A Strategy for Detection and Mitigation of DoS Attacks on Software-Defined Networks

Computer networks support applications in virtually every area of application and knowledge, and as such, they have widely distributed structures and are susceptible to security attacks in general.Software-Defined Networks (SDN), in turn, are a technological solution that has several advantages by separating the control plane from the data plane in the structuring of computer networks. Given this technological difference, software-defined networks are a network implementation paradigm used to mitigate network security attacks. In summary, the use of SDN to mitigate network attacks provides greater flexibility in implementing the attack strategy. However, the separation of control and data planes creates new points of vulnerability for the security of the network operation.The denial of service attack (DoS) of the type Syn-Flooding is one of the most common possible attacks. It can cause, concerning the network, the commitment to perform services and, concerning the operation of the SDN, the commitment in the bandwidth of the communication channel between the control planes and the data plane, the saturation of the ow table in the switch, and the increasing of the processing load in the controller.In general, the investigation about new strategies aimed at safety with SDN becomes necessary to improve security strategies for network attacks and maximize the reliability of SDN operation, allowing use in different application scenarios. This work presents a defense strategy against attacks of DoS Syn-Flooding using the SDN facilities of an integrated controller with an intrusion detection system (IDS).The proposed strategy aims to mitigate Syn-Flooding DoS attacks and the vulnerability arising from the use of SDN to mitigate attacks.

An Indigenous Solution for SYN Flooding

SYN flooding is one of the most challenging problems that many networks applications face, particularly those that are security-related. Disrupting a server's daily function and assigning it to other tasks leaves it a constantly busy server that processes little usable data. In this research, a comprehensive INDIGSOL approach is demonstrated that not only detects SYN flooding but also prevents the attacker(s) from making such attempts in the future. The designed approach has four modules such as node registration and validation, packet capturing, dynamic check system, and hook activation. The approach is further checked and compared with some state-of-the-art baselines on various parameters like detection time, response/processing time, and number of malicious packets detection. It is observed that INDIGSOL performed better than other baselines with an average accuracy of 99% malicious packet detection in six scenarios along with 13.4% faster detection time and 11.2% faster response/processing time. Overall, the provided solution is scalable, robust, and highly accurate that prevents SYN flooding in a timely manner.

Cyber-Security: Dos Attack Outcomes are Dangerous

Dos attacks are an attractive and hot topic nowadays because it is very common for attackers to make a victim and exploit it. In this paper, different kinds of techniques and commands have implemented and contrast for the outcomes of these attacks. By performing, experiments problems are identified. By following experiments, the goal is prevention and mitigation of Dos attacks at large extent by knowing the outcomes of the attacks. For proving the work, different types of experiments are performed on different platforms and observed that Dos attacks are how dangerous to our systems. In this paper, hping3 and syn flooding are performed. Hping3 is used for sending packets in a modified, crafted form. It is the networking tool used by developers to perform a DOS attack. SYN flooding is used for sending so many requests in an insane amount to the server and used all of the resources of the server.

Privacy Preserving Machine Learning in Various Attacks on Security Threat Models

Intrusion Detection System(IDS) is regularly used to recognize and forestall strange practices in an organization the executives framework. The fundamental thought of IDS is to utilize highlight esteems from network bundle catch system to characterize whether a conduct is anomalous. Notwithstanding, most customary order calculations are unequipped for perceiving obscure practices. The aim of the project is to review the state-of-the art of detection mechanisms of SYN flooding. The detection schemes for SYN Flooding attacks classified broadly into three categories – detection schemes based on the router data structure, statistical analysis of the packet flow based on artificial intelligence. The advantages and disadvantages for various detection schemes under each category have been critically examined Additionally, this crossover methodology for the proposed calculation is pointed toward improving the exactness of strange conduct identification of such a framework, diminishing the calculation season of an arrangement calculation, and making it feasible for the IDS to perceive the obscure and new variation assaults in an organization climate. The test results shows that the proposed calculation outflanks the wide range of various order calculations thought about in this paper regarding the precision.

