Code Obfuscation: A Comprehensive Approach to Detection, Classification, and Ethical Challenges

Round 1

Reviewer 1 Report

Comments and Suggestions for Authors

In this paper authors have used machine learning models to detect code obfuscation in files.

The title and structure of the paper is not aligning well. Machine learning is used for detection of code obfuscation but the structure of the paper also talks about classification and ethics. I would suggest authors to focus on one area to make the paper more readable. There should be a clear research question and paper should be structured around that.

Introduction is well written; it would be good to have structure of remaining paper in the last paragraph of introduction.

In table 3, how the values of high, medium extensive, high are given? Is it based on authors perception?

Section 2-7 provides background information but it is important to comprehensively review the literature and come up with a knowledge gap, which is not the case in the manuscript. It would be better to have a related work section and highlight knowledge gap.

There should be a materials and methods section and some subsections of section 8 need to be moved in this section.

There is a need for presenting the result based on selected research question in introduction.

There is a need for discussion section where the findings are referred back to scientific discourse, practical and managerial implications are discussed and future directions also listed.

Author Response

In this paper authors have used machine learning models to detect code obfuscation in files.

• The title and structure of the paper is not aligning well - Machine learning is used for detection of code obfuscation but the structure of the paper also talks about classification and ethics. I would suggest authors to focus on one area to make the paper more readable - Challenges in Code Obfuscation: Detection, Classification and Ethics 

"Code Obfuscation: A Comprehensive Approach to Detection, Classification, and Ethical Challenges"

 

1. There should be a clear research question and paper should be structured around that.

Our article article's focus on 3 main issues:

1. Understanding the application of obfuscation techniques in securing software.
2. Evaluating machine learning models (Random Forest, Gradient Boosting, SVM) for detecting obfuscation.
3. Exploring the ethical tensions between obfuscation, transparency, and user trust.

the research question can be: How we effectively and ethically detect and classify code obfuscation techniques using machine learning models.

1. Introduction is well written; it would be good to have structure of remaining paper in the last paragraph of introduction. 

Section 2 provides a detailed overview of code obfuscation techniques, including lexical, control flow, and data obfuscation, along with their respective strengths and limitations. Section 3 introduces the machine learning models employed for obfuscation detection and classification, detailing the feature engineering process, dataset preparation, and evaluation metrics. 

Section 4 discusses the experimental results, comparing the performance of the detection models across various obfuscation tools and techniques. 

Section 5 addresses the ethical implications of code obfuscation, focusing on transparency, user trust, and potential misuse. 

Section 6 explores the practical applications of obfuscation detection in areas such as cybersecurity audits and malware analysis. 

Finally, Section 7 concludes the paper with a summary of findings, implications for future research, and recommendations for the responsible use of obfuscation techniques in software development.

 

1. In table 3, how the values of high, medium extensive, high are given? Is it based on authors perception? - Table 3 presents a comparison of the key features of these obfuscation tools, based on the authors' subjective perceptions as well as feedback from various users, gathered through comments and discussion in social media. One can run an experiment in which groups of people will be given the task of reverse-engineering several versions of obfuscated and non-obfuscated code and the time/quality and subjective difficulty would be measured. But this is out of the scope of this paper.  להזיז לעמוד הקודם
2. Section 2-7 provides background information but it is important to comprehensively review the literature and come up with a knowledge gap,which is not the case in the manuscript. It would be better to have a related work section and highlight knowledge gap.

While the literature provides substantial insights into the techniques, metrics, and detection methods for code obfuscation, four critical gaps remain: 1. Adaptive obfuscation detection models that can generalize effectively across obfuscators. 2. Practical ways to detect and classify code obfuscation. 3. Actionable ethical guidelines for the use of code obfuscation. 4. Countermeasures to detection and classification of obfuscated code. 

1. There should be a materials and methods section and some subsections of section 8 need to be moved in this section. 

Added as subsections 2.2-2.4

1. There is a need for presenting the result based on selected research question in introduction. 

Referred to it in the conclusion section 

1. There is a need for discussion section where the findings are referred back to scientific discourse, practical and managerial implications are discussed and future directions also listed. 

Appears in the conclusion section

Reviewer 2 Report

Comments and Suggestions for Authors

The article addresses the relevant topic of code obfuscation in modern programming. The authors conduct experimental research and analyze key machine learning models.
However, the work has certain shortcomings that reduce its scientific and practical value.

Comment 1: Insufficient consideration of obfuscation quality measurement

Recommendations for improvement:

1. Refinement of criteria:

-        The article does not sufficiently address metrics for evaluating the quality of obfuscated code, such as readability, performance, and cost. Include an analysis of additional quality metrics to enrich the theoretical foundation (e.g., from "Obfuscated Code Quality Measurement").

-        More clearly link metrics (e.g., String Entropy) to criteria such as Potency, Resilience, and Stealth.

