#FactCheck! Viral Image Claiming Virat Kohli and Rohit Sharma Visited Kedarnath Is AI-Generated

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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Executive Summary:

Given that AI technologies are evolving at a fast pace in 2024, an AI-oriented phishing attack on a large Indian financial institution illustrated the threats. The documentation of the attack specifics involves the identification of attack techniques, ramifications to the institution, intervention conducted, and resultant effects. The case study also turns to the challenges connected with the development of better protection and sensibilisation of automatized threats. 

Introduction

Due to the advancement in AI technology, its uses in cybercrimes across the world have emerged significant in financial institutions. In this report a serious incident that happened in early 2024 is analysed, according to which a leading Indian bank was hit by a highly complex, highly intelligent AI-supported phishing operation. Attack made use of AI’s innate characteristic of data analysis and data persuasion which led into a severe compromise of the bank’s internal structures.

Background

The chosen financial institution, one of the largest banks in India, had a good background regarding the extremity of its cybersecurity policies. However, these global cyberattacks opened up new threats that AI-based methods posed that earlier forms of security could not entirely counter efficiently. The attackers concentrated on the top managers of the bank because it is evident that controlling such persons gives the option of entering the inner systems as well as financial information. 

Attack Execution

The attackers utilised AI in sending the messages that were an exact look alike of internal messages sent between employees. From Facebook and Twitter content, blog entries, and lastly, LinkedIn connection history and email tenor of the bank’s executives, the AI used to create these emails was highly specific. Some of these emails possessed official formatting, specific internal language, and the CEO’s writing; this made them very realistic. 

 It also used that link in phishing emails that led the users to a pseudo internal portal in an attempt to obtain the login credentials. Due to sophistication, the targeted individuals thought the received emails were genuine, and entered their log in details easily to the bank’s network, thus allowing the attackers access. 

Impact

It caused quite an impact to the bank in every aspect. Numerous executives of the company lost their passwords to the fake emails and compromised several financial databases with information from customer accounts and transactions. The break-in permitted the criminals to cease a number of the financial’s internet services hence disrupting its functions and those of its customers for a number of days. 

 They also suffered a devastating blow to their customer trust because the breach revealed the bank’s weakness against contemporary cyber threats. Apart from managing the immediate operations which dealt with mitigating the breach, the financial institution was also toppling a long-term reputational hit. 

Technical Analysis and Findings

1. The AI techniques that are used in generation of the phishing emails are as follows: 

•  The attack used powerful NLP technology, which was most probably developed using the large-scaled transformer, such as GPT (Generative Pre-trained Transformer). Since these models are learned from large data samples they used the examples of the conversation pieces from social networks, emails and PC language to create quite credible emails. 

 Key Technical Features: 

•  Contextual Understanding: The AI was able to take into account the nature of prior interactions and thus write follow up emails that were perfectly in line with prior discourse. 
•  Style Mimicry: The AI replicated the writing of the CEO given the emails of the CEO and then extrapolated from the data given such elements as the tone, the language, and the format of the signature line. 
•  Adaptive Learning: The AI actively adapted from the mistakes, and feedback to tweak the generated emails for other tries and this made it difficult to detect. 

2. Sophisticated Spear-Phishing Techniques

Unlike ordinary phishing scams, this attack was phishing using spear-phishing where the attackers would directly target specific people using emails. The AI used social engineering techniques that significantly increased the chances of certain individuals replying to certain emails based on algorithms which machine learning furnished. 

 Key Technical Features: 

•  Targeted Data Harvesting: Cyborgs found out the employees of the organisation and targeted messages via the public profiles and messengers were scraped. 
•  Behavioural Analysis: The latest behaviour pattern concerning the users of the social networking sites and other online platforms were used by the AI to forecast the courses of action expected to be taken by the end users such as clicking on the links or opening of the attachments. 
•  Real-Time Adjustments: These are times when it was determined that the response to the phishing email was necessary and the use of AI adjusted the consequent emails’ timing and content. 

3. Advanced Evasion Techniques

The attackers were able to pull off this attack by leveraging AI in their evasion from the normal filters placed in emails. These techniques therefore entailed a modification of the contents of the emails in a manner that would not be easily detected by the spam filters while at the same time preserving the content of the message. 

