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In cybersecurity, skewing refers to the targeted manipulation of data to distort the results of analysis or machine learning models. This type of attack is often referred to as a skewing attack or data poisoning attack and can significantly impair the effectiveness and reliability of models, such as those used to detect anomalies or fraud.
How does skewing work?
Skewing attacks aim to manipulate a machine learning model into making false or unwanted predictions. This manipulation is done by making targeted changes to the training or input data to “skew” the model’s decision making.
- Analyze the target: Understand the model
When launching a skewing attack, the attacker starts by analyzing the target model to determine which data patterns and properties are crucial to the model’s predictions. To do this, the attacker examines how the model responds to different inputs and the criteria it uses to classify data points as “normal” or “anomalous”. This phase is crucial because the attacker will use the insights to manipulate the data patterns later on.
- Creating manipulated data: Targeted distortion of the model
Based on the analysis of the target model, the attacker creates manipulated data to mislead the model. This can be done in two ways:
- Data poisoning: The attacker deliberately inserts manipulated data into the model’s training set. This causes the model to learn false patterns or to weight certain patterns as insignificant.
- Input manipulation: The attacker manipulates data directly when it enters the model. This method is often used with real-time models, such as anomaly detection systems, and aims to immediately deceive the model.
- Manipulating distribution and patterns: Statistical patterns shift
The targeted insertion or modification of data changes the statistical distribution of the data set. This is done in a way that gives the model a different view of the data. Examples include:
- Over-representing certain patterns: The attacker ensures that certain harmless patterns are strongly represented in the data set. The model then recognizes these over-represented patterns as “normal”.
- Camouflaging dangerous patterns: Dangerous data patterns are combined with harmless properties in the manipulated data so that the model does not classify them as threatening.
- Reinforcement of false classification: The model is distorted
Due to the changes in data distribution and pattern preferences, the model begins to classify certain data incorrectly. For example, it ignores dangerous anomalies because it considers them “normal” or it has been made hypersensitive to harmless data. This bias increases over time, causing the model to make increasingly inaccurate predictions, which the attacker can use to their advantage.
- Exploiting the security vulnerability: Bypassing the manipulated model
Once the model has been successfully skewed, the attacker begins to exploit this vulnerability. They can carry out attacks or illegal activities without being detected as a threat. Since the model classifies the manipulated data as harmless, the attacker is able to effectively deceive the system.
What are the consequences of skewing?
The consequences of skewing attacks can be significant, especially for companies and systems that rely on machine learning models to detect threats, fraud attempts or anomalies.
Skewing attacks can cause threats and anomalies to go undetected, creating security vulnerabilities and leaving organizations vulnerable to cyberattacks. The financial losses from undetected fraud and necessary recoveries can be significant, and organizations also risk loss of trust and damage to their reputation, especially in security-sensitive industries. Skewing can similarly lead to incorrect decision-making due to the inaccuracy of model predictions. Skewing makes it more difficult to update and further develop models, requires increased security measures, and leads to more complex and costly error diagnoses. These attacks thus not only undermine security, but also the economic efficiency and reliability of the systems.
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Which industries are typically affected by skewing?
In theory, skewing attacks can affect any industry that uses machine learning models and data-driven systems. However, some industries are particularly vulnerable to such attacks because they rely on the correct detection of anomalies and the processing of large amounts of data.
This is especially apparent in the financial industry, where banks and financial institutions use automated systems to detect fraud and assess risk. Manipulation here can result in fraudulent activities or risky borrowers going undetected, which can result in significant financial losses.
The cybersecurity industry is also heavily affected because it relies on automated threat detection. Skewing attacks can create security gaps because suspicious activity is no longer recognized as potential risks. This increases the risk that attacks or unauthorized access to networks will go unnoticed.
In the healthcare sector, large amounts of data are analyzed for diagnosis and patient monitoring. Skewing can result in medical anomalies being overlooked or misdiagnosed. This not only endangers patients but can also have serious consequences for healthcare facilities.
The e-commerce and retail sector is also vulnerable because automated systems are used for fraud detection and personalized recommendations. Manipulation could cause fraudulent transactions to appear legitimate or distort purchase recommendations. This can affect customer trust in the platform.
In the insurance industry, data-based systems are used for risk analysis and claims processing. A skewing attack could cause high-risk activities to go undetected or lead to the acceptance of unjustified claims, making risk assessment and premium calculations more difficult.
Transportation and logistics rely on data-driven systems to monitor supply chains and analyze driver behavior. Skewing attacks could result in the manipulation of routes, delivery times or security checks, for example, affecting efficiency and reliability.
In the energy sector, data is used to detect anomalies in electricity consumption and to control the energy supply. Skewing could result in unusual consumption patterns or threats to the power grid going unnoticed.
Manufacturing and production industries are also affected, where quality control and predictive maintenance systems are in use. Skewing attacks can lead to production errors or machine failures not being detected, which can result in quality issues and downtime.
How can companies protect themselves?
To protect themselves from skewing attacks, companies can take several important measures to improve the resilience of their machine learning models and systems.
Data quality and monitoring
Quality controls and regular audits of training data help to detect manipulated or unusual data patterns early on. Companies should monitor data sources and ensure that only reliable, verified data is used for training.
Robust model architectures
Models should be designed to be insensitive to small changes in the data. Robustness and stress tests can check how the model reacts to potentially manipulated data, thus revealing vulnerabilities.
Anomaly detection for input data
The implementation of anomaly detection systems can help to identify and block unusual or unexpected data patterns at an early stage. This ensures that potentially manipulated data from skewing attacks is not used for training or decision-making.
Regularly re-train models
By regularly re-training, companies can ensure that their models remain up to date and adapted to new threats. Models should be trained and tested with fresh, unfiltered data to avoid bias from old, manipulated data.
Use ensemble models
Ensemble models combine multiple models that work in parallel and learn from each other. This increases resilience, as attackers find it more difficult to manipulate every model at the same time. Consolidating the results of different models can help to reduce false classifications due to skewing.
Transparency and monitoring
Companies should set up detailed monitoring systems that make model decisions and data flows comprehensible. This makes it easier to detect and analyze anomalies or inconsistent classifications.
Access and data security measures
Access to sensitive data and models should be strictly regulated. Access controls and data encryption are essential to prevent unauthorized access to training data. This minimizes the risk of data being manipulated from within the company.
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