-        Provide examples or experiments demonstrating how specific metric values affect the complexity of reverse engineering.

2. Quantitative evaluation of security impact:

-        Conduct tests to show how obfuscation with high Potency or Resilience values slows down or blocks deobfuscation efforts.

-         Measure the actual time required to breach various obfuscation methods.

3. Performance analysis:

-        Add data on how obfuscation affects software performance to consider cost implications.

Comment 2: Limited justification for model selection

-        While Random Forest, Gradient Boosting, and SVM models deliver good results, the reasons for choosing these over alternatives (e.g., neural networks) are not explained.

Recommendations for improvement:

-        Provide additional justification and compare results with other approaches.

Comment 3: Lack of validation on other datasets

-        All experiments are focused on the tools mentioned in the article. The model’s universality with other datasets is not sufficiently addressed.

Recommendations for improvement:

-        Test the proposed method on datasets different from those used in the current experiments.

Comment 4: Ethical aspect is superficially addressed

-        Although the ethical aspect is stated, no concrete recommendations or solutions are proposed to mitigate these risks.

Recommendations for improvement:

-        Consider real-world examples of ethical dilemmas related to obfuscation.

Comment 5: Limited explanation of tool selection

-        The article includes a comparative table of tools but does not explain why these specific tools were chosen.

Recommendations for improvement:

Add justification for the selection and consider alternative tools.

Author Response

The article addresses the relevant topic of code obfuscation in modern programming. The authors conduct experimental research and analyze key machine learning models.
However, the work has certain shortcomings that reduce its scientific and practical value.

Comment 1: Insufficient consideration of obfuscation quality measurement

Recommendations for improvement:

1. Refinement of criteria:

-        The article does not sufficiently address metrics for evaluating the quality of obfuscated code, such as readability, performance, and cost. Include an analysis of additional quality metrics to enrich the theoretical foundation (e.g., from "Obfuscated Code Quality Measurement"). 

-        More clearly link metrics (e.g., String Entropy) to criteria such as Potency, Resilience, and Stealth

-        Provide examples or experiments demonstrating how specific metric values affect the complexity of reverse engineering. 

1. Quantitative evaluation of security impact:

-        Conduct tests to show how obfuscation with high Potency or Resilience values slows down or blocks deobfuscation efforts. 

-         Measure the actual time required to breach various obfuscation methods. 

1. Performance analysis:

-        Add data on how obfuscation affects software performance to consider cost implications. 

 

As I understand it, the second reviewer is asking us to mention and address two additional articles that focus on Obfuscated Code Quality Measurement. 

Semenov, S., Davydov, V., & Voloshyn, D. (2019, September). Obfuscated Code Quality Measurement. In 2019 XXIX International Scientific Symposium" Metrology and Metrology Assurance"(MMA) (pp. 1-6). IEEE.

Ebad, Shouki A., Abdulbasit A. Darem, and Jemal H. Abawajy. "Measuring software obfuscation quality–a systematic literature review." IEEE Access 9 (2021): 99024-99038.

While we all agree on the foundational principles and challenges of code obfuscation, our focus differs from these articles.

The two referenced articles emphasize the theoretical underpinnings and metrics for evaluating obfuscation quality, whereas we provide a practical perspective, integrating advanced detection models, use cases, and ethical discussions into the analysis.  I agree that combining these, could offer a holistic view of obfuscation's technical and ethical dimensions, we chose to maintain a distinct focus.

Common points of view

Both the referenced articles and our own work highlight the two primary objectives of obfuscation:

1. Increasing code complexity to prevent reverse engineering.
2. Safeguarding intellectual property by concealing software logic and proprietary algorithms.

Metrics such as potency, resilience, stealth, and cost are identified as essential for assessing obfuscation quality. Additionally, all three works recognize key challenges, including maintenance difficulties, debugging complexities, and ethical implications. These challenges encompass reduced transparency, potential introduction of bugs, and concerns about hidden malicious features.

All the articles emphasize the tension between obfuscation's benefits for intellectual property protection and its ethical drawbacks, such as reduced user trust and hidden vulnerabilities.

Key Differences

1. Difference 1
• Our work explores machine learning models like Random Forest, Gradient Boosting, and Support Vector Machines to classify obfuscated files. These models demonstrate high accuracy in identifying obfuscation techniques used by tools such as PyArmor and Jlaive.
• In contrast, the other two articles focus on theoretical metrics such as Span, Fitzpatrick, and Shannon entropy to quantify the effects of obfuscation.
2. Difference 2
• The two referenced articles detail specific statistical features like average token length, comment density, and the standard deviation of token length to assess obfuscation quality.
• Our work addresses broader categories such as control flow obfuscation and lexical obfuscation without delving deeply into statistical metrics.
3. Difference 3
• We extend the discussion to practical applications of deobfuscation, particularly in security audits and malware analysis. Our article includes use cases for reversing obfuscation.
• The other two articles focus primarily on refining obfuscation evaluation methods and do not address practical deobfuscation scenarios.
4. Depth of Ethical Analysis
• Our article dedicates significant attention to ethical challenges, such as balancing privacy protection with the potential for misuse in malicious activities.
• The other two articles touch on ethical concerns as part of broader considerations but do not deeply analyze their practical implications.