Key Technical Features: 

•  Dynamic Content Alteration: The AI merely changed the different aspects of the email message slightly to develop several versions of the phishing email that would compromise different algorithms. 
•  Polymorphic Attacks: In this case, polymorphic code was used in the phishing attack which implies that the actual payloads of the links changed frequently, which means that it was difficult for the AV tools to block them as they were perceived as threats. 
•  Phantom Domains: Another tactic employed was that of using AI in generating and disseminating phantom domains, that are actual web sites that appear to be legitimate but are in fact short lived specially created for this phishing attack, adding to the difficulty of detection. 

4. Exploitation of Human Vulnerabilities

This kind of attack’s success was not only in AI but also in the vulnerability of people, trust in familiar language and the tendency to obey authorities. 

Key Technical Features: 

•  Social Engineering: As for the second factor, AI determined specific psychological principles that should be used in order to maximise the chance of the targeted recipients opening the phishing emails, namely the principles of urgency and familiarity. 
•  Multi-Layered Deception: The AI was successfully able to have a two tiered approach of the emails being sent as once the targeted individuals opened the first mail, later the second one by pretext of being a follow up by a genuine company/personality. 

Response

On sighting the breach, the bank’s cybersecurity personnel spring into action to try and limit the fallout. They reported the matter to the Indian Computer Emergency Response Team (CERT-In) to find who originated the attack and how to block any other intrusion. The bank also immediately started taking measures to strengthen its security a bit further, for instance, in filtering emails, and increasing the authentication procedures. 

 Knowing the risks, the bank realised that actions should be taken in order to enhance the cybersecurity level and implement a new wide-scale cybersecurity awareness program. This programme consisted of increasing the awareness of employees about possible AI-phishing in the organisation’s info space and the necessity of checking the sender’s identity beforehand.

Outcome

Despite the fact and evidence that this bank was able to regain its functionality after the attack without critical impacts with regards to its operations, the following issues were raised. Some of the losses that the financial institution reported include losses in form of compensation of the affected customers and costs of implementing measures to enhance the financial institution’s cybersecurity. However, the principle of the incident was significantly critical of the bank as customers and shareholders began to doubt the organisation’s capacity to safeguard information in the modern digital era of advanced artificial intelligence cyber threats. 

 This case depicts the importance for the financial firms to align their security plan in a way that fights the new security threats. The attack is also a message to other organisations in that they are not immune from such analysis attacks with AI and should take proper measures against such threats.

Conclusion

The recent AI-phishing attack on an Indian bank in 2024 is one of the indicators of potential modern attackers’ capabilities. Since the AI technology is still progressing, so are the advances of the cyberattacks. Financial institutions and several other organisations can only go as far as adopting adequate AI-aware cybersecurity solutions for their systems and data. 

 Moreover, this case raises awareness of how important it is to train the employees to be properly prepared to avoid the successful cyberattacks. The organisation’s cybersecurity awareness and secure employee behaviours, as well as practices that enable them to understand and report any likely artificial intelligence offences, helps the organisation to minimise risks from any AI attack.

Recommendations

1. Enhanced AI-Based Defences: Financial institutions should employ AI-driven detection and response products that are capable of mitigating AI-operation-based cyber threats in real-time. 
2.  Employee Training Programs: CYBER SECURITY: All employees should undergo frequent cybersecurity awareness training; here they should be trained on how to identify AI-populated phishing. 
3.  Stricter Authentication Protocols: For more specific accounts, ID and other security procedures should be tight in order to get into sensitive ones. 
4.  Collaboration with CERT-In: Continued engagement and coordination with authorities such as the Indian Computer Emergency Response Team (CERT-In) and other equivalents to constantly monitor new threats and valid recommendations. 
5.  Public Communication Strategies: It is also important to establish effective communication plans to address the customers of the organisations and ensure that they remain trusted even when an organisation is facing a cyber threat. 

Through implementing these, financial institutions have an opportunity for being ready with new threats that come with AI and cyber terrorism on essential financial assets in today’s complex IT environments.

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Introduction

In 2019 India got its bill on Data protection in the form of the Personal Data Protection Bill 2019. This bill focused on digital rights and duties pertaining to data privacy. However, the bill was scrapped by the Govt in mid-2022, and a new bill was drafted, Successor bill was introduced as the Digital Personal Data Protection Bill, 2022 on 18th November 2022, which was made open for public comments and consultations and now the bill is expected to be tabled at the parliament in the Monsoon session.

