Deep learning model for distributed denial of service (DDoS) detection

Distributed denial of service (DDoS) attacks is one of the serious threats in the domain of cybersecurity where it affects the availability of online services by disrupting access to its legitimate users. The consequences of such attacks could be millions of dollars in worth since all of the online services are relying on high availability. The magnitude of DDoS attacks is ever increasing as attackers are smart enough to innovate their attacking strategies to expose vulnerabilities in the intrusion detection models or mitigation mechanisms. The history of DDoS attacks reflects that network and transport layers of the OSI model were the initial target of the attackers, but the recent history from the cybersecurity domain proves that the attacking momentum has shifted toward the application layer of the OSI model which presents a high degree of difficulty distinguishing the attack and benign traffics that make the combat against application-layer DDoS attack a sophisticated task. Striding for high accuracy with high DDoS classification recall is key for any DDoS detection mechanism to keep the reliability and trustworthiness of such a system. In this paper, a deep learning approach for application-layer DDoS detection is proposed by using an autoencoder to perform the feature selection and Deep neural networks to perform the attack classification. A popular benchmark dataset CIC DoS 2017 is selected by extracting the most appealing features from the packet flows. The proposed model has achieved an accuracy of 99.83% with a detection rate of 99.84% while maintaining the false-negative rate of 0.17%, which has the heights accuracy rate among the literature reviewed so far.

PROJECT REPORT ON OSI MODEL

The OSI model has been virtually for 30 years and still plays a viable part in understanding how networks communicate. In fact, it is now part of the networking vernacular. The Organization International de Normalization (International Standards Organization, or ISO), which has been developing international standards since 1946.Adopted in 1984, the OSI Basic Reference Model defines a network tracery consisting of seven layers in the communications process. Each layer is having its own responsibilities.

Osi Model: The Basics Structure of Network Communication

In the present time, if we see around the world we can realize that information transfer through one place to another is very easy. A person lives in America easy do business with the person live far away from it. All this can be achieved by the phenomenon known as Networking. And the device through which the information are transferred are called interconnected device. As we know, in present time our need is not only transfer or sharing of information but in a secure way. So with the help of this we are not just transferring the information but in a secure manner To understand the whole phenomenon of this networking, the basic requirement is OSI LAYER Model. This is not just a model but a complete frame which gives us whole information of its working as well as link between them. So through this paper we give some basics concept building of OSI LAYER which help in understanding the Networking

THE MAIN STAGES OF DEVELOPMENT OF THE CRYPTOGRAPHIC PROTOCOLS SSL/TLS AND IPsec

The paper discusses the main stages of development of cryptographic protocols from SSL 2.0 (Secure Socket Layer) to TLS 1.3 (Transport Layer Security), which ensure the protection of transport layer data in the OSI model. A brief description of the modification of the RuTLS protocol based on TLS 1.3 and their main differences is given. The development of IPsec, which provides cryptographic protection of communications at the network level of the OSI model, is considered using examples of the development of the three most commonly used protocols. These include IKE (Internet Key Exchange), AH (Authentication Header), and ESP (Encapsulation Security Payload). For the SSL/TLS and IPsec specifications, the basic handshake protocols and the main stages of their development are considered. The described handshakes include primary cryptographic information exchange cycles in the form of identifiers of interaction participants, one-time numbers, lists of supported cryptographic combinations. Authentication of participants based on certificates, shared symmetric keys, data exchange for establishing a shared Diffie — Hellman secret, development of key material for secret keys of communication sessions, message authentication, and other cryptographic parameters are presented. For different versions of SSL/TLS and IPsec, the logical structures of application data cryptographic protection functions are described.

Data Transmission Model with Lost Fragments Recovery Based on Application Layer ARQ

Wireless networks in difficult conditions of signal receiving are characterized by a high level of burst data losses, at which a large number of data fragments can be lost in a row. In this case, to recover the lost data, the use of forward error correction methods (FEC) in most cases does not give a sufficient effect. The use of standard data loss recovery methods based on automatic retransmission request (ARQ) at the data link and transport layers of the OSI model can lead to significant delays, which is often unacceptable for real-time streaming services. In such a case, it may be preferable to skip the piece of data rather than delay waiting for the piece to be delivered on retransmissions. The use of ARQ-based techniques on application layer of OSI model for data streaming allows for a more efficient recovery of lost data chunks in wireless networks with a high level of burst losses. The known models of a discrete channel for wireless networks allow for analytically assessing the probability of data loss, however, they do not take into account cases with retransmission of lost data. The study proposes a mathematical model of data transmission in a wireless communication channel based on the Gilbert model, which takes into account the loss recovery by the ARQ method and allows you to calculate the data loss ratio. To check the adequacy of the proposed model, a software was developed that ensures the transmission of data streaming in a wireless communication network with recovery of fragment losses at the application level, and a corresponding experimental study was carried out. It is shown that the mathematical model takes into account the burstiness of transmitted data losses and their recovery by the ARQ method.

The Architectural Dynamics of Encapsulated Botnet Detection (EDM)

Botnet is one of the numerous attacks ravaging the networking environment. Its approach is said to be brutal and dangerous to network infrastructures as well as client systems. Since the introduction of botnet, different design methods have been employed to solve the divergent approach but the method of taking over servers and client systems is unabated. To solve this, we first identify Mpack, ICEpack and Fiesta as enhanced IRC tool. The analysis of its role in data exchange using OSI model was carried out. This further gave the needed proposal to the development of a High level architecture representing the structural mechanism and the defensive mechanism within network server so as to control the botnet trend. Finally, the architecture was designed to respond in a proactive state when scanning and synergizing the double data verification modules in an encapsulation manner within server system.