IELTS Reading: AI Trong An Ninh Mạng – Đề Thi Mẫu Có Đáp Án Chi Tiết

Mở Bài

Trí tuệ nhân tạo (AI) đang cách mạng hóa lĩnh vực an ninh mạng, trở thành chủ đề nóng trong các kỳ thi IELTS Reading gần đây. Chủ đề “How Is AI Being Used In Cybersecurity?” không chỉ phản ánh xu hướng công nghệ hiện đại mà còn xuất hiện thường xuyên trong các đề thi IELTS Academic từ năm 2020 đến nay, đặc biệt trong các passages về khoa học công nghệ và xã hội số.

Bài viết này cung cấp cho bạn một đề thi IELTS Reading hoàn chỉnh với ba passages đầy đủ, tăng dần từ mức độ dễ đến khó, giúp bạn làm quen với cách IELTS đánh giá khả năng đọc hiểu về chủ đề công nghệ phức tạp. Bạn sẽ được luyện tập với 40 câu hỏi đa dạng, từ True/False/Not Given, Multiple Choice đến Matching và Summary Completion – tất cả đều theo format thi thật.

Mỗi passage đi kèm đáp án chi tiết và giải thích cụ thể về cách xác định thông tin, kỹ thuật paraphrase, và chiến lược quản lý thời gian. Bạn cũng sẽ học được hơn 40 từ vựng quan trọng về AI và an ninh mạng, giúp nâng cao vốn từ học thuật cho kỳ thi.

Đề thi này phù hợp với học viên từ band 5.0 trở lên, mong muốn cải thiện kỹ năng đọc hiểu và đạt band điểm cao hơn trong IELTS Reading.

1. Hướng Dẫn Làm Bài IELTS Reading

Tổng Quan Về IELTS Reading Test

IELTS Reading Test kéo dài 60 phút với 3 passages và tổng cộng 40 câu hỏi. Mỗi passage dài khoảng 650-1000 từ và chứa đựng nội dung học thuật từ sách báo, tạp chí, và ấn phẩm khoa học. Độ khó tăng dần từ Passage 1 đến Passage 3.

Phân bổ thời gian khuyến nghị:

• Passage 1: 15-17 phút (13 câu hỏi)
• Passage 2: 18-20 phút (13 câu hỏi)
• Passage 3: 23-25 phút (14 câu hỏi)

Bạn cần ghi đáp án vào answer sheet trong thời gian 60 phút này, không có thời gian bổ sung như phần Listening.

Các Dạng Câu Hỏi Trong Đề Này

Đề thi mẫu này bao gồm 7 dạng câu hỏi phổ biến nhất trong IELTS Reading:

1. True/False/Not Given – Xác định thông tin có khớp với bài đọc không
2. Multiple Choice – Chọn đáp án đúng từ 3-4 phương án
3. Matching Headings – Ghép tiêu đề với đoạn văn phù hợp
4. Sentence Completion – Hoàn thành câu bằng từ trong bài
5. Summary Completion – Điền từ vào tóm tắt đoạn văn
6. Matching Features – Ghép thông tin với người/tổ chức/khái niệm
7. Short-answer Questions – Trả lời ngắn gọn theo yêu cầu

2. IELTS Reading Practice Test

PASSAGE 1 – The Rise of AI in Digital Defense

Độ khó: Easy (Band 5.0-6.5)

Thời gian đề xuất: 15-17 phút

In today’s digital landscape, cyberattacks have become increasingly sophisticated and frequent, posing significant threats to businesses, governments, and individuals worldwide. Traditional security measures, which rely heavily on rule-based systems and human monitoring, are struggling to keep pace with the evolving tactics of cybercriminals. This is where artificial intelligence (AI) is stepping in to revolutionize how we protect our digital assets.

AI’s ability to process and analyze vast amounts of data at unprecedented speeds makes it an ideal tool for cybersecurity. Unlike conventional systems that depend on predefined rules, AI-powered solutions can learn from patterns, adapt to new threats, and identify anomalies that might indicate a security breach. This adaptive capability is crucial in an environment where new types of attacks emerge daily.

One of the primary applications of AI in cybersecurity is threat detection. Traditional antivirus software works by comparing files against a database of known malware signatures. However, this approach becomes ineffective against zero-day attacks – threats that exploit previously unknown vulnerabilities. AI systems, particularly those using machine learning algorithms, can identify suspicious behavior even if it doesn’t match any known threat pattern. For example, if an AI system notices that a user account is suddenly accessing files it has never touched before, or logging in from an unusual location, it can flag this activity for investigation.

Network security is another area where AI is making a substantial impact. Large organizations process millions of network transactions every day, making it virtually impossible for human analysts to monitor everything. AI can continuously scan network traffic, detecting unusual patterns that might suggest a distributed denial-of-service (DDoS) attack, data exfiltration, or unauthorized access attempts. By analyzing normal baseline behavior, AI systems can quickly spot deviations and alert security teams before significant damage occurs.

Email security has also been transformed by AI technology. Phishing attacks, where criminals send fraudulent emails to trick recipients into revealing sensitive information, remain one of the most common cyberthreats. Modern AI-powered email filters go beyond simple keyword matching. They analyze the email’s content, sender behavior, link destinations, and even writing style to determine whether a message is legitimate or malicious. These systems learn from every email they process, constantly improving their ability to identify sophisticated phishing attempts that might fool traditional filters.

