Amplification Attacks Research Articles

DNS Amplification Attacks: Obsolete or Potent Threats?

DNS Amplification Attacks: Obsolete or Potent Threats?

A New Mitigation Method against DRDoS Attacks Using a Snort UDP Module in Low-Specification Fog Computing Environments

Current cloud computing expects to face huge traffic costs, data loads, and high latency due to the explosion of data from devices as the IoT and 5G technology evolve. Fog computing has emerged to overcome these issues. It deploys small fog servers at the edge of the network to process critical data in real time while sending the remaining secondary tasks to the central cloud, instead of sending massive amounts of data to the cloud. With the rise in fog computing, among traditional security threats, distributed denial-of-service (DDoS) attacks have become the major threat to availability. This is especially true for fog computing, where real-time processing is critical; there are many fog servers, and the processing power is relatively low. Distributed reflection denial-of-service (DRDoS), one of the frequently used DDoS attack techniques, is an amplification attack that can be used on a small or large scale. It is widely used in attack tools due to its easy configuration. This study analyzes the characteristics of fog computing, the characteristics of DRDoS attacks, and the advantages and disadvantages of existing countermeasures. Based on these analyses, this study proposes a model that could effectively mitigate attacks even on low-specification fog servers by combining a modified Snort module with reduced functionality, simple pattern matching, and filtering distribution using Anycast. This mitigation algorithm has a simple structure rather than a complex filtering structure. To achieve this goal, this study virtually implemented the corresponding fog IoT environment. In spite of its simple structure, it proved that the fog server could secure availability even under DRDoS attacks by implementing and validating the mitigation model.

An Architecture of Enhanced Profiling Assurance for IoT Networks

Attacks launched from IoT networks can cause significant damage to critical network systems and services. IoT networks may contain a large volume of devices. Protecting these devices from being abused to launch traffic amplification attacks is critical. The manufacturer usage description (MUD) architecture uses pre-defined stateless access control rules to allow or block specific network traffic without stateful communication inspection. This can lead to false negative filtering of malicious traffic, as the MUD architecture does not include the monitoring of communication states to determine which connections to allow through. This study presents a novel solution, the enhanced profiling assurance (EPA) architecture. It incorporates both stateless and stateful communication inspection, a unique approach that enhances the detection effectiveness of the MUD architecture. EPA contains layered intrusion detection and prevention systems to monitor stateful and stateless communication. It adopts three-way decision theory with three outcomes: allow, deny, and uncertain. Packets that are marked as uncertain must be continuously monitored to determine access permission. Our analysis, conducted with two network scenarios, demonstrates the superiority of the EPA over the MUD architecture in detecting malicious activities.

Mitigation of channel tampering attacks in continuous-variable quantum key distribution

Despite significant advancements in continuous-variable quantum key distribution (CV-QKD), practical CV-QKD systems can be compromised by various attacks. Consequently, identifying new attack vectors and countermeasures for CV-QKD implementations is important for the continued robustness of CV-QKD. In particular, as CV-QKD relies on a public quantum channel, vulnerability to communication disruption persists from potential adversaries employing denial-of-service (DoS) attacks. Inspired by DoS attacks, this paper introduces a threat in CV-QKD called the channel amplification (CA) attack, wherein Eve manipulates the communication channel through amplification. We specifically model this attack in a CV-QKD optical fiber setup. To counter this threat, we propose a detection and mitigation strategy. Detection involves a machine learning (ML) model based on a decision tree classifier, classifying various channel tampering attacks, including CA and DoS attacks. For mitigation, Bob, postselects quadrature data by classifying the attack type and frequency. Our ML model exhibits high accuracy in distinguishing and categorizing these attacks. The CA attack's impact on the secret key rate (SKR) is explored concerning Eve's location and the relative intensity noise of the local oscillator (LO). The proposed mitigation strategy improves the attacked SKR for CA attacks and, in some cases, for hybrid CA-DoS attacks. Our study marks an application of both ML classification and postselection in this context. These findings are important for enhancing the robustness of CV-QKD systems against emerging threats on the channel. Published by the American Physical Society 2024

Analyzing Network Time Protocol (NTP) Based Amplification DDoS Attack and its Mitigation Techniques

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

Analyzing Network Time Protocol (NTP) Based Amplification DDoS Attack and its Mitigation Techniques

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

Machine Learning-Based DoS Amplification Attack Detection against Constrained Application Protocol

This paper discusses the Internet of Things (IoT) and the security challenges associated with it. IoT is a network of interconnected devices that share information. However, the low power and resources of IoT devices make them vulnerable to attacks. Using heavy protocols like HTTP for IoT devices can prove costly and using popular lightweight protocols like CoAP can invite attacks such as DoS (Denial-of-Service). While security models such as DTLS and LSPWSN can secure IoT against such attacks, they also have limitations. To overcome this problem, this paper proposes a machine learning model that detects DoS amplification attacks against CoAP with 99% accuracy. To the best of our knowledge, this research is the first to use the multi-classification process to detect and classify the different types of the DoS amplification techniques that attack CoAP client use against victim CoAP clients.

