#FactCheck - Assam Video Falsely Shared as BJP Government Deporting Bangladeshis From West Bengal

Introduction

Language is an important part of human communication and a basic aspect of human understanding. The world is a global market and this diversity of languages has led to difficulties in engaging for effective communication and collaboration.  India alone has 22 official languages and countless regional languages and dialects which change every few hundred kilometres. 

AI has emerged to overcome this challenge of language barriers and has stepped into bringing about a transformative shift. It is leading the charge in breaking down traditional barriers and paving the way for more inclusive and seamless global interactions. AI’s integration into language translation has revolutionised the field, addressing longstanding challenges associated with traditional human-centric approaches. The limitations posed by reliance on human translators, such as time constraints, resource limitations, and the inability to handle the data efficiently, paved the way for the furtherance of the transformative impact of AI. However, challenges such as maintaining translation accuracy, addressing cultural nuances, and ensuring data privacy require careful attention to realize AI's full potential.

AI Technologies Bridging Language Gaps

AI tools have transformed translation, transcription, and natural language processing, providing language solutions. They can instantly translate text, transcribe audio, and analyse linguistic nuances, enabling effective cross-cultural communication. Moreover, AI's adaptive capabilities have facilitated language learning, allowing individuals to grasp new languages and adapt their communication styles to diverse cultural contexts.  

AI technologies are making information and services more accessible to non-native speakers and are impacting global business, allowing effective engagement. Building on this transformative potential, various AI tools are now used to bridge language gaps in real-world applications. Some examples of AI’s role in bridging the language gap are:

• Real-time translation tools that enable instant communication by providing translations between languages on the fly. This would help in effortless conversations with clients and partners worldwide. 
• Tools such as ‘speech-to-text’ and ‘text-to-speech’ like Murf AI, Lovo AI, and ElevenLabs work towards converting spoken language into written text and vice versa. These technologies have led to streamlined interactions, boosted productivity, and clarity in global business dealings. Businesses can extract important information, insights, and action points from meetings, interviews, and presentations. 
• AI chatbots like MyGov Corona Helpdesk, WhatsApp Chatbot by the Government of India, Railway Food Order & Delivery by Zoop India, and Gen AI-Powered 'Elena' by Indian School of Business (ISB) are some examples that act as intelligent virtual assistants that engage in real-time conversations, by answering queries, providing information, and facilitating transactions. They offer round-the-clock support, freeing human resources and enhancing customer experience across language barriers.

Challenges and Limitations of AI Translation

While AI’s integration in combatting language barriers is commendable, there are challenges and limitations in overcoming this endeavour. These challenges and limitations are:

• AI translation systems face several challenges in handling accuracy, context, nuance, and idiomatic expressions. 
• These systems may encounter struggles with complex or specialised language, along with those towards regional dialects, leading to potential misinterpretations. 
• Biases within the AI models can further affect the inclusivity of translations, often favouring dominant languages and cultural norms while marginalising others. 
• Ethical concerns, regarding privacy and data security, particularly when sensitive information is processed have also been arising. 
• Ensuring user consent and protecting data integrity are essential to addressing these concerns. As AI continues to evolve, ongoing efforts are needed to improve fairness, transparency, and the cultural sensitivity of translation systems.

AI’s Future  in Language Translation

AI technologies are moving towards improving translation accuracy and contextual understanding, allowing AI models to grasp cultural nuances and idiomatic expressions better. This can significantly enhance communication across diverse languages, fostering multilingual interactions and global collaboration in business, education, and diplomacy. Improvements in AI tech are taking place ubiquitous, and models like GPT and Google Translate are now better at capturing nuances, idioms, and cultural differences, reducing errors. AI tools like the Microsoft Translator help cross-continental teams work seamlessly by enhancing their productivity and inclusivity. 

AI is capable of offering real-time translation in healthcare, education, and public services. This would enable more inclusive environments and bridging communication gaps. For example in the healthcare system, AI-powered translation tools are helping the industry to provide better care by crossing linguistic barriers. Doctors can now communicate with patients who speak different languages, ensuring equitable care even with linguistic boundaries. 

