Hackernoon logoMapping Cybersecurity For The Distributed Web by@realchainlife

Mapping Cybersecurity For The Distributed Web

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@realchainlifeSonia John

Protocol Developer

The internet is the largest computer network in the world, today we use it across the globe to collect, transfer and process information through forms as diverse as data server warehouses, in-hand mobile devices or other connected devices. Due to its constantly changing size and shape, we are faced with issues around cyber attacks, database vulnerabilities and hardware defenses.

Traditionally, system maintainers used static cyber firewalls around a network perimeter to patch any discovered holes. This method is not without its demerits, evident in the great number of hacks, data leaks and privacy violations witnessed over the last couple decades. In this post we will review the new approaches that rely on knowing the cyber terrain within decentralized & distributed networks that form part of Web3.0.
We'll dive deep into distributed data structures and cover the various security aspects of distributed networks that are crucial for effective cyber security standards implementation. These include (i) cryptographic key management, (ii) privacy protection mechanisms, (iii) critical infrastructure security and (iv) predictive cyber protection.

Cryptographic Key Management

The ideal architecture of modern cyber security is one built on the foundations of Trusted Computing and Zero Trust combined to provide high-quality data security. In this model, algorithmic controls are applied and verification performed ensuring data privacy and confidentiality that makes it simpler for users to operate. These architectural components include:
  • Key management: This is an essential component of a cryptographic access control system within a distributed or decentralized network. It manages the secret keys assigned to network entities in such a way that only authorized users can access particular resources.

    The important objective of key management in a network with multiple nodes is to restrict access of confidential data to authorized users verified using each nodes key. Cryptographic algorithms are continually being improved to perfect functions like granting/revoking access, data restructuring in case of user/node revocation or deletion.
  • Key Tampering: While strides have been made to ensure consensus algorithms are resilient to real world attacks, protocol architects need to constantly design new implementation models of cryptographic schemes that can capture potential attacks for example the deployment of encryption in detecting and deterring tampering attacks.

    The Hardened Enterprise Security Platform for example deploys a security encryption framework that's designed to comprehensively secure aspects and node endpoints of a networks' core cryptographic infrastructure, key management, secure data storage and more.
Networks that are incorporating these architectural features include Bitcoin, IPFS, and other major blockchain networks.

Privacy Protection Mechanisms

Although we have briefly covered key management and its privacy enhancing qualities, networks like the Hyperledger Fabric developed more frameworks such as certificate authority, channels and private data collection to improve privacy protection mechanisms.
Explored further, the privacy protection frameworks of the Fabric Network comprise of the following aspects:
  • asymmetric cryptography to separate transaction data from on-chain records
  • digital certificate management service
  • multi-channels that separates information between different channels
  • privacy data collection further satisfies the need for the isolation of privacy data between different organizations within the same channel
In all distributed networks, these frameworks produce data that is tamper-proof, traceable and trustworthy. This nature of the technology is expected to be the cornerstone of Web 3.0. Yet despite these underlying capabilities, cyber security standards and controls must be followed within other technical infrastructure linked to the distributed network to protect them from outside attacks.

Critical Infrastructure Security

The adoption rate of serverless infrastructure has been on the increase in recent years and to date, billions have been and continue to be invested in the development and support of these infrastructure. Given that serverless computing is a relatively new technology, its unique security risks have been a challenge to understand and manage.
  • Software vulnerabilities: Since this technology started shipping in millions of devices globally, standards have been created and solutions are emerging to tackle cyber attacks that target critical consumer and business physical systems, such as smart connected devices. Unlike centralized networks which are vulnerable to DoS & DDos attacks, distributed networks are prone to scalability and code vulnerability attacks that result in extensive financial burdens.
  • Hardware vulnerabilities: In spite of the increasing sophistication of software attacks, distributed networks like Bitcoin have to contend with evolving hardware vulnerabilities through unsafe hardware architecture which can be used to extract private keys or complex production processes that could result in disgruntled or malicious chip designers implanting malicious logic or circuits without being noticed.
Some of the measures network maintainers can take to mitigate risks associated with database changes, code modifications and cloud storage events include the performance of regular code audits to address exploitable security vulnerabilitiesusing CI/CD to mitigate bugs or code vulnerabilities and leveraging tools to increase visibility and efficiency of attack indicators. 

Predictive Cyber Protection

The evolving complex nature of cyber attacks on distributed networks has called for development of predictive cyber defense beyond baking hack resistance directly into hardware. Several solutions implemented on various networks automatically generate, deploy and manage secure configurations of components and sub protocols for use in these networks.
Machine learning solutions and AI tools are also used to develop integrated systems, to transform data to signals, relevant to predicting network attacks. Although its yet to be seen which solutions will stand out in this industry, it can be argued that careful implementation of other cyber security components, like key management, privacy protection mechanisms and infrastructure security, on any distributed network will minimize system vulnerabilities.
Conclusion
The Integration of Trusted Computing standards, such as decentralized DNS systems or distributed nodes, to guide users, processes or technology has produced system neutral solutions that address the challenge of distributed security. These are the key points to consider while developing an effective cyber security strategy for a distributed or decentralized web.

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