Countering DNS Amplification Attacks Based on Analysis of Outgoing Traffic

Countering DNS Amplification Attacks Based on Analysis of Outgoing Traffic

Amplification/Reflection Attack Suppression Using Victim Separation

Amplification and reflection attacks represent flavors of DDoS attacks which use servers such as DNS, NTP, SNMP, SSDP and CoAP as amplifier to launch the attacks. In this letter, we describe a method to minimize the effects of reflection and amplification DDoS attacks by separating the victim service under the attack. We propose three approaches involving container level separation, packet limiting, and network hardware level separation. Our detailed experimental results on DNS based amplification attacks show a near-no-attack performance with an improvement of up to 88% in the test completion time while using the proposed approaches.

DNS Intrusion Detection (DID) — A SNORT-based solution to detect DNS Amplification and DNS Tunneling attacks

Domain Name System (DNS) plays a critical role in the Internet ecosystem, translating numerical IP addresses to memorable domain names and vice versa. The malicious user targets DNS by taking advantage of vulnerabilities in DNS. The most complex attacks in the DNS attacks vector include Distributed Denial of Service (DDoS) based DNS amplification attacks and sophisticated DNS tunneling attacks. An Intrusion Detection System (IDS) is a solution available to monitor the traffic for intrusion in the network but not exclusively for DNS intrusions. In this research paper, we present – DNS Intrusion Detection (DID), a system integrated into SNORT – a prominent open-source IDS, to detect major DNS-related attacks. We developed novel IDS signatures for various tools used in the tunneling, amplification, and DoS attacks and added them to the existing ruleset file of IDS to detect DNS-based intrusions. Our approach successfully identifies empirical DNS attacks carried out by various known tools available over the Internet. Evaluation of DID showed a high detection rate and a very low false-positive rate.

Automating Mitigation of Amplification Attacks in NFV Services

The combination of virtualization techniques with capillary computing and storage resources allows the instantiation of Virtual Network Functions throughout the network infrastructure, which brings more agility in the development and operation of network services. Beside forwarding and routing, this can be also used for additional functions, e.g., for security purposes. In this paper, we present a framework to systematically create security analytics for virtualized network services, specifically targeting the detection of cyber-attacks. Our framework largely automates the deployment of security sidecars into existing service templates and their interconnection to an external analytics platform. Notably, it leverages code augmentation techniques to dynamically inject and remove inspection probes without affecting service operation. We describe the implementation of a use case for the detection of DNS amplification attacks in virtualized 5G networks, and provide extensive evaluation of our innovative inspection and detection mechanisms. Our results demonstrate better efficiency with respect to existing network monitoring tools in terms of CPU usage, as well as good accuracy in detecting attacks even with variable traffic patterns.

RareGAN: Generating Samples for Rare Classes

We study the problem of learning generative adversarial networks (GANs) for a rare class of an unlabeled dataset subject to a labeling budget. This problem is motivated from practical applications in domains including security (e.g., synthesizing packets for DNS amplification attacks), systems and networking (e.g., synthesizing workloads that trigger high resource usage), and machine learning (e.g., generating images from a rare class). Existing approaches are unsuitable, either requiring fully-labeled datasets or sacrificing the fidelity of the rare class for that of the common classes. We propose RareGAN, a novel synthesis of three key ideas: (1) extending conditional GANs to use labelled and unlabelled data for better generalization; (2) an active learning approach that requests the most useful labels; and (3) a weighted loss function to favor learning the rare class. We show that RareGAN achieves a better fidelity-diversity tradeoff on the rare class than prior work across different applications, budgets, rare class fractions, GAN losses, and architectures.

Large-scale empirical evaluation of DNS and SSDP amplification attacks

Reflection-based volumetric distributed denial-of-service (DDoS) attacks take advantage of the available to all (open) services to flood and possibly overpower a victim’s server or network with an amplified amount of traffic. This work concentrates on two key protocols in the assailants’ quiver regarding DoS attacks, namely domain name system (DNS) and simple service discovery protocol (SSDP). Our contribution spans three axes: (a) We perform countrywide IP address scans (probes) across three countries in two continents to locate devices that run open DNS or SSDP services, and thus can be effectively exploited in the context of amplification attacks, (b) we fingerprint the discovered devices to derive information about their type and operating system, and (c) we estimate the amplification factor of the discovered reflectors through a dozen of diverse, suitably crafted DNS queries and a couple of SSDP ones depending on the case. The conducted scans span fifteen months, therefore comparative conclusions regarding the evolution of the reflectors population over time, as well as indirect ones regarding the security measures in this field, can be deduced. For instance, for DNS, it was calculated that the third quartile of the amplification factor distribution remains more than 30 for customarily exploited queries across all the examined countries, while in the worst case this figure can reach up to 70. The same figures for SSDP range between roughly 41 and 73 for a specific type of query. To our knowledge, this work offers the first full-fledged mapping and assessment of DNS and SSDP amplifiers, and it is therefore anticipated to serve as a basis for further research in this ever-changing and high-stakes network security field.

