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Network Security and Attacks in Blockchains | Advanced Blockchain Concepts
Introduction to Blockchain
course content

Course Content

Introduction to Blockchain

Introduction to Blockchain

1. Getting Started
2. Blocks and Transactions
3. Advanced Blockchain Concepts

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Network Security and Attacks in Blockchains

Despite remarkable security features, decentralization, and immutability, blockchain networks can still fall victim to various attacks, so let's explore some of the major network security challenges and attacks that blockchain systems face, along with strategies to mitigate these vulnerabilities.

51% Attacks

In a 51% attack, an attacker gains control of over half of a blockchain network's mining hashrate or staking power. This majority control enables the attacker to double-spend coins, prevent new transactions from being confirmed, and block other miners or validators from the network. Such attacks, however, are of no concern for major blockchains like Bitcoin and Ethereum and more feasible in smaller, less decentralized networks, emphasizing the need for broad network participation and decentralization.

Here is an example of such attack on Bitcoin (just for illustration):

In blockchains with proof of stake (PoS), like Ethereum, an attacker would have to own more than 50% of all the cryptocurrency on the network

In order for blockchains to minimize the rist of 51% attacks, the following steps should be taken:

  • encouraging greater network participation;
  • adopting improved consensus mechanisms;
  • monitoring for unusual concentrations of hashrate.

Sybil Attacks

A Sybil attack involves an attacker creating numerous fake identities to gain a disproportionately large influence on the network. This can disrupt network operations or skew the consensus process.

To carry out a Sybil attack, the attacker first takes control of several nodes within the network. This is often achieved by creating a large number of fake nodes or by hijacking existing ones. Once in control, the attacker can intercept, monitor, or manipulate the information passing through these nodes. This might involve gathering sensitive information about users, such as their IP addresses, transaction details, or network activity.

Here is an illustration of a Sybil attack:

A possible solution to minimize risks of a Sybil attack is implementing effective identity verification mechanisms like Proof of Work or Proof of Stake which makes it costly or difficult to create multiple misleading identities.

Eclipse Attacks

Eclipse attacks represent a significant security threat in decentralized blockchain networks, exploiting the network's structural limitations to isolate and manipulate individual nodes.

The process of launching an eclipse attack involves a malicious actor targeting a specific node—such as a miner, a well-connected node, or one associated with a particular organization or user—with the intention of isolating it from the rest of the network. The attacker constructs a botnet, a network of nodes under their control, positioned to intercept and dominate all connections to the targeted node. This strategic positioning allows the attacker to monopolize the target node's connections, effectively controlling all information the node receives and transmits.

Let's take a look at the illustration below to make things clear:

As a result of this attack, the botnet can feed false information to the isolated node, obscuring its view of legitimate transactions or blocks and potentially leading to fraudulent activities such as double spending.

Random selection of new connections instead of using the same nodes repeatedly can minimize the risk of this attack.

DDoS Attacks

Speaking Distributed Denial of Service (DDoS) attacks, they flood a blockchain network or its nodes with an overwhelming amount of traffic, aiming to incapacitate the network and prevent legitimate transactions from being processed.

The following steps can be taken as precautions:

  • implementing rate limiting, using DDoS protection services;

  • diversifying node distribution;

  • employing anti-DDoS hardware or software solutions to absorb or deflect the flood of requests.

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Section 3. Chapter 2
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