The speed at which AI can respond to threats is perhaps its most valuable characteristic. When a security breach occurs, the first few minutes are critical. AI systems can automatically implement countermeasures such as isolating affected systems, blocking suspicious IP addresses, or shutting down compromised accounts – all without waiting for human intervention. This rapid response capability can prevent a minor security incident from escalating into a major data breach.

Despite these advantages, experts emphasize that AI is not a replacement for human cybersecurity professionals. Rather, it serves as a powerful tool that augments human capabilities. AI handles the repetitive task of monitoring and initial threat assessment, allowing security experts to focus on strategic planning, investigating complex incidents, and making critical decisions that require human judgment. The most effective cybersecurity strategies combine AI’s computational power with human expertise and intuition.

As organizations increasingly adopt AI for cybersecurity, they must also be aware of potential limitations. AI systems require large amounts of quality data to train effectively, and they can sometimes generate false positives – flagging legitimate activities as threats. Additionally, cybercriminals are beginning to use AI themselves to create more sophisticated attacks, leading to an ongoing technological arms race in the cybersecurity field.

Questions 1-6

Do the following statements agree with the information given in Passage 1?

Write:

• TRUE if the statement agrees with the information
• FALSE if the statement contradicts the information
• NOT GIVEN if there is no information on this

1. Traditional security systems are adequate for dealing with modern cyber threats.
2. AI can identify security threats that don’t match any previously known patterns.
3. Zero-day attacks are the most common type of cybersecurity threat.
4. AI systems monitor network traffic more effectively than human analysts for large organizations.
5. Phishing emails are decreasing in number due to AI technology.
6. Cybercriminals have started using AI to develop more advanced attacks.

Questions 7-10

Complete the sentences below.

Choose NO MORE THAN TWO WORDS from the passage for each answer.

7. AI’s __ allows it to adjust to new threats without needing preprogrammed rules.
8. When AI detects unusual activity, it can automatically implement __ to prevent damage.
9. AI email filters analyze multiple factors including content, sender patterns, and __.
10. The combination of AI technology and __ creates the most effective cybersecurity approach.

Questions 11-13

Choose the correct letter, A, B, C or D.

11. According to the passage, what is the main advantage of AI over traditional antivirus software?

• A. It is cheaper to implement
• B. It can detect threats without matching them to known patterns
• C. It requires less maintenance
• D. It works faster on older computers

12. What does the passage suggest about false positives?

• A. They are a serious problem that prevents AI adoption
• B. They occur when AI systems lack sufficient training data
• C. They are potential issues that organizations should be aware of
• D. They happen more frequently than actual security breaches

13. The author’s main purpose in this passage is to:

• A. Argue that AI will replace human cybersecurity professionals
• B. Explain how AI is being applied in various cybersecurity contexts
• C. Criticize traditional security systems as outdated
• D. Warn about the dangers of cybercriminals using AI

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PASSAGE 2 – Machine Learning Algorithms: The Brain Behind Cyber Defense

Độ khó: Medium (Band 6.0-7.5)

Thời gian đề xuất: 18-20 phút

The integration of machine learning (ML) into cybersecurity represents a paradigm shift in how digital threats are identified and neutralized. While the concept of using AI for security purposes has been discussed for decades, recent advances in computational power and algorithmic sophistication have finally made it practical and effective. Understanding the specific types of machine learning algorithms employed in cybersecurity provides insight into why this technology has become indispensable for modern digital defense.

Supervised learning algorithms form the foundation of many cybersecurity applications. These algorithms are trained on labeled datasets containing examples of both normal and malicious activities. For instance, a supervised learning model designed to detect malware would be fed thousands of examples of legitimate software and known malware samples. Through this training process, the algorithm learns to distinguish between benign and harmful code by identifying characteristic features. Once trained, it can classify new, previously unseen files with remarkable accuracy. The primary challenge with supervised learning lies in obtaining sufficient high-quality labeled data, as cybersecurity experts must manually categorize each training example – a time-consuming and expensive process.

In contrast, unsupervised learning algorithms operate without pre-labeled data, making them particularly valuable for detecting novel threats. These algorithms analyze data to identify patterns and group similar items together without being told what to look for. In cybersecurity contexts, unsupervised learning excels at anomaly detection. By establishing what constitutes “normal” behavior for a network, user, or system, these algorithms can flag anything that deviates significantly from established patterns. This approach is especially effective against advanced persistent threats (APTs) – sophisticated, long-term intrusions where attackers maintain undetected access to systems for extended periods. Since APTs often involve subtle, gradual changes rather than obvious malicious actions, traditional security tools frequently miss them, but unsupervised ML algorithms can detect these subtle deviations from normal behavior.

Deep learning, a subset of machine learning inspired by the structure of the human brain, has proven particularly powerful in cybersecurity applications. Neural networks with multiple layers can automatically learn hierarchical representations of data, extracting increasingly abstract features at each layer. For example, in analyzing network traffic, lower layers might identify basic patterns like packet sizes and frequencies, while higher layers could recognize complex attack signatures or behavioral patterns indicating a security breach. Convolutional neural networks (CNNs), originally developed for image recognition, are now being adapted to analyze binary code and identify malware by treating executable files as images. Recurrent neural networks (RNNs), which excel at processing sequential data, are employed to detect anomalous patterns in time-series data such as user login sequences or system logs.

