Optimal specifications for a protective framework against HTTP-based DoS and DDoS attacks

Denial of Service (DoS) or Distributed Denial of Service (DDoS) is a powerful attack which prevents the system from providing services to its legitimate users. Several approaches exist to filter network-level attacks, but application-level attacks are harder to detect at the firewall. Filtering at application level can be computationally expensive and difficult to scale, while still creating bogus positives that block legitimate users. In this paper, authors show application layer DoS attack for SIP server using some open source DoS attack tools and also suggest a mechanism that can protect a given SIP server from application-level DoS attacks especially the attacks targeting the resources including CPU, sockets, memory of the victim server. In this paper author’s attempt to illustrate application layer distributed denial of Service (DDoS) attack on SIP Server such as SIP flooding attack, real time transport (RTP) flooding attack using open source DDoS attack tools. We propose a new DDoS defence mechanism that protects SIP servers from application-level DDoS attacks based on the two methodologies: IPtables and fail2ban detection. The attack flow detection mechanism detects attach flows based on the symptom or stress at the server, since it is getting more difficult to identify bad flows only based on the incoming traffic patterns. A popular software known as Wireshark which is a network protocol analyzer is used to capture the packets during DoS attack from the victim server Ethernet interface to detect the attacking host IP address and analysis the types of attack. We evaluate the performance of the proposed scheme via experiment

Software-defined networking (SDN) has revolutionized traditional network management by decoupling the control and data planes, offering enhanced flexibility, programmability, and efficiency. However, this centralized architecture makes SDN vulnerable to Denial of Service (DoS) and Distributed Denial of Service (DDoS) attacks, which severely impact network performance and reliability. Through overloading critical network components, especially the SDN controller, these attacks reduce network availability and result in significant service interruptions. This review offers a thorough summary of mitigation strategies designed to combat DoS and DDoS attacks in SDN systems. These attacks overwhelm network resources, disrupt services, and compromise the availability of critical components such as the SDN controller. Additionally, it explores the key challenges facing SDN in defending against DoS and DDoS attacks, such as the need for real-time detection, the impact of network scale, and the complexities of adaptive responses. As such, addressing DoS and DDoS attacks has become a critical research area for ensuring the security and robustness of SDN-based networks. It categorizes existing approaches based on detection methods, response strategies, and deployment models, providing a detailed analysis of their strengths and limitations. Furthermore, it discusses problems such as scalability, concurrent detection, and adaptability that hinder the effectiveness of current solutions. Emerging trends, including integrating artificial intelligence, blockchain, and collaborative frameworks, are highlighted as promising directions for future research. This aim is to give researchers, network admins, and cyber-security practitioners an insight into the state of the art as well as a basis on which to build for future SDN security advancements. This survey aims to serve as a comprehensive resource for researchers and practitioners seeking to imporve SDN's resilience against DoS and DDoS attacks.

Software Defined Networking (SDN) is a paradigm for the networks, where the control planes and data planes are separated. It provides centralized network control by separating the network’s control logic from the underlying hardware devices. However, like traditional networks SDN is also susceptible to Denial of Service (DoS) and Distributed Denial of Service (DDoS) attacks. This paper aims to detect and mitigate DoS and DDoS attacks in an SDN environment using an entropy-based approach. The proposed mechanism calculates the entropy of the network over the collected traffic, and derives a dynamic threshold according to the network traffic conditions to determine whether the environment is subject to DoS or DDoS attacks. In the event of the attack, the proposed mechanism installs a drop flow rule into underlying forwarding devices, discarding the traffic sent from attacking host to victim host

The presence of either malicious vehicles or inaccessibility of network services makes vehicular ad-hoc networks (VANETs) easy targets for denial of service (DoS) attacks. The sole purpose of DoS attacks is to prevent the intended users from accessing the available resources and services. When the DoS attack is carried out by multiple vehicles distributed throughout the network, it is referred as distributed DoS (DDoS) attack. A lot of works have been done by researchers in securing the vehicular communication against the DDoS attacks. In this paper, efforts have been made to simulate the DDoS attacks in VANETs as well as to study their impact on the performance of networks. The experimental results are presented using six important metrics, which are collision, jitter, delay, packet drop, ratio-in-out, and throughput. The experimental results show that the impact of distributed DoS (DDoS) attack on networks performance is very critical and must be addressed to ensure the smooth functioning of networks.