What is DPDP, 2022?

Digital Personal Data Protection Bill, is the lasted draft regulation for data privacy in India. The bill has been essentially focused towards data protection by companies and the keep aspect of Puttaswamy judgement of data privacy as a fundamental right has been upheld under the scope of the bill. The bill comes after nearly 150 recommendations which the parliamentary committee made when the PDP, 2019 was scrapped.

The bill highlights the following keen aspects-

• Data Fiduciary- The entity (an individual, company, firm, state, etc.) which decides the purpose and means of processing an individual’s personal data.
• Data Principle- The individual to whom personal data is related.
• Processing- The entire cycle of operations that can be carried out concerning personal data.
• Gender Neutrality- For the first time in India’s legislative history, “her” and “she” have been used to refer to individuals irrespective of gender.
• Right to Erase Data- Data principals will have the right to demand the erasure and correction of data collected by the data fiduciary.
• Cross-border data transfer- The bill allows cross-border data after an assessment of relevant factors by the Central Government.
• Children’s Rights- The bill guarantees the right to digital privacy under the protection of parents/guardians.
• Heavy Penalties- The bill enforces heavy penalties for non-compliance with the provisions, not exceeding Rs 500 crore.

Data Protection Board

The bill lays down provisions for setting up a Data Protection Board. This board will be an independent body acting solely on the factors of data privacy and protection of the data principles and maintaining compliance by data fiduciaries. The board will be headed by a chairperson of essential and relevant qualifications, and members and various other officials shall assist him/her under the board. The board will serve grievance redressal to the data principles and can conduct investigation, inquiry, proceeding, and pass orders equivalent to a Civil court. The proceeding will be undertaken on the principle of natural justice, and the aggrieved can file an appeal to the High Court of appropriate jurisdiction.

Global Comparison

Many countries have data protection laws that regulate the processing of personal data. Some of the notable examples include:

• European Union: The EU’s General Data Protection Regulation (GDPR) is one of the world’s most comprehensive data protection laws. It regulates public and private entities’ processing of personal data and gives individuals a wide range of rights over their personal data.
• United States: The US has several data protection laws that apply to specific sectors or types of data, such as health data (HIPAA) or financial data (Gramm-Leach-Bliley Act). However, there is no comprehensive federal data protection law in the US.
• Japan: Japan’s Personal Information Protection Act (PIPA) regulates the handling of personal data by private entities and gives individuals certain rights over their personal data.
• Australia: Australia’s Privacy Act 1988 regulates the handling of personal data by public and private entities and gives individuals certain rights over their personal data.
• Brazil: Brazil’s General Data Protection Law (LGPD) regulates the processing of personal data by public and private entities and gives individuals certain rights over their personal data. It also imposes heavy fines and penalties on entities that violate the provisions of the law.

Overall, while there are some similarities in data protection laws across countries, there are also significant differences in scope, applicability, and enforcement. It is important for organisations to understand the data protection laws that apply to their operations and take appropriate steps to comply with these laws.

Parliamentary Asscent

The case of violation of the privacy policy by WhatsApp at the Hon’ble Supreme Court resulted in a significant advocacy for Data privacy as a fundamental right, and it was held that, as suggested otherwise in the privacy policy, Whatsapp was sharing its user’s data with Meta. This massive breach of trust could have led to data mismanagement affecting thousands of Indian users. The Hon’ble Supreme Court has taken due consideration of data privacy and its challenges in India and asked the Govt to table the bill in Parliament. The bill will be tabled for discussion in the monsoon session. The Supreme Court has set up a constitutional bench to check the bill’s scope, extent and applications and provide its judicial oversight.  The constitution bench of Justices KM Joseph, Ajay Rastogi, Aniruddha Bose, Hrishikesh Roy and CT Ravikumar has fixed the matter for hearing in August in order to enforce the potential changes and amendments in the act post the parliamentary discussion.

Conclusion

India is the world’s largest democracy, so the crucial aspects of passing laws and amendments have always been followed by the government and kept under check by the judiciary. The discussion over bills is a crucial part of the democratic process, and bills as important as Digital Personal Data Protection need to be discussed and analysed thoroughly in both houses of Parliament to ensure the govt passes a sustainable and efficient law.