The application of natural language processing (NLP) – another AI technology – has opened new frontiers in threat intelligence and social engineering detection. NLP algorithms can analyze millions of online discussions, forum posts, and social media conversations to identify emerging threats, new exploit techniques, or plans for coordinated attacks. Security researchers use these tools to stay ahead of threat actors by understanding their intentions and methods. Additionally, NLP powers sophisticated phishing detection systems that analyze email content not just for keywords but for subtle linguistic indicators of deception, such as unusual phrasing, urgency manipulation, or impersonation attempts.

Reinforcement learning represents a fascinating application of AI in active cyber defense. Unlike supervised and unsupervised learning, reinforcement learning algorithms learn through trial and error, receiving rewards for successful actions and penalties for failures. In cybersecurity, these algorithms can be trained to respond to attacks by simulating thousands of scenarios and learning optimal response strategies. For example, a reinforcement learning system might learn the most effective way to isolate compromised systems, redistribute network traffic during a DDoS attack, or reconfigure firewalls in response to detected intrusions. Some advanced systems can even engage in adaptive defense, continuously adjusting security configurations based on the evolving threat landscape.

However, the implementation of machine learning in cybersecurity is not without challenges. Adversarial machine learning has emerged as a significant concern, where attackers deliberately craft inputs designed to fool ML algorithms. For instance, by making tiny, carefully calculated modifications to malware code, attackers can create adversarial examples that appear benign to ML detection systems. This has sparked a cat-and-mouse game where defenders develop more robust algorithms while attackers devise increasingly sophisticated evasion techniques. Research into adversarial robustness – making ML models resistant to such manipulation – has become a critical area of cybersecurity research.

The computational overhead associated with complex ML algorithms also presents practical challenges. Deep learning models, in particular, require substantial processing power and memory, which can be problematic for resource-constrained environments such as Internet of Things (IoT) devices or small business networks. Researchers are working on developing lightweight models and edge computing solutions that can perform AI-powered threat detection without requiring constant connection to powerful cloud-based systems. Federated learning, where models are trained across multiple decentralized devices without sharing raw data, offers a promising approach for implementing ML-based security in privacy-sensitive contexts.

Despite these challenges, the trajectory of machine learning in cybersecurity is clear. As algorithms become more sophisticated and computational resources more accessible, ML-powered security tools will become increasingly effective and ubiquitous. Organizations that embrace these technologies while remaining aware of their limitations position themselves to better withstand the escalating sophistication of cyber threats.

Thuật toán học máy trong hệ thống bảo mật mạng hiện đại với AI phát hiện mối đe dọa

Questions 14-19

Reading Passage 2 has nine paragraphs, A-I.

Which paragraph contains the following information?

Write the correct letter, A-I.

14. A description of how AI learns optimal responses through experimentation
15. An explanation of the resource limitations affecting some cybersecurity implementations
16. Details about algorithms that can function without pre-classified training examples
17. Information about techniques attackers use to deceive machine learning systems
18. An application of AI technology originally designed for visual recognition
19. A mention of the time and cost involved in preparing data for certain algorithms

Questions 20-23

Complete the summary below.

Choose NO MORE THAN TWO WORDS from the passage for each answer.

Deep learning uses neural networks with multiple layers to analyze data and identify threats. These networks can learn (20) __ of data automatically. One type, originally created for recognizing images, is now used to examine (21) __ for malware detection. Another type, skilled at handling (22) __, helps identify unusual patterns in login attempts and system records. Meanwhile, natural language processing algorithms can scan online conversations to discover new (23) __ being discussed by potential attackers.

Questions 24-26

Do the following statements agree with the claims of the writer in Passage 2?

Write:

• YES if the statement agrees with the claims of the writer
• NO if the statement contradicts the claims of the writer
• NOT GIVEN if it is impossible to say what the writer thinks about this

24. Unsupervised learning is more effective than supervised learning for all cybersecurity applications.
25. Natural language processing can identify phishing attempts by detecting unusual language patterns.
26. The computational demands of deep learning make it unsuitable for IoT devices without modifications.

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PASSAGE 3 – The Ethical and Strategic Implications of AI-Driven Cybersecurity

Độ khó: Hard (Band 7.0-9.0)

Thời gian đề xuất: 23-25 phút

The proliferation of artificial intelligence in cybersecurity has ushered in an era of unprecedented capability in threat detection and response, yet it simultaneously introduces a labyrinth of ethical dilemmas and strategic considerations that society has only begun to address. As AI systems assume increasingly autonomous roles in defending digital infrastructure, questions surrounding accountability, privacy, bias, and the potential for misuse have emerged as critical areas requiring multidisciplinary examination and regulatory frameworks that currently remain nascent or absent entirely.

The accountability paradox presents one of the most vexing challenges in AI-driven cybersecurity. Traditional security systems operate according to explicit rules programmed by human engineers, creating clear chains of responsibility when failures occur. AI systems, particularly those employing deep learning architectures, function as “black boxes” whose decision-making processes remain opaque even to their creators. When an AI system fails to detect a breach, generates excessive false positives that overwhelm security teams, or mistakenly identifies legitimate activities as threats – thereby disrupting business operations – determining culpability becomes problematic. Is the organization deploying the AI responsible? The vendor who developed it? The engineers who trained the model? Or the AI system itself? This ambiguity in attribution has significant legal and insurance implications, particularly as AI-powered security failures could result in massive financial losses or compromised personal data affecting millions of individuals.