DDoS amplification attacks and impacts on enterprise service-oriented network infrastructures: DNS servers

DDoS amplification attacks and impacts on enterprise service-oriented network infrastructures: DNS servers

DDoS amplification attacks and impacts on enterprise service-oriented network infrastructures: DNS servers

DDoS amplification attacks and impacts on enterprise service-oriented network infrastructures: DNS servers

Memcached: An Experimental Study of DDoS Attacks for the Wellbeing of IoT Applications

Distributed denial-of-service (DDoS) attacks are significant threats to the cyber world because of their potential to quickly bring down victims. Memcached vulnerabilities have been targeted by attackers using DDoS amplification attacks. GitHub and Arbor Networks were the victims of Memcached DDoS attacks with 1.3 Tbps and 1.8 Tbps attack strengths, respectively. The bandwidth amplification factor of nearly 50,000 makes Memcached the deadliest DDoS attack vector to date. In recent times, fellow researchers have made specific efforts to analyze and evaluate Memcached vulnerabilities; however, the solutions provided for security are based on best practices by users and service providers. This study is the first attempt at modifying the architecture of Memcached servers in the context of improving security against DDoS attacks. This study discusses the Memcached protocol, the vulnerabilities associated with it, the future challenges for different IoT applications associated with caches, and the solutions for detecting Memcached DDoS attacks. The proposed solution is a novel identification-pattern mechanism using a threshold scheme for detecting volume-based DDoS attacks. In the undertaken study, the solution acts as a pre-emptive measure for detecting DDoS attacks while maintaining low latency and high throughput.

Response System for DRDoS Amplification Attacks

Response System for DRDoS Amplification Attacks

SUPnP: Secure Access and Service Registration for UPnP-Enabled Internet of Things

The service-oriented nature of the Universal Plug-and-Play (UPnP) protocol supports the creation of flexible, open, and dynamic systems. As such, it is widely used in Internet-of-Things (IoT) deployments. However, the protocol’s service access mechanism does not consider security from the first principles and is therefore vulnerable to various attacks. In this article, we present an in-depth analysis of the service advertisement, discovery, and access methods of the UPnP protocol stack and identify security issues in an IoT network. Our analysis shows that adversaries can perform resource exhaustion, buffer overflow, reflection, and amplification attacks by exploiting the vulnerabilities of the UPnP protocol. To address these issues, we propose a capability-based security model for UPnP to ensure secure discovery, advertisement, and access of the UPnP services that considers the resource limitations of IoT devices. Our analysis shows the effectiveness of the proposed model against potential attacks, and our experimental evaluation highlights the feasibility of implementing our Secure UPnP (SUPnP) protocol in a network of IoT devices, incurring minimal network and performance overhead.

Preventing DNS Misuse for Reflection / Amplification Attacks With Minimal Computational Overhead on the Internet

DNS reflection/amplification attacks are types of Distributed Denial of Service (DDoS) attacks that take advantage of vulnerabilities in the Domain Name System (DNS) and use it as an attacking tool. This type of attack can quickly deplete the resources (i.e. computational and bandwidth) of the targeted system. Many defense mechanisms are proposed to mitigate the impact of this type of attack. However, these defense mechanisms are centralized-based and cannot deal with a distributed-based attack. Also, these defense mechanisms have a single point of deployment which leads to a lack of computational resources to handle an attack with a large magnitude. In this work, we presented a new distributed-based defense mechanism (DDM) to counter reflection/ amplification attacks. While operating, we calculated the CPU counters of the machines that we deployed our defense mechanism with which showed 19.9% computational improvement. On top of that, our defense mechanism showed that it can protect the attack path from exhaustion during reflection/amplification attacks without putting any significant traffic load on the network by eliminating every spoofed request from getting responses.

NAT++: An Efficient Micro-NAT Architecture for Solving IP-Spoofing Attacks in a Corporate Network

The Internet Protocol (IP) version 4 (IPv4) has several known vulnerabilities. One of the important vulnerabilities is that the protocol does not validate the correctness of the source address carried in an IP packet. Users with malicious intentions may take advantage of this vulnerability and launch various attacks against a target host or a network. These attacks are popularly known as IP Address Spoofing attacks. One of the classical IP-spoofing attacks that cost several million dollars worldwide is the DNS-amplification attack. Currently, the availability of solutions is limited, proprietary, expensive, and requires expertise. The Internet is subjected to several other forms of amplification attacks happening every day. Even though IP-Spoofing is one of the well-researched areas since 2005, there is no holistic solution available to solve this problem from the gross-root. Also, every solution assumes that the attackers are always from outside networks. In this paper, we provide an efficient and scalable solution to solve the IP-Spoofing problem that arises from malicious or compromised inside hosts. We use a modified form of Network Address Translation (NAT) to build our solution framework. We call our framework as NAT++. The proposed infrastructure is robust, crypto-free, and easy to implement. Our simulation results have shown that the proposed NAT++ infrastructure does not consume more than the resources required by a simple NAT.