 

Comment 2: Limited justification for model selection

-        While Random Forest, Gradient Boosting, and SVM models deliver good results, the reasons for choosing these over alternatives (e.g., neural networks) are not explained. 

Recommendations for improvement:

-        Provide additional justification and compare results with other approaches. 

 

Comment 3: Lack of validation on other datasets

-        All experiments are focused on the tools mentioned in the article. The model’s universality with other datasets is not sufficiently addressed. 

 

Recommendations for improvement:

-        Test the proposed method on datasets different from those used in the current experiments

Comment 4: Ethical aspect is superficially addressed

-        Although the ethical aspect is stated, no concrete recommendations or solutions are proposed to mitigate these risks. 

Recommendations for improvement:

-        Consider real-world examples of ethical dilemmas related to obfuscation. 

We added an example story in the conclusion section

Ransomware Attacks: WannaCry is a ransomware attack that spread rapidly in May 2017. The attackers used obfuscation techniques to hide the malicious code within seemingly legitimate files. It affected over 200,000 computers across 150 countries, causing significant disruptions in various sectors, including healthcare, where the UK's National Health Service (NHS) was severely impacted.

Emotet Malware: Emotet started as a banking Trojan and evolved into a modular malware that delivers other types of malware. Attackers used obfuscation techniques to hide the malicious payload within email attachments or links. Emotet has been responsible for numerous phishing campaigns, leading to data breaches and financial losses for individuals and organizations worldwide.

APT28 (Fancy Bear): APT28, also known as Fancy Bear, is a Russian cyber espionage group. They have used obfuscation techniques to hide their malware and evade detection by security systems. APT28 has been linked to several high-profile cyber espionage campaigns, including the breach of the Democratic National Committee (DNC) in 2016.

SolarWinds Attack: In the SolarWinds attack, attackers inserted obfuscated malicious code into the Orion software updates. This allowed them to gain access to the networks of thousands of organizations, including several U.S. government agencies. The attack led to significant breaches of sensitive information and highlighted the vulnerabilities in supply chain security.

Cryptojacking: Coinhive was a JavaScript-based cryptocurrency miner that was often embedded in websites without the users' knowledge. Attackers used obfuscation to hide the mining script within legitimate web pages. This led to unauthorized use of visitors' computing resources to mine cryptocurrency, affecting the performance of their devices and leading to increased electricity costs.

Botnets: The Mirai botnet used obfuscation techniques to hide its code and make it difficult for security researchers to analyze and mitigate the threat. It targeted IoT devices with weak security. Mirai was responsible for some of the largest DDoS attacks in history, including the attack on Dyn in 2016, which disrupted major websites like Twitter, Netflix, and Reddit.

Comment 5: Limited explanation of tool selection

-        The article includes a comparative table of tools but does not explain why these specific tools were chosen. 

Recommendations for improvement:

Add justification for the selection and consider alternative tools. 

We chose a subset of the common tools

Reviewer 3 Report

Comments and Suggestions for Authors

I have the following concerns:

1. The paper covers a range of code obfuscation techniques and tools (e.g., Jlaive, PyObfuscate, Pyarmor), but it lacks a systematic taxonomy or categorization of these methods.

2. While the paper acknowledges ethical concerns, the discussion is underdeveloped and lacks references to prior ethical frameworks or guidelines.

3. The paper’s structure does not make the review comprehensive or easy to follow. The lack of a dedicated section for challenges and future directions makes it difficult to understand open research problems. 

Author Response

1. The paper covers a range of code obfuscation techniques and tools (e.g., Jlaive, PyObfuscate, Pyarmor), but it lacks a systematic taxonomy or categorization of these methods. 
2. While the paper acknowledges ethical concerns, the discussion is underdeveloped and lacks references to prior ethical frameworks or guidelines.

We’ve added some examples of ethical dilemmas

3. The paper’s structure does not make the review comprehensive or easy to follow. The lack of a dedicated section for challenges and future directions makes it difficult to understand open research problems. 

We’ve revised the structure as suggested

Round 2

Reviewer 1 Report

Comments and Suggestions for Authors

Authors have responded to my comments.

Reviewer 2 Report

Comments and Suggestions for Authors

The reviewer's comments have been addressed. Thank you

Reviewer 3 Report

Comments and Suggestions for Authors

The revised manuscript looks good to me. No more questions from my side.