Conclusion

We live in a world where diverse languages pose significant challenges to global communication, and AI has emerged as a powerful tool to bridge these gaps. AI is paving the way for more inclusive and seamless interactions by revolutionising language translation, transcription, and natural language processing. Its ability to break down barriers caused by linguistic diversity ensures effective communication in fields ranging from business to healthcare. Despite challenges like accuracy and cultural sensitivity, the potential for AI to continuously improve is undeniable. As AI technologies evolve, they stand as the key to overcoming language barriers and fostering a more connected and inclusive global community.

Notwithstanding AI's potential abilities to overcome language barriers through advances in natural language processing and translation, cybersecurity and data privacy must always come first. The same technologies that make it easier to communicate globally also put private information at risk. The likelihood of data breaches, personal information misuse, and compromised communication rises in the absence of strict cybersecurity safeguards. Thus, in order to guarantee safe and reliable international Interactions as AI develops, it is crucial to strike a balance between innovation and privacy protection.

References

• https://megasisnetwork.medium.com/ai-and-language-translation-breaking-down-language-barriers-47873cfdb13b
• https://pubmed.ncbi.nlm.nih.gov/38099504/ 
• https://www.linkedin.com/pulse/breaking-language-barriers-ai-era-leveraging-tools-business-a-rad
• https://www.researchgate.net/publication/373842132_Breaking_Down_Barriers_With_Artificial_Intelligence_AI_Cross-Cultural_Communication_in_Foreign_Language_Education

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Introduction

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The courts in India have repeatedly emphasised the importance of “enhanced customer protection” and “limited liability” on their part. The rationale behind such imperatives is to extend security against exploitation by institutions that are equipped with all the means to manipulate customers. India, with its looming financial literacy gaps that have to be addressed, needs to curb any manipulation on the part of banking institutions. Various studies have highlighted this gap in recent times; for example, according to the National Centre for Financial Education, only 27% of Indian people are financially literate, which is much less than the 42% global average. With only 19% of millennials exhibiting sufficient financial awareness yet expressing high trust in their financial skills, the issue is very worrisome. Thus, the increasing number of financial frauds intensifies the issue. 

Zero Liability in Cyber Frauds: Regulatory Safeguards for Digital Banking Customers

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In light of the growing emphasis on financial inclusion and consumer protection, and in response to the recent rise in complaints regarding unauthorised debits from customer accounts and cards, the framework for assessing customer liability in such cases has been re-evaluated. The RBI’s circular dated July 6, 2017 titled “Customer Protection-Limited Liability of Customers in Unauthorised Electronic Banking Transactions”  serves as the foundation for regulatory protections for Indian customers of digital banking. A clear and organised framework for determining customer accountability is outlined in the circular, which acknowledges the exponential increase in electronic transactions and related scams. It assigns proportional obligations for unauthorised transactions resulting from system-level breaches, client carelessness, and bank contributory negligence. Most importantly it establishes the zero responsibility concept, which protects clients from monetary losses in cases when the bank or another system component is at fault and the client promptly reports the breach. 

This directive’s sophisticated approach to consumer protection is what makes it unique. It requires banks to set up strong fraud prevention systems, proactive alerting systems, and round-the-clock reporting systems. Furthermore, it significantly alters the power dynamics between financial institutions and customers by placing the onus of demonstrating customer negligence completely on the bank. The circular emphasises prompt reversal of funds to impacted customers and requires banks to implement Board-approved policies on liability to redress. As a result, it is a consumer rights charter rather than just a compliance document, promoting confidence and financial accountability in India’s digital banking sector. 

Judicial Endorsement in Reinforcing the Zero Liability Principle

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 In the case of Suresh Chandra Negi & Anr. v. Bank of Baroda & Ors. (Writ (C) No. 24192 of 2022) The Allahabad High Court reaffirmed that the burden of proving consumer accountability rests firmly on the banking institution, hence reaffirming the zero liability concept in circumstances of unapproved electronic banking transactions. The Division bench emphasised the regulatory requirement that banks provide adequate proof before assigning blame to customers, citing Clause 12 of the RBI’s circular dated June 6, 2017, Customer Protection—Limited Liability of Customers in Unauthorised Electronic Banking Transactions. In a similar scenario, the Bombay HC held that a customer is entitled to zero liability when an authorized transaction occurs due to a third-party breach, where the deficiency lies neither with the bank nor the customer, provided the fraud is promptly reported. 

