claims data to receive higher reimbursements.
  6. Voting Systems:
    • Altering voter registration data, vote counts, or candidate preferences in electronic voting systems can impact election outcomes.
    • Such manipulation can damage the democratic process.

Always remember that data diddling is an unethical and illegal practice. Organizations must implement robust security measures to prevent and detect such fraudulent activities. 🔒🚫

How to be Safe from Data Diddling?

Detecting data diddling can be challenging due to its discreet nature. However, Protecting against data diddling requires a combination of technical controls, policy enforcement, and vigilance. Here’s how organizations and individuals can safeguard their data:

1. Implement String Access Controls: Access controls are critical to ensuring that only authorized personnel have access to sensitive data and data entry systems. This includes:

  • User Authentication: Utilize multi-factor authentication to strengthen access security.
  • Role-based Access Control (RBAC): Limit user access based on their roles within the organization.
  • Least Privilege Principle: Ensure that users have only the minimum level of access necessary to perform their job functions.

2. User Input Validation: Input validation is a technique used to ensure that only properly formatted data is entered into a system. This can prevent malicious data from being processed and can include:

  • Data Type Checks: Ensure that the data matches the expected type (e.g., numeric, text).
  • Range Checks: Verify that values fall within acceptable parameters.
  • Format Checks: Confirm that data adheres to specific formatting rules (e.g., date formats).

3. Employ Encryption:  Encrypt data during transmission to prevent unauthorized interception and alteration. Encryption ensures that data is transformed into a secure format that is unreadable without the corresponding decryption key.

This is particularly important for data being transmitted over unsecured or public networks.

Prevention from Data Diddling in Cyber Security

4. Maintain Audit Trails: Keeping detailed logs of data transactions and system access can help in detecting and investigating suspicious activities:

  • Transaction Logs: Record details of all data entries and modifications.
  • Access Logs: Track who accessed data and when.

5. Conduct Regular Audits: Regular audits of both systems and data help ensure that any discrepancies or malicious activities are detected early:

  • Internal Audits: Periodically review systems and data handling processes.
  • External Audits: Employ third-party services to validate and assess the security measures in place.
Encryption in Data Diddling

6. Implement Data Integrity Checks: Use checksums, hashes, and other data integrity verification methods to ensure that data has not been altered without authorization:

  • Validate data integrity at various points, such as before and after data transmission or storage.
  • Use cryptographic signatures to verify the authenticity and integrity of data.

7. Educate and Train Employees: Since human error or insider threats can lead to data diddling, educating employees about cybersecurity risks and proper data handling procedures is crucial:

  • Security Awareness Training: Regularly educate employees on the latest security threats and best practices.
  • Phishing Training: Teach employees how to recognize and respond to phishing attempts, which can be a precursor to data manipulation attacks.

8. Develop and Enforce Security Policies: Creating clear, enforceable policies regarding data handling and security can provide guidelines and expectations for employees:

  • Specify how data should be handled and protected.
  • Develop and maintain a plan for responding to data security breaches, including steps for prevention and investigation.

Conclusion

Data Diddling represents a significant threat in the realm of cybersecurity, primarily due to its ability to go undetected and its potential to cause extensive damage. It is crucial for organizations to implement strong security measures, regular monitoring, and strict access controls to protect against such attacks. Awareness and training are equally important to recognize and prevent data diddling.

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FAQ's

Typically, data diddling is done to obtain financial advantage or to hide other illegal activity. An employee might, for instance, lie about financial records to transfer money into their own account, or a hacker could alter data to change how a transaction turns out or obtain sensitive information without authorization.

A salami attack is a type of cybercrime that hackers and attackers commonly use to steal money. One by one, cybercriminals take away funds or other resources from a system’s financial accounts. This attack happens when a number of smaller attacks come together to form a stronger attack.

Unauthorized modification, deletion, or other interference with data kept on a computer, server, or other storage medium is referred to as data alteration. Cybercrime of this kind can take many different forms, including: breaking into and changing websites, data removal from a server.

The fraudulent practice of repeatedly stealing extremely small amounts of money is known as penny shaving, by utilizing financial transactions that are rounded to the nearest cent. Making the change so tiny that no transaction is noticed is the aim.

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