## 7 Attack Strategies
### Sybil Attacks  
Sybil attacks received their name from [PERSON]'s 2002 work, introducing the concept within peer-to-peer networks. The term references the book Sybil, representing the idea of one entity assuming multiple identities [117]. Historically, these attacks have challenged decentralized systems, from early file sharing to blockchain. In distributed systems, where trust and authenticity are critical, Sybil attacks exploit these by creating numerous fake identities. This allows attackers to dominate decision making, sway consensus, and distort information sharing. Such manipulation degrades the trust that is essential to distributed systems, undermining their cooperative nature [118; 119].  
Defense Protocols Against Sybil Attacks in FL  
Navigating the FL landscape necessitates not only the appreciation of its decentralized nature but also keen awareness of the underlying threats, which include Sybil attacks. As FL systems grow in complexity and adoption, ensuring the integrity and authenticity of the participating nodes becomes paramount. With this goal in mind, several defense protocols have emerged to counter the threat of Sybil attacks.  
One of the foundational approaches in this regard is the implementation of strong identity verification protocols. In an FL environment, the very act of onboarding a node carries huge significance. Ensuring that every node, before it can participate and contribute updates, undergoes a rigorous identity verification process can serve as the first line of defense. This involves not only traditional authentication mechanisms but can also include cryptographic methods, hardware attestations, or even third-party verifications. The primary objective is clear: to validate the legitimacy of a node before it becomes an active participant in the learning process.  
Identity verification, while vital, might not suffice on its own, especially if an attacker manages to bypass this first gate. This necessitates a second layer of defense in the form of rate limiting. By controlling and limiting the number of updates or the participation rate for newly onboard or untrusted nodes, FL systems can diminish the potential impact of malicious entities. For instance, a new node's updates could be given lesser weight in the global model until it establishes a track record of consistent and genuine contributions. This ensures that, even if a Sybil node were to infiltrate the network, its influence would be curtailed, at least until it gains undue credibility [120].  
While these proactive measures build a strong defense, they are complemented by reactive strategies centered around behavioral analysis. In FL, where data remain localized but updates are shared, monitoring the nature, pattern, and timing of these updates can reveal important information. By employing advanced analytics and machine learning techniques, it is possible to discern patterns consistent with Sybil attacks, such as suspiciously synchronized updates from multiple nodes. Detecting such anomalies in real time can allow for immediate intervention, either in the form of isolating the suspected nodes or subjecting their updates to further scrutiny.