Perancangan Bot Untuk Monitoring Server Dari Serangan Distributed Denial Of Service Menggunakan JSON WEB Token

Untuk mengamankan sebuah server diperlukan sistem monitoring agar meminimalisir resiko jika terjadi percobaan intrusi. Contoh serangan yang dapat mengancam server adalah serangan DDOS (Distributed Denial of Service). Berdasarkan permasalahan tersebut, diperlukan sebuah bot untuk melakukan monitoring server dari serangan DDOS, dalam penelitian ini difokuskan serangan DDOS berjenis UDP Flooding dan SYN Flooding. Monitoring server memanfaatkan bot untuk mendeteksi serangan DDOS berjenis UDP Flooding dan SYN Flooding dengan membatasi jumlah paket yang dikirimkan kepada port yang dibuka oleh server, apabila jumlah paket yang dikirimkan lebih dari 100 paket per detik maka bot akan mengidentifikasi pengiriman tersebut sebagai serangan. Penerapan metode JSON Web Token agar bisa dioperasikan oleh bot admin server yakni dengan mengimplementasikan package JSON Web Token pada bot lalu memanggil fungsi encode agar JSON Web Token dapat melakukan enkripsi pada klaim yang berisikan data penyerang oleh bot, sehingga bot admin server dapat mengamankan klaim yang akan dikirimkan ke admin server.

Analisa Sistem Identifikasi DDoS Menggunakan KNN Pada Jaringan Software Defined Network(SDN)

AbstrakAbstrak - Kebutuhan pada jaringan mengutamakan performa untuk mendukung sebuah efisiensi jaringan merupakan hal penting untuk saat ini. Penentuan konfigurasi yang semakin banyak dan kompleks serta kontrol jaringan yang semakin rumit, membuat jaringan semakin tidak fleksibel dan susah untuk diterapkan pada sebuah topologi jaringan yang besar. Software Defined Network (SDN) muncul dengan mekanisme yang dapat menyelesaikan masalah tersebut. Pada dasarnya konsep dari Software Defined Network (SDN) adalah memisahkan kontroller dan data/forwarding plane, sehingga mampu untuk me-menejemen jaringan yang begitu banyak dalam sebuah kontroller. Namun pada kontroller belum memiliki keamanan yang cukup untuk melindungi dari serangan jaringan seperti DDoS, SYN Flooding Attack sehingga kontroller akan menjadi target dari attacker. Sehingga penelitian ini mengusulkan penambahan aplikasi machine learning pada kontroller untuk menangani serangan seperti DDoS dan SYN Flooding Attack. Dalam penelitian ini kontroller yang digunakan adalah ryu controller yang menggunakan bahasa pemrograman python. Dalam penelitian ini menggunakan topologi linear pada mininet dan membuat paket dalam format .pcap untuk pengujian serangan yang dilakukan. Sehingga dapat mengetahui rata-rata jumlah paket yang masuk dan keluar dan keberhasilan dalam melakukan mitigasi terhadap paket yang dianggap DDoS.Abstract The need for the network to prioritize performance to support a network efficiency is important for now. Determination of configurations that are more and more complex and increasingly complicated network control, makes the network more inflexible and difficult to apply to a large network topology. Software Defined Network (SDN) appears with a mechanism that can solve the problem. Basically the concept of Software Defined Network (SDN) is to separate the controller and the data / forwarding plane, so that it is able to manage so many networks in a controller. But the controller does not have enough security to protect against network attacks such as DDoS, SYN Flooding Attack so the controller will be the target of the attacker. So this study proposes adding machine learning applications to controllers to handle attacks such as DDoS and SYN Flooding Attack. In this study the controller used is the Ryu controller that uses the Python programming language. In this study using a linear topology on Mininet and create a package in. Pcap format for testing attacks carried out. So as to know the average number of incoming and outgoing packages and success in mitigating packages that are considered DDoS.