Privacy concerns intensify as AI systems require ever-expanding access to data for effective operation. To accurately distinguish normal from anomalous behavior, AI must continuously monitor user activities, network traffic, communications, and system interactions. This pervasive surveillance, while necessary for security, creates a tension with fundamental privacy rights. Employee monitoring presents a particularly contentious arena: while employers have legitimate security interests in monitoring corporate networks, the granularity of insight AI systems can provide – tracking every keystroke, application use, website visit, and communication – raises questions about the erosion of workplace privacy and the potential for abuse. The situation becomes more complex in jurisdictions with stringent data protection regulations such as the European Union’s General Data Protection Regulation (GDPR), which mandates data minimization principles that may conflict with AI’s voracious appetite for training data.

The issue of algorithmic bias in cybersecurity AI systems has received less public attention than bias in other AI applications like facial recognition or hiring algorithms, yet it poses equally serious consequences. Machine learning models inherit biases present in their training data. If, for instance, a system is trained predominantly on attacks targeting Western corporations, it may prove less effective at detecting threats common in other regions or against different types of organizations. More insidiously, biases could lead to differential treatment of users based on factors like geography, language, or behavior patterns correlated with demographic characteristics. An AI system might flag legitimate activities by users from certain countries as suspicious while overlooking similar behaviors by others, effectively creating a form of algorithmic discrimination. The inscrutability of deep learning models makes detecting and correcting such biases particularly challenging, as the features the model uses to make decisions often cannot be explicitly identified.

The dual-use nature of AI cybersecurity technology presents profound ethical and geopolitical complications. Technologies developed for defensive purposes can typically be adapted for offensive operations with minimal modification. A machine learning system designed to detect vulnerabilities in software to help patch them can equally well be used to discover exploitable weaknesses for attacks. AI tools that analyze network traffic for threats can be repurposed for mass surveillance. Autonomous cyber weapons – AI systems capable of identifying targets, planning attacks, and executing them without human intervention – have moved from science fiction to near-term possibility. The strategic implications are staggering: cyber conflicts could escalate at machine speed, with AI systems on opposing sides engaging in attack and defense cycles that human operators cannot follow in real-time, let alone control. The potential for unintended escalation or miscalculation in such scenarios raises questions about maintaining human control in the decision-making loop – a principle known as “meaningful human control” in the autonomous weapons debate.

Export controls and technology transfer issues complicate international cooperation on cybersecurity AI. Nations developing advanced AI security capabilities face difficult choices: sharing technology with allies could strengthen collective defense but also risks proliferation to adversaries through espionage or technology leakage. Conversely, restricting technology transfer may leave partner nations vulnerable while potentially fragmenting the global cybersecurity ecosystem into incompatible systems that cannot effectively share threat intelligence. These concerns have already manifested in restrictions on Chinese telecommunications equipment in Western markets and debates over whether foundational AI research should be classified or openly published.

The emergence of AI-versus-AI dynamics in cybersecurity represents a fundamental shift in the threat landscape. As defenders deploy AI, sophisticated attackers are doing likewise, creating an asymmetric arms race with uncertain outcomes. Offensive AI can probe defenses systematically, learning from each attempt and automatically adapting tactics – essentially automating the expertise of elite human hackers and making it accessible to less skilled actors. Defensive AI must not only detect traditional attacks but also identify and counter AI-driven threats that may exhibit unprecedented characteristics. This co-evolution of offensive and defensive AI raises questions about stability: will defensive advantages outpace offensive capabilities, or vice versa? Historical precedent suggests that offense and defense alternate in advantage, but the pace and autonomy of AI systems could make transitions more abrupt and destabilizing.

Regulatory approaches to AI in cybersecurity remain fragmented and reactive. While some jurisdictions have begun implementing AI governance frameworks, most were designed with applications like autonomous vehicles or facial recognition in mind and inadequately address the unique characteristics of cybersecurity AI. The technical complexity of AI systems challenges lawmakers’ ability to craft appropriate regulations, while the rapid pace of technological advancement often renders specific technical requirements obsolete before they can be implemented. Some experts advocate for principle-based regulation focusing on outcomes and accountability rather than prescriptive technical standards, while others argue that the critical nature of cybersecurity requires more stringent oversight. Industry self-regulation has proven insufficient in other technology domains, yet government regulation risks stifling innovation in a field where technological superiority directly correlates with security effectiveness.

The long-term trajectory of AI in cybersecurity will likely be shaped not only by technological advances but also by the ethical frameworks, governance structures, and social norms that emerge to guide its development and deployment. Achieving the optimal balance between security effectiveness, individual rights, ethical operation, and strategic stability represents one of the defining challenges of the digital age. As AI systems become increasingly sophisticated and autonomous, the imperative for transparent, accountable, and ethically grounded approaches to AI-driven cybersecurity intensifies. Failure to address these challenges proactively may result in a future where the cure proves as problematic as the disease – where the systems designed to protect us instead become instruments of control, discrimination, or unintended harm.

Vấn đề đạo đức và chiến lược trong ứng dụng AI cho an ninh mạng

Questions 27-31

Choose the correct letter, A, B, C or D.

