Mitigating DDoS attacks towards Top Level Domain name service

In the context of work on internationalizing the Domain Name System (DNS), there have been extensive discussions about multilingual or internationalized top level domain names (TLDs), especially for countries whose predominant language is not written in a Roman-based script. This document reviews some of the motivations for such domains, several suggestions that have been made to provide needed functionality, and the constraints that the DNS imposes. It then suggests an alternative, local translation, that may solve a superset of the problem while avoiding protocol changes, serious deployment delays, and other difficulties. The suggestion utilizes a localization technique in applications to permit any TLD to be accessed using the vocabulary and characters of any language. It is not restricted to language- or country-specific multilingual TLDs in the language(s) and script(s) of that country. This memo provides information for the Internet community.

Internet-based computing has lead to an emergence of a large number of threats. One of the major threat is DDoS (Distributed Denial of Service) attack. Recent incidents have shown that DDoS attacks have the capability of shutting a business not for a day but weeks. DDoS attacks have a greater impact on multi-tenant clouds than traditional infrastructure. DDoS attacks in the cloud, take the shape of EDoS (Economic denial of sustainability) attacks. In EDoS, instead of “Service Denial”, economic harms occur due to fake resource usage and subsequent addition or buying of resources using on-demand provisioning. To detect and mitigate DDoS attacks in the cloud, we argue that on-demand resource allocation (known as auto-scaling) should also be looked, in addition to network or application layer mitigation. We have proposed a novel mitigation strategy, DARAC, which makes auto-scaling decisions by accurately differentiating between legitimate requests and attacker traffic. Attacker traffic is detected and dropped based on human behavior analysis based detection. We also argue that most of the solutions in the literature, do not pay much attention to the service quality to legitimate requests during an attack. We calculate the share of legitimate clients in resource addition/buying and make subsequent accurate auto-scaling decisions. Experimental results show that DARAC mitigates various DDoS attack sets and take accurate and quick auto-scaling decisions for various legitimate and attacker traffic combinations saving from EDoS. We also show how proposed mechanism could make “arms-race” very difficult for the attackers as the resource need to defeat DARAC mechanism on a very small capacity server is huge. Results also show significant improvements in the average response time of the web-service under attack, in addition to infrastructure cost savings up to 50 % in heavy attack cases.KeywordsCloud ComputingVirtual MachineAttack ScenarioIdle ResourceCloud ConsumerThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

In an age where data security and encryption is the highest priority in any kind of data communication, domain name services (DNS) are still vulnerable to various kinds of threats and attacks since domain names are still communicated in plaintext. Most of the attacks take place in the Top Level Domains (TLDs) that controls the DNS system. Given below is a study of the current situation and the possible solutions.KeywordsDNSPrivacySecurityMaliciousBlacklistingRandom forest

The article presents a consideration of top-level domain names and possibility that telemedicine generates top-level domain name for medicine itself. Using the United States of America domain, the article has tried to explain all the most important facts about top-level domain names. It has also presents a current situation about responsibility in this sector and the mode to adopt new top-level domains.

Background & Objective: Detecting and mitigating Distributed Denial of Service (DDoS) attacks is a serious problem. In addition, new features and network deployments such as Software- Defined Networking (SDN) may open the door for new threats that did not previously exist. : Recent publications and patent are reviewed to find new techniques developed for integrating different mechanisms to secure networks against DDoS. Methods: This work presents a simple model for integrating different mechanisms to secure both SDN and legacy network in a hybrid cloud environment, it is called FocusON. It aims at mitigating DDoS attacks of a victim network. In addition, separating network monitoring from its control aims at mitigating DDoS attacks of a victim network. Traffic pattern analysis is apart from attack detection mechanism that gives a conceptual representation of a specific kind of DDoS attacks. DDoS detection is a completely automated process. Once called, for the reaction, the active response will be taken against the real IP source of the attacker. : The communication time overhead was tested in order to evaluate the remote server response time in case of deploying our proposed model mechanisms and without our proposed model. : Here we introduce a response mechanism that consists of an analysis of event logs, traffic patterns, and IP traceback. The proposed model categorizes the underlying network according to the location into a victim network and the source of attack (public cloud). Results & Conclusion:: The proposed model implemented in a hybrid cloud environment using the network of SDN and legacy network. The experimental setup was built using our network lab connected to the Amazon public cloud.

