Biblio

Protecting an identity of IPv6 packet against Denial-of-Service (DoS) attack, depend on the proposed methods of cryptography and steganography. Reliable communication using the security aspect is the most visible issue, particularly in IPv6 network applications. Problems such as DoS attacks, IP spoofing and other kinds of passive attacks are common. This paper suggests an approach based on generating a randomly unique identities for every node. The generated identity is encrypted and hided in the transmitted packets of the sender side. In the receiver side, the received packet verified to identify the source before processed. Also, the paper involves implementing nine experiments that are used to test the proposed scheme. The scheme is based on creating the address of IPv6, then passing it to the logistics map then encrypted by RSA and authenticated by SHA2. In addition, network performance is computed by OPNET modular. The results showed better computation power consumption in case of lost packet, average events, memory and time, and the better results as total memory is 35,523 KB, average events/sec is 250,52, traffic sent is 30,324 packets/sec, traffic received is 27,227 packets/sec, and lose packets is 3,097 packets/sec.

DDoS attacks are usually accompanied by IP spoofing, but the availability of existing DDoS defense systems for high-speed networks decreases when facing DDoS attacks with IP spoofing. Although IP traceback technologies are proposed to focus on IP spoofing in DDoS attacks, there are problems in practical application such as the need to change existing protocols and extensive infrastructure support. To defend against DDoS attacks under IP spoofing in high-speed networks, we propose a novel DDoS defense system, IM-Shield. IM-Shield uses the address pair consisting of the upper router interface MAC address and the destination IP address for DDoS attack detection. IM-Shield implements fine-grained defense against DDoS attacks under IP spoofing by filtering the address pairs of attack traffic without requiring protocol and infrastructure extensions to be applied on the Internet. Detection experiments using the public dataset show that in a 10Gbps high-speed network, the detection precision of IM-Shield for DDoS attacks under IP spoofing is higher than 99.9%; and defense experiments simulating real-time processing in a 10Gbps high-speed network show that IM-Shield can effectively defend against DDoS attacks under IP spoofing.

In today's world the number of consumers of cloud computing is increasing day by day. So, security is a big concern for cloud computing environment to keep user's data safe and secure. Among different types of attacks in cloud one of the harmful and frequently occurred attack is Distributed Denial of Service (DDoS) attack. DDoS is one type of flooding attack which is initiated by sending a large number of invalid packets to limit the services of the victim server. As a result, server can not serve the legitimate requests. DDoS attack can be done by a lot of strategies like malformed packets, IP spoofing, smurf attack, teardrop attack, syn flood attack, local area network denial (LAND) attack etc. This paper focuses on IP spoofing and LAND based DDoS attack. The objective of this paper is to propose an algorithm to detect and prevent IP spoofing and LAND attack. To achieve this objective a new approach is proposed combining two existing solutions of DDoS attack caused by IP spoofing and ill-formed packets. The proposed approach will provide a transparent solution, filter out the spoofed packets and minimize memory exhaustion through minimizing the number of insertions and updates required in the datatable. Finally, the approach is implemented and simulated using CloudSim 3.0 toolkit (a virtual cloud environment) followed by result analysis and comparison with existing algorithms.

The Spoofer project has collected data on the deployment and characteristics of IP source address validation on the Internet since 2005. Data from the project comes from participants who install an active probing client that runs in the background. The client automatically runs tests both periodically and when it detects a new network attachment point. We analyze the rich dataset of Spoofer tests in multiple dimensions: across time, networks, autonomous systems, countries, and by Internet protocol version. In our data for the year ending August 2019, at least a quarter of tested ASes did not filter packets with spoofed source addresses leaving their networks. We show that routers performing Network Address Translation do not always filter spoofed packets, as 6.4% of IPv4/24 tested in the year ending August 2019 did not filter. Worse, at least two thirds of tested ASes did not filter packets entering their networks with source addresses claiming to be from within their network that arrived from outside their network. We explore several approaches to encouraging remediation and the challenges of evaluating their impact. While we have been able to remediate 352 IPv4/24, we have found an order of magnitude more IPv4/24 that remains unremediated, despite myriad remediation strategies, with 21% unremediated for more than six months. Our analysis provides the most complete and confident picture of the Internet's susceptibility to date of this long-standing vulnerability. Although there is no simple solution to address the remaining long-tail of unremediated networks, we conclude with a discussion of possible non-technical interventions, and demonstrate how the platform can support evaluation of the impact of such interventions over time.