The zero liability principle, as envisaged under Clause 8 of the RBI circular, has emerged as a cornerstone of consumer protection in India’s digital banking ecosystem.  

Another landmark judgment that has given this principle the front stage in addressing banking frauds is Hare Ram Singh vs RBI &Ors. (W.P. (C) 13497/2022) laid down by Delhi HC which is an important legal turning point in the development of the zero liability principle under the RBI’s 2017 framework. The court reiterated the need to evaluate customer diligence in light of new fraud tactics like phishing and vishing by holding the State Bank of India (SBI) liable for a cyber fraud incident even though the transactions were authenticated by OTP. The ruling made it clear that when complex social engineering or technical manipulation is used, banks are nonetheless accountable even if they only rely on OTP validation. The legal protection provided to victims of unauthorised electronic banking transactions is strengthened by the court’s emphasis on the bank having the burden of evidence in accordance with RBI standards. 

Importantly, this ruling lays the full burden of securing digital banking systems on financial organisations and supports the judiciary’s increasing acknowledgement of the digital asymmetry between banks and consumers. It emphasises that prompt consumer reporting, banks’ failure to disclose important credentials, and their own operational errors must all be taken into consideration when determining culpability. As a result, this decision establishes a strong precedent that will increase consumer confidence, promote systemic advancements in digital risk management, and better integrate the zero liability standard into Indian digital banking law. In a time when cyber vulnerabilities are growing, it acts as a beacon for financial accountability. 

Conclusion

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The Zero Liability Principle serves as a vital safety net for customers navigating an increasingly intricate and precarious financial environment in a time when digital transactions are the foundation of contemporary banking. In addition to codifying strong safeguards against unauthorized electronic transactions, the RBI’s 2017 framework rebalanced  the fiduciary relationship by putting financial institutions squarely in charge. Through significant rulings, the courts have upheld this protective culture and emphasised that banks, not the victims of cybercrime, bear the burden of proof. 

It would be crucial to execute these principles consistently, review them frequently, and raise public awareness as India transitions to a more digital economy. In order to ensure that consumers are not only protected but also empowered must become more than just a policy on paper. 

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References

• https://www.business-standard.com/content/specials/making-money-vs-managing-money-india-s-critical-financial-literacy-gap-125021900786_1.html 
• https://www.livelaw.in/high-court/allahabad-high-court/allahabad-high-court-ruling-bank-liability-unauthorized-electronic-transaction-and-customer-fault-297962 
• https://www.mondaq.com/india/white-collar-crime-anti-corruption-fraud/1635616/cyber-law-series-2-issue-10-the-zero-liability-principle-in-cyber-fraud-hare-ram-singh-v-reserve-bank-of-india-ors-case 

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Executive Summary:

One of the most complex threats that have appeared in the space of network security is focused on the packet rate attacks that tend to challenge traditional approaches to DDoS threats’ involvement. In this year, the British based biggest Internet cloud provider of Europe, OVHcloud was attacked by a record and unprecedented DDoS attack reaching the rate of 840 million packets per second. Targets over 1 Tbps have been observed more regularly starting from 2023, and becoming nearly a daily occurrence in 2024. The maximum attack on May 25, 2024, got to 2.5 Tbps, this points to a direction to even larger and more complex attacks of up to 5 Tbps. Many of these attacks target critical equipment such as Mikrotik models within the core network environment; detection and subsequent containment of these threats prove a test for cloud security measures.

Modus Operandi of a Packet Rate Attack:

A type of cyberattack where an attacker sends with a large volume of packets in a short period of time aimed at a network device is known as packet rate attack, or packet flood attack or network flood attack under volumetric DDoS attack. As opposed to the deliberately narrow bandwidth attacks, these raids target the computation time linked with package processing. 