27. According to the passage, the “accountability paradox” in AI cybersecurity refers to:

• A. The difficulty of programming AI systems to follow security protocols
• B. The unclear responsibility when AI security systems fail or malfunction
• C. The conflict between AI efficiency and human oversight
• D. The high costs associated with implementing AI security measures

28. What does the author suggest about privacy concerns related to AI cybersecurity?

• A. They are easily resolved through existing data protection laws
• B. They primarily affect individual users rather than employees
• C. They create a fundamental conflict with security requirements
• D. They have been exaggerated by privacy advocates

29. The passage indicates that algorithmic bias in cybersecurity systems is:

• A. More serious than bias in other AI applications
• B. Deliberately introduced by system developers
• C. Difficult to identify due to the complexity of the models
• D. Mainly a problem in developing countries

30. What is the main concern regarding the “dual-use nature” of AI cybersecurity technology?

• A. It requires twice the budget to implement effectively
• B. Defensive technologies can be converted to offensive purposes
• C. Both public and private sectors need the same technology
• D. Training requires expertise in two different fields

31. The author’s attitude toward current regulatory approaches to AI in cybersecurity can best be described as:

• A. Optimistic about recent progress
• B. Critical of their inadequacy
• C. Neutral and objective
• D. Supportive of industry self-regulation

Questions 32-36

Complete each sentence with the correct ending, A-H, below.

Write the correct letter, A-H.

32. AI cybersecurity systems that operate as “black boxes”
33. The extensive data monitoring required by AI security systems
34. Export controls on AI cybersecurity technology
35. The development of autonomous cyber weapons
36. Principle-based regulation of AI cybersecurity

Endings:

A. may prevent effective international cooperation on security threats.
B. focuses on results rather than specific technical requirements.
C. have decision-making processes that even their creators cannot fully explain.
D. will replace human cybersecurity professionals within the next decade.
E. creates tensions between security needs and privacy rights.
F. raises serious questions about maintaining human control in conflicts.
G. has proven more effective than government oversight in other industries.
H. requires attackers to develop entirely new types of malware.

Questions 37-40

Do the following statements agree with the claims of the writer in Passage 3?

Write:

• YES if the statement agrees with the claims of the writer
• NO if the statement contradicts the claims of the writer
• NOT GIVEN if it is impossible to say what the writer thinks about this

37. Machine learning systems can only inherit biases if they are deliberately programmed by biased engineers.
38. The speed at which AI systems operate could lead to unintended escalation in cyber conflicts.
39. Most current AI governance frameworks adequately address the specific needs of cybersecurity applications.
40. The future effectiveness of AI in cybersecurity depends on both technological progress and ethical frameworks.

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3. Answer Keys – Đáp Án

PASSAGE 1: Questions 1-13

1. FALSE
2. TRUE
3. NOT GIVEN
4. TRUE
5. NOT GIVEN
6. TRUE
7. adaptive capability
8. countermeasures
9. writing style
10. human expertise
11. B
12. C
13. B

PASSAGE 2: Questions 14-26

14. F
15. H
16. C
17. G
18. D
19. B
20. hierarchical representations
21. binary code
22. sequential data
23. exploit techniques
24. NO
25. YES
26. YES

PASSAGE 3: Questions 27-40

27. B
28. C
29. C
30. B
31. B
32. C
33. E
34. A
35. F
36. B
37. NO
38. YES
39. NO
40. YES

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4. Giải Thích Đáp Án Chi Tiết

Passage 1 – Giải Thích

Câu 1: FALSE

• Dạng câu hỏi: True/False/Not Given
• Từ khóa: traditional security systems, adequate, modern cyber threats
• Vị trí trong bài: Đoạn 1, dòng 2-3
• Giải thích: Bài đọc nói rõ “Traditional security measures… are struggling to keep pace with the evolving tactics of cybercriminals” (Các biện pháp bảo mật truyền thống đang gặp khó khăn trong việc theo kịp chiến thuật phát triển của tội phạm mạng). Điều này trực tiếp mâu thuẫn với việc hệ thống truyền thống là “adequate” (đầy đủ/thích hợp).

Câu 2: TRUE

• Dạng câu hỏi: True/False/Not Given
• Từ khóa: AI, identify threats, don’t match, previously known patterns
• Vị trí trong bài: Đoạn 3, dòng 5-7
• Giải thích: Câu “AI systems… can identify suspicious behavior even if it doesn’t match any known threat pattern” khớp hoàn toàn với câu hỏi. Đây là paraphrase: “previously known patterns” = “known threat pattern”.

Câu 3: NOT GIVEN

• Dạng câu hỏi: True/False/Not Given
• Từ khóa: zero-day attacks, most common type
• Vị trí trong bài: Đoạn 3
• Giải thích: Mặc dù bài có đề cập đến zero-day attacks, nhưng không có thông tin nào so sánh hoặc khẳng định chúng là loại tấn công phổ biến nhất. Bài chỉ nói về phishing là “one of the most common cyberthreats”.

Câu 4: TRUE

• Dạng câu hỏi: True/False/Not Given
• Từ khóa: AI systems, monitor network traffic, more effectively, human analysts, large organizations
• Vị trí trong bài: Đoạn 4, dòng 1-3
• Giải thích: Bài viết: “Large organizations process millions of network transactions every day, making it virtually impossible for human analysts to monitor everything. AI can continuously scan network traffic…” Điều này xác nhận AI giám sát hiệu quả hơn con người trong tổ chức lớn.

Câu 6: TRUE

• Dạng câu hỏi: True/False/Not Given
• Từ khóa: cybercriminals, using AI, more advanced attacks
• Vị trí trong bài: Đoạn cuối, câu cuối
• Giải thích: “Additionally, cybercriminals are beginning to use AI themselves to create more sophisticated attacks” – khớp chính xác với câu hỏi.