Denial-of-Service attack (DoS attack) is an attack on network in which an attacker tries to disrupt the availability of network resources by overwhelming the target network with attack packets. In DoS attack it is typically done using a single source, and in a Distributed Denial-of-Service attack (DDoS attack), like the name suggests, multiple sources are used to flood the incoming traffic of victim. Typically, such attacks use vulnerabilities of Domain Name System (DNS) protocol and IP spoofing to disrupt the normal functioning of service provider or Internet user. The attacks involving DNS, or attacks exploiting vulnerabilities of DNS are known as DNS based DDOS attacks. Many of the proposed DNS based DDoS solutions try to prevent/mitigate such attacks using some intelligent non-``network layer'' (typically application layer) protocols. Utilizing the flexibility and programmability aspects of Software Defined Networks (SDN), via this proposed doctoral research it is intended to make underlying network intelligent enough so as to prevent DNS based DDoS attacks.

Dynamic Fuzzy Rule Interpolation (D-FRI) offers a dynamic rule base for fuzzy systems which is especially useful for systems with changing requirements and limited prior knowledge. This suggests a possible application of D-FRI in the area of network security due to the volatility of the traffic. A honeypot is a valuable tool in the field of network security for baiting attackers and collecting their information. However, typically designed with fewer resources they are not considered as a primary security tool for use in network security. Consequently, such honeypots can be vulnerable to many security attacks. One such attack is a spoofing attack which can cause severe damage to the honeypot, making it inefficient. This paper presents a vigilant dynamic honeypot based on the D-FRI approach for use in predicting and alerting of spoofing attacks on the honeypot. First, it proposes a technique for spoofing attack identification based on the analysis of simulated attack data. Then, the paper employs the identification technique to develop a D-FRI based vigilant dynamic honeypot, allowing the honeypot to predict and alert that a spoofing attack is taking place in the absence of matching rules. The resulting system is capable of learning and maintaining a dynamic rule base for more accurate identification of potential spoofing attacks with respect to the changing traffic conditions of the network.

Cloud computing is a long-established technique which deals with storage and processing of information. In cloud computing, any user is liable to pay and demand according to its particular needs. Due to various limitations of cloud computing like higher latency and more bandwidth consumptions for transmitted information, the origination of Fog computing was essential. Fog computing inherits all the advantages of cloud computing, and also brings computing at the network's edge. In addition, security is a very serious concern for cloud computing. In this paper, fog computing is used as a defensive approach from the day-to-day increasing security threats particularly DDoS attacks in cloud computing. Here an architecture has been proposed to obstruct the malicious traffic generated by the DDoS attack from user to the cloud by utilizing the benefits of fog computing. Fog functions as a filtering layer for the traffic generated and is placed between user and cloud. This paper primarily works to improve the overall performance of the network and enhances reduction in the traffic forwarded to the cloud.

The IoT (Internet of Things) is one of the primary reasons for the massive growth in the number of connected devices to the Internet, thus leading to an increased volume of traffic in the core network. Fog and edge computing are becoming a solution to handle IoT traffic by moving timesensitive processing to the edge of the network, while using the conventional cloud for historical analysis and long-term storage. Providing processing, storage, and network communication at the edge network are the aim of fog computing to reduce delay, network traffic, and decentralise computing. In this paper, we define a framework that realises fog computing that can be extended to install any service of choice. Our framework utilises fog nodes as an extension of the traditional switch to include processing, networking, and storage. The fog nodes act as local decision-making elements that interface with software-defined networking (SDN), to be able to push updates throughout the network. To test our framework, we develop an IP spoofing security application and ensure its correctness through multiple experiments.

With the global widespread usage of the Internet, more and more cyber-attacks are being performed. Many of these attacks utilize IP address spoofing. This paper describes IP spoofing attacks and the proposed methods currently available to detect or prevent them. In addition, it presents a statistical analysis of the Hop Count parameter used in our proposed IP spoofing detection algorithm. We propose an algorithm, inspired by the Hop Count Filtering (HCF) technique, that changes the learning phase of HCF to include all the possible available Hop Count values. Compared to the original HCF method and its variants, our proposed method increases the true positive rate by at least 9% and consequently increases the overall accuracy of an intrusion detection system by at least 9%. Our proposed method performs in general better than HCF method and its variants.
 

IP spoofing based DDoS attack that relies on multiple compromised hosts in the network to attack the victim. In IP spoofing, IP addresses can be forged easily, thus, makes it difficult to filter illegitimate packets from legitimate one out of aggregated traffic. A number of mitigation techniques have been proposed in the literature by various researchers. The conventional Hop Count Filtering or probabilistic Hop Count Filtering based research work indicates the problems related to higher computational time and low detection rate of illegitimate packets. In this paper, DPHCF-RTT technique has been implemented and analysed for variable number of hops. Goal is to improve the limitations of Conventional HCF or Probabilistic HCF techniques by maximizing the detection rate of illegitimate packets and reducing the computation time. It is based on distributed probabilistic HCF using RTT. It has been used in an intermediate system. It has the advantage for resolving the problems of network bandwidth jam and host resources exhaustion. MATLAB 7 has been used for simulations. Mitigation of DDoS attacks have been done through DPHCF-RTT technique. It has been shown a maximum detection rate up to 99% of malicious packets.