This chapter focuses on the very serious problem of denial of service (DoS) attacks against computer systems and networks. DoS attacks are attacks designed to disrupt, overwhelm, and/or damage computing resources and data. These attacks are aimed not against confidentiality of data or integrity of systems and data, but rather against availability of systems and data. Attackers launch DoS attacks for a wide variety of reasons. The probability of success is high, due in large part to the fact that the Internet itself has not been designed for security. The cost of DoS attacks is often high—several well‐known attacks have reportedly cost organizations billions of dollars. Many types of DoS attacks have surfaced so far, including resource starvation attacks, buffer overflow attacks, and packet fragmentation attacks. Distributed denial of service (DDoS) attacks, attacks in which programs planted in multiple systems cooperate, represent an even higher level of threat. Preventing DoS attacks is difficult in the first place due to a plethora of reasons; limiting the potential for damage and disruption is often the most cost‐effective strategy. Hot sites, cold sites, use of uninterruptible power supplies (UPSs) and Redundant Array of Independent Drives (RAID) solutions, and other measures can limit the damage from DoS attacks. Using properly configured and maintained firewalls is one of the best single prevention measures. Additionally, using packet filters on systems, keeping up with patches, limiting services that run on these systems, and using other measures go a long way in helping prevent susceptibility to DoS attacks.

The Internet of Things (IoT) has gained remarkable acceptance from millions of individuals. This is evident in the extensive use of intelligent devices such as smartphones, smart television, speakers, air conditioning, lighting, and high-speed networks. The general application area of IoT includes industries, hospitals, schools, homes, sports, oil and gas, automobile, and entertainment, to mention a few. However, because of the unbounded connection of IoT devices and the lack of a specific method for overseeing communication, security concerns such as distributed denial of service (DDoS), denial of service (DoS), replay, botnet, social engineering, man-in-the-middle, and brute force attacks have posed enormous challenges in the IoT environment. Regarding these enormous challenges, this study focuses on DDoS and DoS attacks. These two attacks have the most severe consequences in the IoT environment. The solution proposed in this study can also help future researchers tackle the expansion of IoT security threats. Moreover, the study conducts rigorous experiments to assess the efficiency of the proposed approach. In summary, the experimental results show that the proposed hybrid approach mitigates data exfiltration caused by DDoS and DoS attacks by 95.4%, with average network lifetime, energy consumption, and throughput improvements of 15%, 25%, and 60%, respectively.

Denial of Service (DoS) and Distributed Denial of service (DDoS) attacks are common place in today's computer networks. There are different types of attacks among which Flood based attacks are the major ones. Attacks detection mechanisms usually rely on statistical information of the traffic. As regards of the fact that statistical properties of DoS and DDoS attacks are very similar to those in legitimate traffics, in this paper we characterize the frequency domain of denial of service attacks instead of time domain. We consider the number of packets arriving to the node of victim as a random process which is acquired by sampling the packets number every 1 milli-second. Having created the process we find the normalized spectrum of the sampled data. The results show that the main energy of DoS and DDoS attacks are distributed in high and low frequencies respectively while energy is spread evenly through all range of frequencies in Normal TCP traffic.

With the increasing number of Internet users, cybersecurity is becoming more and more critical. Denial of service (DoS) and distributed denial of service (DDoS) attacks are two of the most common types of attacks that can severely affect a website or a server and make them unavailable to other users. The number of DDoS attacks increased by 55% between the period January 2020 and March 2021. Some approaches for detecting the DoS and DDoS attacks employing different machine learning and deep learning techniques are reported in the literature. Recently, it is also observed that the attackers have started leveraging state-of-the-art AI tools such as generative models for generating synthetic attacks which fool the standard detectors. No concrete approach is reported for developing and training the models which are not only robust in the detection of standard DDoS attacks but which can also detect adversarial attacks which are created synthetically by the attackers with harmful intentions. To that end, in this work, we employ a generative adversarial network (GAN) to develop such a robust detector. The proposed framework can generate and classify the synthetic benign (normal) and malignant (DDoS) instances which are very similar to the corresponding real instances as evaluated by similarity scores. The GAN-based model also demonstrates how effectively the malicious actors can generate adversarial DDoS network traffic instances which look like normal instances using feature modification which are very difficult for the classifier to detect. An approach on how to make the classifiers robust enough to detect such kinds of deliberate adversarial attacks via modifying some specific attack features manually is also proposed. This work provides the first step towards developing a generic and robust detector for DDoS attacks originating from various sources.

Cloud Computing is one of the most nurtured as well as debated topic in today’s world. Billions of data of various fields ranging from personal users to large business enterprises reside in Cloud. Therefore, availability of this huge amount of data and services is of immense importance. The DOS (Denial of Service) attack is a well-known threat to the availability of data in a smaller premise. Whenever, it’s a Cloud environment this simple DOS attack takes the form of DDOS (Distributed Denial of Service) attack. This paper provides a generic insight into the various kinds of DOS as well as DDOS attacks. Moreover, a handful of countermeasures have also been depicted here. In a nutshell, it aims at raising an awareness by outlining a clear picture of the Cloud availability issues.Our paper gives a comparative study of different techniques of detecting DOS.

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.

