# Studying the Impact of Different TCP DoS Attacks on the Parameters of VoIP Streams
## 2 Related Work  
In [31], the authors propose their mechanism for protecting SIP (Session Initiation Protocol) from attacks, which aims to detect vulnerabilities in lesser-known features of the protocol. For the purpose of the study, the authors used a virtual machine equipped with Trixbox, an Asterisk-based IP-PBX system that served as the attack target, and softphones. Asterisk was affected by SIP-DRDoS (Distributed Reflection Denial-of-Service) attacks; thus, the authors verified the effectiveness of their protection mechanism. These attacks bypassed any defenses such as firewalls, IDS/IPS (Intrusion Detection system/Intrusion Protection System), control lists, etc. As a result of the attack, the CPU was overloadedmultiple times. The proposed protection mechanism reduced the CPU load and the impact of the attack on the system. In [32], the authors propose a new protocol for SIP security--secure SIP (S-SIP). The proposed protocol was based on a thorough literature review performed by the authors, which examines different ways to secure the SIP protocol. For the purpose of the research and to prove the feasibility of the developed protocol, the authors created an experimental network in which they carried out various experiments. The results show that the proposed protocol offered more security capabilities and imposed a lower computational load. In [33], the authors propose a method for DDoS-attack (Distributed Denial of Service attack) detection based on the Deep Packet Inspection (DPI) method for packet analysis (a kind of an intrusion-detection system). Through this method, certain information (attack signatures) was extracted from the packets. Using this information, new rules were applied to detect VoIP DDoS attacks. For the purpose of the study, the authors used Asterisk FREE PBX. The experimental studies confirmed that the use of the IDS system prevented DDoS attacks. In [34], the authors review different methods for detecting DoS and DDoS attacks applied to SIP and classify them based on different factors. Furthermore, the authors explore the strengths and weaknesses of these methods. Finally, the authors provide a discussion of how to improve the reviewed methods and propose future research directions to build more effective solutions for detecting DoS and DDoS attacks. In [35], the authors propose the use of a deep-learning model based on Recurrent Neural Networks (RNNs) to detect low- and high-intensity DDoS attacks. To prove the feasibility of the proposed method, the authors used real traffic traces (SIP messages) that were \"injected\" with malicious messages. Based on the obtained results, it was proved that the proposed method had high detection accuracy and low detection times. In [36], the authors provide an in-depth review of the most-used IDS/IPS systems, with a corresponding analysis for each system considered. From the analysis carried out by the authors, it was found that eDAIT and the e-GAP performed the best out of all the reviewed models. In [37], the authors developed a Support Vector Machine (SVM) learning algorithm for detection and prevention, which was used to detect DoS attacks. To validate the applicability of the algorithm, the authors used IP-PBX real-time traffic datasets. The obtained results, such as high detection rate, low execution times to classify the attack, low rates of false negatives, and other findings proved the applicability of the algorithm. In [38], the author again proposes the use of deep-learning and entropy techniques to detect DDoS attacks in SIP-based systems. The proposed algorithm was a combination of deep-learning convolutional neural networks and a stacked bidirectional long short-term memory network. To validate the applicability of the algorithm, the author used a dataset of different types and intensities of DDoS flooding attacks. The proposed algorithm proved its feasibility by achieving high detection rates and low attack-detection times.