Câu 7: adaptive capability

• Dạng câu hỏi: Sentence Completion
• Từ khóa: AI, adjust to new threats, without preprogrammed rules
• Vị trí trong bài: Đoạn 2, câu cuối
• Giải thích: “This adaptive capability is crucial in an environment where new types of attacks emerge daily” – cụm từ “adaptive capability” là đáp án phù hợp.

Câu 8: countermeasures

• Dạng câu hỏi: Sentence Completion
• Từ khóa: AI, unusual activity, automatically implement
• Vị trí trong bài: Đoạn 6, dòng 3-4
• Giải thích: “AI systems can automatically implement countermeasures such as isolating affected systems…” – từ “countermeasures” là đáp án.

Câu 11: B

• Dạng câu hỏi: Multiple Choice
• Giải thích: Đoạn 3 giải thích rằng traditional antivirus dựa vào database của malware đã biết, trong khi AI “can identify suspicious behavior even if it doesn’t match any known threat pattern” – tương ứng với đáp án B.

Câu 13: B

• Dạng câu hỏi: Multiple Choice
• Giải thích: Toàn bộ passage giải thích cách AI được ứng dụng trong nhiều lĩnh vực an ninh mạng khác nhau (threat detection, network security, email security), làm cho đáp án B là phù hợp nhất.

Passage 2 – Giải Thích

Câu 14: F

• Dạng câu hỏi: Matching Information
• Từ khóa: learns optimal responses through experimentation
• Vị trí trong bài: Paragraph F
• Giải thích: Đoạn F nói về reinforcement learning “learn through trial and error” và “learning optimal response strategies” – khớp với “learns optimal responses through experimentation”.

Câu 16: C

• Dạng câu hỏi: Matching Information
• Từ khóa: function without pre-classified training examples
• Vị trí trong bài: Paragraph C
• Giải thích: Đoạn C nói về unsupervised learning: “operate without pre-labeled data” = “without pre-classified training examples”.

Câu 17: G

• Dạng câu hỏi: Matching Information
• Từ khóa: techniques attackers use to deceive machine learning
• Vị trí trong bài: Paragraph G
• Giải thích: Đoạn G thảo luận về “adversarial machine learning” và cách attackers “craft inputs designed to fool ML algorithms”.

Câu 20: hierarchical representations

• Dạng câu hỏi: Summary Completion
• Vị trí trong bài: Đoạn D, dòng 2-3
• Giải thích: “Neural networks… can automatically learn hierarchical representations of data” – cụm từ chính xác trong bài.

Câu 21: binary code

• Dạng câu hỏi: Summary Completion
• Vị trí trong bài: Đoạn D, dòng 6-7
• Giải thích: “Convolutional neural networks… are now being adapted to analyze binary code and identify malware”.

Câu 24: NO

• Dạng câu hỏi: Yes/No/Not Given
• Giải thích: Bài không khẳng định unsupervised learning tốt hơn cho TẤT CẢ ứng dụng. Bài chỉ nói nó “particularly valuable for detecting novel threats” trong một số trường hợp cụ thể.

Câu 25: YES

• Dạng câu hỏi: Yes/No/Not Given
• Vị trí trong bài: Đoạn E
• Giải thích: “NLP powers sophisticated phishing detection systems that analyze… subtle linguistic indicators of deception, such as unusual phrasing” – khẳng định NLP có thể phát hiện phishing qua ngôn ngữ bất thường.

Các dạng câu hỏi phổ biến trong IELTS Reading Test về công nghệ và AI

Passage 3 – Giải Thích

Câu 27: B

• Dạng câu hỏi: Multiple Choice
• Vị trí trong bài: Đoạn 2
• Giải thích: Đoạn 2 giải thích accountability paradox: “When an AI system fails… determining culpability becomes problematic. Is the organization… responsible? The vendor…? The engineers…?” Điều này khớp với đáp án B về unclear responsibility.

Câu 28: C

• Dạng câu hỏi: Multiple Choice
• Vị trí trong bài: Đoạn 3
• Giải thích: Đoạn 3: “This pervasive surveillance, while necessary for security, creates a tension with fundamental privacy rights” – tạo ra xung đột cơ bản giữa bảo mật và quyền riêng tư, tương ứng đáp án C.

Câu 29: C

• Dạng câu hỏi: Multiple Choice
• Vị trí trong bài: Đoạn 4
• Giải thích: “The inscrutability of deep learning models makes detecting and correcting such biases particularly challenging” – khó xác định do độ phức tạp của models, khớp với đáp án C.

Câu 30: B

• Dạng câu hỏi: Multiple Choice
• Vị trí trong bài: Đoạn 5
• Giải thích: “Technologies developed for defensive purposes can typically be adapted for offensive operations with minimal modification” – công nghệ phòng thủ có thể chuyển đổi thành tấn công, đáp án B.

Câu 32: C

• Dạng câu hỏi: Matching Sentence Endings
• Giải thích: “Black boxes” được định nghĩa trong đoạn 2: “whose decision-making processes remain opaque even to their creators” – khớp với ending C.

Câu 33: E

• Dạng câu hỏi: Matching Sentence Endings
• Giải thích: Đoạn 3 nói extensive monitoring “creates a tension with fundamental privacy rights” – khớp với ending E.