The increasing use of Internet of Things (IoT) devices has led to a rise in Distributed Denial of Service (DDoS) and Denial of Service (DoS) attacks on these networks. These attacks can have severe consequences, resulting in the unavailability of critical services and financial losses. In this paper, we propose an Intrusion Detection System (IDS) based on a Conditional Tabular Generative Adversarial Network (CTGAN) for detecting DDoS and DoS attacks on IoT networks. Our CGAN-based IDS utilizes a generator network to produce synthetic traffic that mimics legitimate traffic patterns, while the discriminator network learns to differentiate between legitimate and malicious traffic. The syntactic tabular data generated by CTGAN is employed to train multiple shallow machine-learning and deep-learning classifiers, enhancing their detection model performance. The proposed approach is evaluated using the Bot-IoT dataset, measuring detection accuracy, precision, recall, and F1 measure. Our experimental results demonstrate the accurate detection of DDoS and DoS attacks on IoT networks using the proposed approach. Furthermore, the results highlight the significant contribution of CTGAN in improving the performance of detection models in machine learning and deep learning classifiers.

DDoS (Distributed Denial of Service) attacks consist of a large number of compromised computer systems that launch joint attacks at a targeted victim, such as a server, website, or other network equipment, simultaneously. DDoS has become a widespread and severe threat to the integrity of computer networks. DDoS can lead to system paralysis, making it difficult to troubleshoot. As a key building block in the development of an integrated defense system, it is essential to detect DDoS attacks as early as possible. With the popularization of artificial intelligence, more and more researchers apply machine learning (ML) and deep learning (DL) to the detection of DDoS attacks and have achieved satisfactory accomplishments. The complexity and sophistication of DDoS attacks have kept increasing and evolving since the first DDoS attack was reported in 1996. Regarding the headways in this problem, a new type of DDoS attack, named adversarial DDoS attack, is investigated in this study. The generating adversarial DDoS traffic is carried out using a symmetric Generative Adversarial Networks (GAN) architecture called Cycle-GAN to demonstrate the severe impact of adversarial DDoS attacks. Experiment results reveal that the synthesized traffic can easily penetrate ML-based detection systems, including Random Forest, KNN (k-Nearest Neighbor), SVM (Support Vector Machine), and Naïve Bayes. These alarming results intimate the urgent need for countermeasures against adversary DDoS attacks.

The technology of today is dramatically more advanced than it was 30 years ago. With fiber optic cables becoming ever more present and the rapid expansion of storage, the volume of data passing through these devices is also becoming more abundant. Over time, it has become easier to launch many kinds of attacks. These attacks are often launched from one computer sending huge amounts of spoofed data for several minutes. It is no surprise then that multiple computers launching an attack will create a dire situation regardless of which PC or server is being attacked. A Denial of Service (DoS) attack occurs when an attacker floods a computer or server with illegitimate data so that the system will deny service to a legitimate user [3]. This occurs because that system is now lacking the resources to handle any more data. A Distributed DoS (DDoS) attack is the same idea, but amplified. A DDoS attack involves multiple systems striking a single target and flooding that target with data, overloading the target's computers, servers, and firewalls [3]. This often causes the physical devices to exhaust and deny legitimate requests. As if that wasn't enough, the attacker can plant a virus or Trojan in the system once they gain access to further sabotage the target. These types of attacks are very damaging to companies all around the world. That is where the Cisco ASA 5500 series firewall comes in. In this paper, we will be examining how efficiently a firewall of this type can defend against a DoS attack and a DDoS attack, and what adverse effects are displayed after the attacks have occurred.

a serious nuisance for network security could be a Distributed Denial of Service (DDoS) attack. The advent of the technological era has also brought along the threat of DDoS attacks for a variety of services and applications that use the Internet. Firms can incur huge financial losses even if there is a disruption in services for a fraction of a period. Analogous to a DDoS attack is the Flash Crowd (FC), in which a particular service is assessed by many legitimate users concurrently, which results in the denial of service. Overloading of network resources is a common issue associated with both these events, which impact CPU, available bandwidth, and memory for legitimate users, thereby leading to limited accessibility. Many researchers have put effort to differentiate between these two kinds of traffic, but none could come up with an effective solution. In this study, we propose an adaptive agent-based model, known as an adaptive application layer flooding protection (AALFP) model, for DDoS and FC flooding attacks. The AALFP model is aimed to protect the network application layer (NAL) against such attacks. Importantly, the model distinguishes and separates normal from abnormal traffic, and then blocks illegitimate traffic. Note that, to operate two traffic filters, the agent relies on the following three parameters of normal traffic intensity, traffic attack behaviour, and IP address history log. An example scenario is provided to simulate the model's application.

Denial of service attacks, viruses and worms are com- mon tools for malicious adversarial behaviour in networks. In this paper we propose the use of our autonomic routing protocol, the Cognitive Packet Network (CPN), as a means to defend nodes from Distributed Denial of Service Attacks (DDoS), where one or more attackers generate flooding traffic from multiple sources towards selected nodes or IP addresses. We use both analytical and simulation mod- elling, and experiments on our CPN testbed, to evaluate the advantages and disadvantages of our approach in the pres- ence of imperfect detection of DDoS attacks, and of false alarms.