## 3 Methodology: Proposed SybilSocNet Algorithm  
This study employed ML algorithms to develop predictive models that are capable of identifying Sybil nodes within social networks. Considering the widespread popularity of such platforms, e.g., Twitter and Facebook, they are the basis for millions to convene globally. Hence, Sybil attacks occur within such networks by penetrating fake user accounts, so individuals can falsely assume identities. Such fraudulent accounts are employed to influence the public or manipulate network dynamics, involving propagating specific ideologies through orchestrated campaigns [44]. The modus operandi involves a Sybil attacker who controls multiple Sybil nodes while orchestrating fraudulent reports on the social network admin, where such reports falsely implicate legitimate accounts as fraudulent or compromised. Subsequently, the administrator proceeds with automated actions that restrict the legitimate users' network access or even ban them. Cases of cybercrimes have been observed where social media was exploited to sway public opinion during elections, convincing votes for specific political parties. For instance, a hacker in Russia commissioned approximately 25,000 fake accounts, generating about 440,000 tweets that influenced public sentiment after a country's parliamentary election [45]. To construct a supervised ML model skilled at discerning Sybil nodes in social networks and to distinguish Sybil nodes from legitimate ones, it is vital to acquire a dataset comprising labeled tweets or posts categorized as Sybil or legitimate. Such a dataset becomes the foundation for employing diverse unsupervised ML algorithms to train a model to differentiate malicious users, posts, or tweets from authentic ones. Our research took the work by [PERSON] et al. and their algorithm SybilSCAR as our base [46] to detect Sybil nodes from the raw dataset of the Twitter follower-followee graph with the nodes labeled as \"benign\" or \"Sybil\" [47]. They did not use machine learning at all: they used two structure-based methods, one was the Random Walk (RW)-based method and the other was the Loop Belief Propagation (LBP)-based method, applying a (different) local rule to every user. Even though we use the same raw dataset, our approach is quite different, and we still obtained essentially the same results. Figure 2: RSS for Legit (**A**) and Sybil nodes (**B**) respectively. Our algorithm begins by cleaning and preprocessing the raw data. Python code was used to reformat the raw data into a matrix that is suitable for input into the machine learning algorithms. We trained multiple machine learning models on our formatted data and evaluated their performance on validation data to identify the most effective model for this research problem. Figure 3 provides an overview of the deployed dataset. We present here in the Algorithm 1 the pseudocode for the splitting of the data. This algorithm generates seventy one smaller data blocks from the dataset. It starts by reading the first line of the dataset and then skips seventy nodes. It captures each new node encountered after the skipping and adds it to a block. This process is repeated until the end of the dataset is reached, creating the first block. The algorithm then repeats all over again but now starting from the second line, beginning the creation of a second block, and continues this process until all seventy one blocks are generated. ```
Input :TwitterDataset Output :BlocksOfData \(BlocksOfData\leftarrow\) Empty array to store blocks of data; \(nodeIndex\gets 0\); for\(i\gets 1\)to71 do whileNot at end of TwitterDatasetdo \(currentNode\leftarrow\) read node at \(nodeIndex\); \(BlocksOfData\leftarrow\) append currentNode to \(BlocksOfData\); \(nodeIndex\gets nodeIndex+70\); ifnodeIndex is at the end of TwitterDatasetthen break;  end if Save SmallBlockOfData to a file; \(nodeIndex\gets i+1\);  end for return\(BlocksOfData\);
```  
**Algorithm 1**Pseudocode for splitting the data  
Algorithm 1 outlines the pseudocode for this initial part of the algorithm.