Câu 35: F

• Dạng câu hỏi: Matching Sentence Endings
• Giải thích: Đoạn 5 về autonomous cyber weapons: “raises questions about maintaining human control” – khớp với ending F.

Câu 37: NO

• Dạng câu hỏi: Yes/No/Not Given
• Giải thích: Đoạn 4 nói biases được “inherit” từ training data, không phải deliberately programmed. Điều này mâu thuẫn với câu hỏi.

Câu 38: YES

• Dạng câu hỏi: Yes/No/Not Given
• Vị trí trong bài: Đoạn 5
• Giải thích: “cyber conflicts could escalate at machine speed… The potential for unintended escalation or miscalculation” – tác giả đồng ý với tuyên bố này.

Câu 39: NO

• Dạng câu hỏi: Yes/No/Not Given
• Vị trí trong bài: Đoạn 8
• Giải thích: “most were designed with applications like autonomous vehicles… and inadequately address the unique characteristics of cybersecurity AI” – mâu thuẫn với “adequately address”.

Câu 40: YES

• Dạng câu hỏi: Yes/No/Not Given
• Vị trí trong bài: Đoạn cuối
• Giải thích: “The long-term trajectory… will likely be shaped not only by technological advances but also by the ethical frameworks” – tác giả đồng ý cả hai yếu tố đều quan trọng.

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5. Từ Vựng Quan Trọng Theo Passage

Passage 1 – Essential Vocabulary

Từ vựng	Loại từ	Phiên âm	Nghĩa tiếng Việt	Ví dụ từ bài	Collocation
sophisticated	adj	/səˈfɪstɪkeɪtɪd/	phức tạp, tinh vi	cyberattacks have become increasingly sophisticated	sophisticated attack/technology/system
cybersecurity	n	/ˌsaɪbəsɪˈkjʊərəti/	an ninh mạng	AI is revolutionizing cybersecurity	cybersecurity measures/threats/professional
anomaly	n	/əˈnɒməli/	sự bất thường	identify anomalies that might indicate a breach	detect/identify an anomaly
malware	n	/ˈmælweə(r)/	phần mềm độc hại	comparing files against a database of known malware	malware signature/detection
zero-day attack	n	/ˈzɪərəʊ deɪ əˈtæk/	tấn công lỗ hổng chưa biết	becomes ineffective against zero-day attacks	zero-day vulnerability/exploit
phishing	n	/ˈfɪʃɪŋ/	lừa đảo qua email	Phishing attacks remain one of the most common cyberthreats	phishing attack/email/attempt
countermeasure	n	/ˈkaʊntəˌmeʒə(r)/	biện pháp đối phó	automatically implement countermeasures	implement/deploy countermeasures
data breach	n	/ˈdeɪtə briːtʃ/	vi phạm dữ liệu	prevent a minor security incident from escalating into a major data breach	major/serious data breach
augment	v	/ɔːɡˈment/	tăng cường, bổ sung	AI augments human capabilities	augment capacity/ability
false positive	n	/fɔːls ˈpɒzətɪv/	kết quả dương tính giả	can sometimes generate false positives	generate/reduce false positives
adaptive	adj	/əˈdæptɪv/	có khả năng thích ứng	This adaptive capability is crucial	adaptive capability/system/learning
unprecedented	adj	/ʌnˈpresɪdentɪd/	chưa từng có	process data at unprecedented speeds	unprecedented speed/scale/level

Passage 2 – Essential Vocabulary

Từ vựng	Loại từ	Phiên âm	Nghĩa tiếng Việt	Ví dụ từ bài	Collocation
paradigm shift	n	/ˈpærədaɪm ʃɪft/	sự thay đổi mô hình	represents a paradigm shift in how threats are identified	paradigm shift in thinking/approach
supervised learning	n	/ˈsuːpəvaɪzd ˈlɜːnɪŋ/	học có giám sát	Supervised learning algorithms form the foundation	supervised learning model/algorithm
unsupervised learning	n	/ˌʌnˈsuːpəvaɪzd ˈlɜːnɪŋ/	học không giám sát	unsupervised learning algorithms operate without pre-labeled data	unsupervised learning technique/method
anomaly detection	n	/əˈnɒməli dɪˈtekʃn/	phát hiện bất thường	unsupervised learning excels at anomaly detection	anomaly detection system/technique
neural network	n	/ˈnjʊərəl ˈnetwɜːk/	mạng thần kinh	Neural networks with multiple layers	neural network architecture/model
hierarchical	adj	/ˌhaɪəˈrɑːkɪkl/	có tính phân cấp	learn hierarchical representations of data	hierarchical structure/system/model
convolutional	adj	/ˌkɒnvəˈluːʃənl/	tích chập	Convolutional neural networks (CNNs)	convolutional layer/network
sequential data	n	/sɪˈkwenʃl ˈdeɪtə/	dữ liệu tuần tự	excel at processing sequential data	sequential data analysis/processing
exploit	n/v	/ɪkˈsplɔɪt/	khai thác, lỗ hổng	new exploit techniques	exploit vulnerability/weakness
reinforcement learning	n	/ˌriːɪnˈfɔːsmənt ˈlɜːnɪŋ/	học tăng cường	Reinforcement learning represents a fascinating application	reinforcement learning algorithm/agent
adversarial	adj	/ˌædvəˈseəriəl/	đối kháng	adversarial machine learning has emerged	adversarial attack/example/training
robust	adj	/rəʊˈbʌst/	mạnh mẽ, vững chắc	develop more robust algorithms	robust system/model/defense
computational overhead	n	/ˌkɒmpjuˈteɪʃənl ˈəʊvəhed/	chi phí tính toán	The computational overhead associated with ML	computational overhead/cost/burden
edge computing	n	/edʒ kəmˈpjuːtɪŋ/	điện toán biên	edge computing solutions	edge computing device/platform
federated learning	n	/ˈfedəreɪtɪd ˈlɜːnɪŋ/	học liên kết	Federated learning offers a promising approach	federated learning framework/system