DNS cache plays a critical role in domain name resolution, providing (1) high scalability at Root and Top-level-domain (TLD) name servers with reduced workloads and (2) low response latency to clients when the resource records of the queried domains are cached. However, the pervasive misuses of domain names, e.g., the domains of ``one-time-use'' pattern, have negative impact on the effectiveness of DNS caching as the cache has been filled with those entries that are highly unlikely to be retrieved. In this paper, we investigate such misuse and identify domain name-based features to characterize those one-time domains. By leveraging the features that are explicitly available from the domain name itself, we build a classifier to combine these features, propose simple policy modifications on caching resolvers for improving DNS cache performance, and validate their efficacy using real traces.

CHAPTER 8 - Installing, Configuring as a Server

Network Time Protocol amplification attack is a form of distributed denial-of-service (DDoS) attack in which an attacker exploits or sends a request to a vulnerable NTP server by using their IP address to flood a targeted network or server with an overwhelming volume of User Datagram Protocol (UDP) traffic. In the past, the techniques that involved reflecting traffic off NTP servers to the victim, with the attacker hiding their identity by spoofing the source IP address were carried out using mainly Domain Name Server (DNS) servers but the use of vulnerable NTP servers as reflectors in DDoS attacks has gain lot of popularity since 2014, and this is as a result of the realization of high amplification scale that NTP servers can provide. This type of reflector attack maximized the use of the amplification factor of NTP servers to magnify the attack bandwidth, making it particularly disruptive and difficult to mitigate. Since NTP amplification is not a popularly known attack and there has not been much thorough research on it, this paper explores a holistic overview of NTP amplification attacks, how NTP is used for DDoS attacks, and the overall method that can be used to mitigate such attacks. Keywords: Distributed Denial-of-Service (DDoS) attack, DNS servers, NTP servers

Network Time Protocol amplification attack is a form of distributed denial-of-service (DDoS) attack in which an attacker exploits or sends a request to a vulnerable NTP server by using their IP address to flood a targeted network or server with an overwhelming volume of User Datagram Protocol (UDP) traffic. In the past, the techniques that involved reflecting traffic off NTP servers to the victim, with the attacker hiding their identity by spoofing the source IP address were carried out using mainly Domain Name Server (DNS) servers but the use of vulnerable NTP servers as reflectors in DDoS attacks has gain lot of popularity since 2014, and this is as a result of the realization of high amplification scale that NTP servers can provide. This type of reflector attack maximized the use of the amplification factor of NTP servers to magnify the attack bandwidth, making it particularly disruptive and difficult to mitigate. Since NTP amplification is not a popularly known attack and there has not been much thorough research on it, this paper explores a holistic overview of NTP amplification attacks, how NTP is used for DDoS attacks, and the overall method that can be used to mitigate such attacks. Keywords: Distributed Denial-of-Service (DDoS) attack, DNS servers, NTP servers

Detecting and mitigating DDoS attacks with moving target defense approach based on automated flow classification in SDN networks

This study focuses on the security of 5G mobile network major technology called Multi-Access Edge Computing (MEC) and its susceptibility to distributed denial of service (DDoS) attacks. The goal of the research is to address the effects of DDoS attacks and implement effective mitigation techniques. Several supervised Machine Learning (ML) techniques, which include Random Forest (RF), Decision Tree, Naïve Bayes, K-Nearest Neighbour, Logistics Regression, and Blending/Stack Model, are evaluated using multiple performance metrics such as accuracy, detection/recall, F1-Measure, Matthew’s correlation coefficient, Receiver Operating Characteristic, and Area Under Receiver Operating Characteristic. According to literature, ML algorithms achieve the best performance in mitigating DDoS attacks, therefore, they can be optimized to enhance their effectiveness. The research provides an overview of the existing mitigation schemes in the MEC and proposes a DDoS mitigation scheme. The findings show that hybrid models outperformed traditional ML models. Among the mitigation techniques evaluated, RF proved to be the most effective in mitigating DDoS attacks in MEC.

Fiber optic networks (FONs) are considered the backbone of telecom companies worldwide. However, the network elements of FONs are scattered over a wide area and managed through a centralized controller based on intelligent devices and the internet of things (IoT), with actuators used to perform specific tasks at remote locations. During the COVID-19 pandemic, many telecom companies advised their employees to manage the network using the public internet (e.g., working from home while connected to an IoT network). Theses IoT devices mostly have weak security algorithms that are easily taken-over by hackers, and therefore can generate Distributed Denial of Service (DDoS) attacks in FONs. A DDoS attack is one of the most severe cyberattack types, and can negatively affect the stability and quality of managing networks. Nowadays, software-defined networks (SDN) constitute a new approach that simplifies how the network can be managed through a centralized controller. Moreover, machine learning algorithms allow the detection of incoming malicious traffic with high accuracy. Therefore, combining SDN and ML approaches can lead to detecting and stopping DDoS attacks quickly and efficiently, especially compared to traditional methods. In this paper, we evaluated six ML models: Logistic Regression, K-Nearest Neighbor, Support Vector Machine, Naive Bayes, Decision Tree, and Random Forest. The accuracy reached 100% while detecting DDoS attacks in FON with two approaches: (1) using SVM with three features (SOS, SSIP, and RPF) and (2) using Random Forest with five features (SOS, SSIP, RPF, SDFP, and SDFB). The training time for the first approach was 14.3 s, whereas the second approach only requires 0.18 s; hence, the second approach was utilized for deployment.

