# Teletraffic Analysis of DoS and Malware Cyber Attacks on P2P Networks under Exponential Assumptions
## 3 Related Work  
Different models have been proposed from different perspectives. We mention the most relevant and use Table 1 to summarizes introduced methodology. In [26], a model is presented that predicts how a P2P-based virus propagates through a network. Ref. [27] presents a density-dependent Markov jump process model for worms. In [28], a model based on an event-driven simulator is developed. Works focused on propagation can be found in [29; 30; 31; 32; 33; 34; 35]. Ref. [36] suggests a model for proactive worm prevention based on P2P networking technologies. In [37], a unified model is developed. Ref. [38] presents a model based on complex network theory. Ref. [39] submits a customized form of susceptible-exposed-infected. A new multi-node coordinated attack model is proposed in [8]. In the context of the Internet of Things, different approaches have been proposed [40; 41; 42; 43]. However, many of these works are focused on IoT systems, which have many key differences from P2P systems. Specifically, in P2P networks, it is desirable for all nodes to cooperate and communicate with the rest of the nodes to increase the system bandwidth. In contrast, in IoT systems, nodes usually have no clear advantage in communicating with any other node in the system. Rather, they communicate with the sink node or some specific nodes that may be used as relay nodes to reach the sink. In this regard, a malware attack would have an entirely unique behavior and performance as in the P2P network. Furthermore, for the DoS attack, if certain key nodes in an IoT system are impaired to communicate, the effect on the system performance would be greatly affected if the packet cannot reach the sink. This is especially true in P2P networks. Indeed, only a portion of nodes would not be able to download nor upload chunks, but the rest of the nodes would continue to operate normally, but with a decreased bandwidth. As we have mentioned, our model focuses on malware and DoS attacks on the P2P system since these are general attacks focused on disrupting the normal operation of the complete system and, therefore, are the most damaging attacks. In this regard, we provide a mathematical framework to quantitatively measure the effectiveness of such attacks. We model both the infection procedure and the case in which countermeasures are used to limit the propagation of malware in infected chunks. Furthermore, we consider a jamming \begin{table}
\begin{tabular}{c c} \hline
**Reference** & **Introduced Methodology** \\ \hline \multirow{3}{*}{[26]} & This model is a modified version of the \\  & susceptible–exposed–infected model \\  & from the field of epidemiology. \\ \hline \multirow{3}{*}{[27]} & Computationally simple hybrid deterministic/stochastic \\  & model for the observed scanning behavior on a local network. \\ \hline \multirow{3}{*}{[28]} & Event-driven simulator. \\ \hline \multirow{3}{*}{[29]} & Discrete simulations that provide some verification. \\ \hline \multirow{3}{*}{[30]} & Non-linear differential equations. \\ \hline \multirow{3}{*}{[31]} & Deep analysis of the features of \\  & file sharing and virus propagation. \\ \hline \multirow{3}{*}{[32]} & 0.01 Files in the network as subjects instead of \\  & the computers, as is traditional. \\ \hline \multirow{3}{*}{[33]} & Epidemiological modeling that predicts. \\ \hline \multirow{3}{*}{[34]} & In-depth analysis of the active malicious code characteristics. \\ \hline \multirow{3}{*}{[35]} & Based on the worm propagation characteristics \\  & and mechanism of a conditional triggered worm attack. \\ \hline \multirow{3}{*}{[37]} & Attacks of various pollution, including file-targeted attacks \\  & and index-targeted attacks. \\ \hline \multirow{3}{*}{[38]} & Complex network theory. \\ \cline{2-2}  & A customized form of susceptible–exposed–infected model \\ \cline{2-2}  & based on the study of epidemiology. \\ \hline \multirow{3}{*}{[8]} & Deterministic models of the propagation of \\  & computer viruses in a heterogeneous network. \\ \hline \end{tabular}
\end{table}