Passage 3 – Essential Vocabulary

Từ vựng	Loại từ	Phiên âm	Nghĩa tiếng Việt	Ví dụ từ bài	Collocation
proliferation	n	/prəˌlɪfəˈreɪʃn/	sự lan rộng	The proliferation of artificial intelligence	proliferation of weapons/technology
labyrinth	n	/ˈlæbərɪnθ/	mê cung	introduces a labyrinth of ethical dilemmas	labyrinth of regulations/rules
accountability	n	/əˌkaʊntəˈbɪləti/	trách nhiệm giải trình	The accountability paradox	accountability framework/mechanism
culpability	n	/ˌkʌlpəˈbɪləti/	tội lỗi, khả năng chịu trách	determining culpability becomes problematic	establish/determine culpability
opaque	adj	/əʊˈpeɪk/	mờ đục, khó hiểu	decision-making processes remain opaque	opaque system/process/structure
pervasive	adj	/pəˈveɪsɪv/	lan rộng, phổ biến	This pervasive surveillance	pervasive influence/technology/monitoring
contention	n	/kənˈtenʃn/	tranh cãi	presents a particularly contentious arena	point of contention/contentious issue
stringent	adj	/ˈstrɪndʒənt/	nghiêm ngặt	jurisdictions with stringent data protection regulations	stringent regulations/requirements/measures
algorithmic bias	n	/ˌælɡəˈrɪðmɪk ˈbaɪəs/	thiên kiến thuật toán	The issue of algorithmic bias	algorithmic bias/discrimination/fairness
inscrutability	n	/ɪnˌskruːtəˈbɪləti/	tính không thể hiểu được	The inscrutability of deep learning models	inscrutability of AI systems
dual-use	adj	/ˈdjuːəl juːs/	hai mục đích	The dual-use nature of AI technology	dual-use technology/application
proliferation	n	/prəˌlɪfəˈreɪʃn/	sự phát tán	sharing technology… risks proliferation	nuclear/weapons proliferation
asymmetric	adj	/ˌeɪsɪˈmetrɪk/	bất đối xứng	creating an asymmetric arms race	asymmetric warfare/conflict/threat
fragmented	adj	/ˈfræɡmentɪd/	phân mảnh	Regulatory approaches remain fragmented	fragmented approach/system/market
stifle	v	/ˈstaɪfl/	kìm hãm, dập tắt	government regulation risks stifling innovation	stifle innovation/growth/competition
stringent oversight	n	/ˈstrɪndʒənt ˈəʊvəsaɪt/	giám sát nghiêm ngặt	the critical nature requires more stringent oversight	stringent oversight/regulation/control
escalation	n	/ˌeskəˈleɪʃn/	sự leo thang	potential for unintended escalation	escalation of conflict/violence
transparent	adj	/trænsˈpærənt/	minh bạch	the imperative for transparent approaches	transparent process/system/operation

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Kết Bài

Chủ đề “How is AI being used in cybersecurity?” không chỉ là một xu hướng công nghệ quan trọng của thời đại mà còn là đề tài phổ biến trong các kỳ thi IELTS Reading hiện nay. Qua đề thi mẫu này, bạn đã được trải nghiệm đầy đủ ba passages với độ khó tăng dần, từ bài giới thiệu cơ bản về AI trong an ninh mạng, đến phân tích chuyên sâu về các thuật toán học máy, và cuối cùng là thảo luận học thuật về những vấn đề đạo đức phức tạp.

40 câu hỏi đa dạng trong đề thi này bao quát hầu hết các dạng bài phổ biến nhất trong IELTS Reading: True/False/Not Given, Yes/No/Not Given, Multiple Choice, Matching Information, Matching Headings, Matching Sentence Endings, Sentence Completion, và Summary Completion. Việc luyện tập với đề thi này giúp bạn làm quen với cách thức paraphrase, xác định từ khóa, và định vị thông tin chính xác trong đoạn văn – những kỹ năng cốt lõi để đạt band điểm cao.

Phần đáp án chi tiết không chỉ cung cấp kết quả đúng mà còn giải thích rõ ràng logic đằng sau mỗi câu trả lời, giúp bạn hiểu được tư duy làm bài đúng đắn. Hơn 40 từ vựng chuyên ngành về AI và cybersecurity được tổng hợp kèm phiên âm, nghĩa và collocations sẽ là tài sản quý giá cho kho từ vựng học thuật của bạn.

Hãy dành thời gian xem lại những câu trả lời sai, phân tích tại sao bạn bị nhầm lẫn, và rút ra bài học cho bản thân. Luyện tập thường xuyên với các đề thi chất lượng cao như thế này sẽ giúp bạn tự tin bước vào phòng thi IELTS và đạt được band điểm mong muốn. Chúc bạn thành công trong hành trình chinh phục IELTS Reading!