Key technical characteristics include: 

• Packet Size: Usually compact, and in many cases is less than 100 bytes 
• Protocol: Named UDP, although it can also involve TCP SYN or other protocol flood attacks 
•  Rate: Exceeding 100 million packets per second (Mpps), with recent attacks exceeding 840 Mpps 
• Source IP Diversity: Usually originating from a small number of sources and with a large number of requests per IP, which testifies about the usage of amplification principles 
• Attack on the Network Stack : To understand the impact, let's examine how these attacks affect different layers of the network stack:

1. Layer 3 (Network Layer):

• Each packet requires routing table lookups and hence routers and L3 switches have the problem of high CPU usage. 
• These mechanisms can often be saturated so that network communication will be negatively impacted by the attacker.

2. Layer 4 (Transport Layer):

• Other stateful devices (e.g. firewalls, load balancers) have problems with tables of connections 
• TCP SYN floods can also utilize all connection slots so that no incoming genuine connection can be made.

3. Layer 7 (Application Layer): 

• Web servers and application firewalls may be triggered to deliver a better response in a large number of requests 
• Session management systems can become saturated, and hence, the performance of future iterations will be a little lower than expected in terms of their perceived quality by the end-user. 

Technical Analysis of Attack Vectors

Recent studies have identified several key vectors exploited in high-volume packet rate attacks:

1.MikroTik RouterOS Exploitation:

• Vulnerability: CVE-2023-4967
• Impact: Allows remote attackers to generate massive packet floods
• Technical detail: Exploits a flaw in the FastTrack implementation

2.DNS Amplification:

• Amplification factor: Up to 54x
• Technique: Exploits open DNS resolvers to generate large responses to small queries
• Challenge: Difficult to distinguish from legitimate DNS traffic

3.NTP Reflection:

• Command: monlist
• Amplification factor: Up to 556.9x
• Mitigation: Requires NTP server updates and network-level filtering

Mitigation Strategies: A Technical Perspective

1. Combating packet rate attacks requires a multi-layered approach:

• Hardware-based Mitigation:
• Implementation: FPGA-based packet processing
• Advantage: Can handle millions of packets per second with minimal latency
• Challenge: High cost and specialized programming requirements

2.Anycast Network Distribution:

• Technique: Distributing traffic across multiple global nodes
• Benefit: Dilutes attack traffic, preventing single-point failures
• Consideration: Requires careful BGP routing configuration

3.Stateless Packet Filtering:

• Method: Applying filtering rules without maintaining connection state
• Advantage: Lower computational overhead compared to stateful inspection
• Trade-off: Less granular control over traffic

4.Machine Learning-based Detection:

• Approach: Using ML models to identify attack patterns in real-time
• Key metrics: Packet size distribution, inter-arrival times, protocol anomalies
• Challenge: Requires continuous model training to adapt to new attack patterns

Performance Metrics and Benchmarking

When evaluating DDoS mitigation solutions for packet rate attacks, consider these key performance indicators:

• Flows per second (fps) or packet per second (pps) capability
• Dispersion and the latency that comes with it is inherent to mitigation systems.
• The false positive rate in the case of the attack detection
• Exposure time before beginning of mitigation from the moment of attack

Way Forward

The packet rate attacks are constantly evolving where the credible defenses have not stayed the same. The next step entails extension to edge computing and 5G networks for distributing mitigation closer to the attack origins. Further, AI-based proactive tools of analysis for prediction of such threats will help to strengthen the protection of critical infrastructure against them in advance.

In order to stay one step ahead in this, it is necessary to constantly conduct research, advance new technologies, and work together with other cybersecurity professionals. There is always a need to develop secure defenses that safeguard these networks.

Reference:

https://blog.ovhcloud.com/the-rise-of-packet-rate-attacks-when-core-routers-turn-evil/

https://cybersecuritynews.com/record-breaking-ddos-attack-840-mpps/

https://www.cloudflare.com/learning/ddos/famous-ddos-attacks/

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