Data diddling is a form of cybercrime or manipulation where data is altered before or during its entry into a system. This tampering typically happens between the time data is input and when it is processed or stored. The primary goal of data diddling is to modify data in ways that benefit the perpetrator or harm the target.
Key Characteristics of Data Diddling:
Data Alteration:
- The data that is entered into a system can be changed either manually or through automated processes.
- Common changes include altering financial transactions, modifying account balances, or tweaking any sensitive input data that influences outcomes.
Entry Points:
- Data can be altered before it is entered into the system, during processing, or while it is being transmitted.
- This is common in industries where data integrity is critical, such as banking, finance, healthcare, and government records.
Non-Destructive:
- Unlike many cyberattacks that may involve destruction or encryption of data (e.g., ransomware), data diddling usually involves subtle changes that can go unnoticed for long periods.
- These small alterations might be harder to detect but can lead to significant financial or reputational damage over time.
Execution Methods:
- Manual Data Diddling: This occurs when an insider or someone with access to the system changes the data deliberately.
- Automated Data Diddling: Hackers may use scripts or malicious software to automatically modify data at specific points in a system.
Targets:
- Financial institutions, where transactional data is manipulated to siphon off small amounts from multiple accounts.
- Payroll systems, where an employee's salary is altered.
- Voting systems, where votes might be modified.
- Healthcare systems, where medical records or insurance claims can be tampered with.
Real-world Examples of Data Diddling:
- Banking: A bank employee might modify the amount of money being deposited or withdrawn from accounts, skimming small amounts over time that go unnoticed.
- Payroll Systems: An insider could alter the salary amounts before the data is processed, causing discrepancies in payments.
- Inventory Systems: Changing quantities of goods to show fewer items in stock than what is physically present, allowing someone to steal unnoticed.
Preventing Data Diddling:
- Data Integrity Checks: Implementing strong validation processes ensures that data entered into the system is accurate.
- Access Control: Limiting access to sensitive data to authorized personnel can reduce the risk of insider tampering.
- Encryption: Ensuring that data is encrypted during transmission and storage can prevent unauthorized alterations.
- Audit Trails: Keeping detailed logs of all data entry activities helps to identify when and where data changes are made, making it easier to detect tampering.
- Regular Audits: Conducting frequent audits of sensitive data systems can help identify any discrepancies early on.
Consequences of Data Diddling:
- Financial Loss: In cases of fraud, data diddling can result in financial losses for organizations and individuals.
- Reputational Damage: If data manipulation is detected, the organization’s credibility may be severely impacted.
- Legal Implications: Data tampering, especially in regulated industries like banking and healthcare, can lead to lawsuits and regulatory penalties.
Types of Data Diddling -
1. Pre-Data Entry Diddling:
- In this type, data is altered before it enters the system. The manipulation occurs at the point of data input, such as in physical forms, manual logs, or even digital records that are being inputted into the system.
- Example: An employee might modify an invoice before entering it into the accounting software to inflate the cost or falsify a transaction.
2. Post-Data Entry Diddling:
- This form occurs after the data has been entered into the system but before it is processed or used. The altered data is manipulated while it resides in the system, before any final calculations or conclusions are made.
- Example: A bank employee might access a transaction before it is finalized to change the amount being transferred or deposited, effectively altering the outcome without the customer knowing.
3. Real-Time Data Diddling:
- Here, the manipulation happens while the data is in transit, usually between the source of entry and the system where it will be processed. Real-time data streams can be intercepted and altered by attackers, allowing them to change values or information during the transmission.
- Example: During a money transfer, an attacker intercepts the data and modifies the recipient’s account number or amount being transferred.
4. Transaction-Based Diddling:
- This involves altering the records of specific transactions, particularly in financial systems. A user or attacker changes transaction values in such a way that it benefits them or causes harm to the original parties.
- Example: A payroll employee alters the amount of salary being processed in the system for themselves or other employees, either inflating or deflating payments.
5. Data Format Manipulation:
- In this case, the actual structure or format of the data is changed in such a way that the system processes the data incorrectly, leading to errors or misuse of the information.
- Example: Modifying database formats, such as date or number fields, which can cause system malfunctions or trigger incorrect data analysis.
6. Falsification of Data Entries:
- This occurs when users deliberately enter incorrect or misleading data into a system to skew results. The aim is to achieve a desired outcome by falsifying input data.
- Example: A sales representative enters fake customer information to meet quotas or targets. Similarly, a worker may input false overtime hours into a payroll system.
7. Log Tampering:
- Attackers or insiders manipulate system logs, either deleting or altering records of transactions or activities to cover their tracks or create a misleading audit trail.
- Example: After a fraudulent transaction is made, the attacker alters the system’s logs to remove or change details that would have exposed the unauthorized transaction.
8. Data Aggregation Manipulation:
- In systems where data from multiple sources is aggregated for analysis or reporting, the perpetrator manipulates these aggregation points to distort the final results. This can be particularly harmful in financial reporting or market analysis.
- Example: Changing sales figures or inventory numbers to influence reports on business performance.
9. User Account Manipulation:
- In some cases, attackers may alter data related to user accounts, such as access permissions, account balances, or personal details, to either gain unauthorized access or defraud the system.
- Example: A hacker modifies the balance of a bank account by tampering with the system’s records before it reflects in a customer’s online banking portal.
10. Time-Based Data Diddling:
- Some attackers manipulate timestamps or other temporal data to create misleading records or outcomes. This is commonly seen in time-sensitive systems where timing plays a critical role in transaction processing.
- Example: Changing the date or time on financial records to manipulate deadlines or make it appear that a transaction occurred at a different time than it actually did.
11. Batch Data Manipulation:
- In environments where data is processed in batches, perpetrators might target the entire batch of data, modifying multiple records at once to avoid detection.
- Example: Modifying a batch of payroll records so that certain employees receive more than they are entitled to.
12. Statistical Manipulation:
- This type involves changing data in a way that affects statistical analysis or reporting. This can be seen in industries like healthcare, government statistics, and marketing, where outcomes are heavily dependent on accurate data.
- Example: Altering survey results or public health data to produce skewed reports for policy decisions.
Preventing Data Diddling:
- Encryption: Encrypting data in transit and at rest to prevent unauthorized modifications.
- Strong Authentication: Limiting access to systems only to authorized users with multi-factor authentication.
- Audit Trails: Implementing audit logs that track data changes, including who accessed and modified the data.
- Data Validation: Using validation rules to ensure that only correct and expected data is accepted into the system.
- Regular Audits: Conducting frequent reviews of system logs, data inputs, and financial records to identify potential anomalies.