The Internet of Things enabled Wireless Sensor Networks (IoT-enabled WSNs) are highly susceptible to a wide range of Distributed Denial of Service (DDoS) attacks. While several methods have been developed to identify and mitigate these attacks, creating a unified strategy to counter multi-layer DDoS attacks remains a challenge due to the diverse nature of threats presented at different layers of IoT communication networks. To address this, a new protocol called Multi-Layer DDoS threat Detection and Mitigation (MLDDM) has been proposed. MLDDM's core capability lies in its audit trail analysis of trusted networks. By continuously calculating trust parameters across various IoT layers, the trust-based network tree analysis method evaluates each sensor node within the network. This analysis is then used to detect and prevent DDoS attacks. The MLDDM is a comprehensive approach designed to detect and mitigate DDoS attacks across the application, transport, internet, and edge layers. The proposed multi-layer trust-based network tree method analyzes each node to determine whether it is a potential attacker, identifying DDoS threats and their root causes. Once a DDoS attack is detected, MLDDM disconnects the affected nodes from the IoT-enabled WSNs until the next scheduled check. Simulation results demonstrate that the MLDDM protocol outperforms existing solutions in terms of throughput, Packet Delivery Rate (PDR), energy efficiency, communication latency, and overhead.

Many modern botnet malwares use Domain Generation Algorithms (DGAs) to dynamically generate the domain names that resolve to their command and control (C2) centers. This approach allows these malwares to subvert traditional detection systems which rely on blacklists of known domains associated with malicious activities to block malware communications. Since the advent of DGA-based malwares, the efforts to prevent the said malwares from contacting their command and control centers (C2) server have been centered around detecting Algorithmically Generated Domain Names through lexicographic analysis, isolating entire infected devices or both. Recent research has emerged, which more accurately identifies infected devices in a network, by monitoring the volumes of domain resolution failures. While effective, these techniques are slow to identify DGA generated domain names. Even after the delayed identification, the only preliminary mitigation known today is a complete shutdown of a device that is suspected to be infected. In this paper, we present a new method to counter DGA-based malwares by limiting the impact of mitigation. Instead of isolating the entire infected device from the network we limit network activity of the malicious process alone. Our objective is to prevent DGA-based malwares from communicating with their C2 centers while allowing an infected device to maintain its normal functionality. We achieve this by tracking Domain Name Service (DNS) responses of individual processes and blacklisting those processes for which DNS traffic have abnormally large numbers of domain resolution failures. The blacklisting at a process level ensures that non-malicious processes in the infected device can continue functioning.

Distributed Denial of Service (DDoS) constitutes major threat to both traditional and SDN networks. An attacker can launch a DDoS attack to exhaust either the controller or other network resources, such as switches, or both. There are different DDoS attacks such as UDP flood, SYN flood, Ping of death, ICMP flood and HTTP flood. Among these, SYN and HTTP flood are the most common attacks these days. In this thesis, we focus on developing a security scheme to alleviate the DDoS attacks with spoofed and non-spoofed IP addresses in the SDN environment. First we use a simple detection mechanism that utilizes a time series window-based traffic statistic measurement to detect possible SYN flood and/or HTTP flood DDoS attacks. To reduce false positives, further investigation of traffic is done based on valid source IP address scheme and single flow packet scheme to separate legitimate traffic from attack traffic. Once the attack is detected, the security scheme deploys a number of mitigation methods to alleviate the attack. For the SYN flood attack, the mitigation method of Source IP address filtering is used to permit traffic only with valid source IP addresses to enter the network. For HTTP flood attack mitigation, a mitigation method is used to identify the attack sources and discard the traffic from those sources. We test our proposed scheme with other DDoS attacks such as ICMP flood attack and UDP flood attacks. We also compare our scheme with other security schemes found in the literature. The result shows that our proposed scheme can effectively protect controller and other network resources from some common DDoS attacks, and that our scheme allows more legitimate traffic connections with less false positives in comparison with other schemes.