Biblio

Internet Protocol Version 6 (IPv6) is expected for widespread deployment worldwide. Such rapid development of IPv6 may lead to safety problems. The main threats in IPv6 networks are denial of service (DoS) attacks and distributed DoS (DDoS) attacks. In addition to the similar threats in Internet Protocol Version 4 (IPv4), IPv6 has introduced new potential vulnerabilities, which are DoS and DDoS attacks based on Internet Control Message Protocol version 6 (ICMPv6). We divide such new attacks into two categories: pure flooding attacks and source address spoofing attacks. We propose P4-NSAF, a scheme to defend against the above two IPv6 DoS and DDoS attacks in the programmable data plane. P4-NSAF uses Count-Min Sketch to defend against flooding attacks and records information about IPv6 agents into match tables to prevent source address spoofing attacks. We implement a prototype of P4-NSAF with P4 and evaluate it in the programmable data plane. The result suggests that P4-NSAF can effectively protect IPv6 networks from DoS and DDoS attacks based on ICMPv6.

Nowadays, in this COVID era, work from home is quietly more preferred than work from the office. Due to this, the need for a firewall has been increased day by day. Every organization uses the firewall to secure their network and create VPN servers to allow their employees to work from home. Due to this, the security of the firewall plays a crucial role. In this paper, we have compared the two most popular open-source firewalls named pfSense and OPNSense. We have examined the security they provide by default without any other attachment. To do this, we performed four different attacks on the firewalls and compared the results. As a result, we have observed that both provide the same security still pfSense has a slight edge when an attacker tries to perform a Brute force attack over OPNSense.

Artificial intelligence (AI) technologies have given the cyber security industry a huge leverage with the possibility of having significantly autonomous models that can detect and prevent cyberattacks – even though there still exist some degree of human interventions. AI technologies have been utilized in gathering data which can then be processed into information that are valuable in the prevention of cyberattacks. These AI-based cybersecurity frameworks have commendable scalability about them and are able to detect malicious activities within the cyberspace in a prompter and more efficient manner than conventional security architectures. However, our one or two completed studies did not provide a complete and clear analyses to apply different machine learning algorithms on different media systems. Because of the existing methods of attack and the dynamic nature of malware or other unwanted software (adware etc.) it is important to automatically and systematically create, update and approve malicious packages that can be available to the public. Some of Complex tests have shown that DNN performs maybe can better than conventional machine learning classification. Finally, we present a multiple, large and hybrid DNN torrent structure called Scale-Hybrid-IDS-AlertNet, which can be used to effectively monitor to detect and review the impact of network traffic and host-level events to warn directly or indirectly about cyber-attacks. Besides this, they are also highly adaptable and flexible, with commensurate efficiency and accuracy when it comes to the detection and prevention of cyberattacks.There has been a multiplicity of AI-based cyber security architectures in recent years, and each of these has been found to show varying degree of effectiveness. Deep Neural Networks, which tend to be more complex and even more efficient, have been the major focus of research studies in recent times. In light of the foregoing, the objective of this paper is to discuss the use of AI methods in fighting cyberattacks like malware and DDoS attacks, with attention on DNN-based models.

Nowadays network applications have more focus on content distribution which is hard to tackle in IP based Internet. Information Centric Network (ICN) have the ability to overcome this problem for various scenarios, specifically for Vehicular Ad Hoc Networks (VANETs). Conventional IP based system have issues like mobility management hence ICN solve this issue because data fetching is not dependent on a particular node or physical location. Many initial investigations have performed on an instance of ICN commonly known as Named Data Networking (NDN). However, NDN exposes the new type of security susceptibilities, poisoning cache attack, flooding Interest attack, and violation of privacy because the content in the network is called by the name. This paper focused on mitigation of Interest flooding attack by proposing new scheme, named Interest Flooding Attack Mitigation Scheme (IFAMS) in Vehicular Named Data Network (VNDN). Simulation results depict that proposed IFAMS scheme mitigates the Interest flooding attack in the network.