Entropy Based Approach to Prevent the DDoS Attacks for Secured Web Services

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

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.

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

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.

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.

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.

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.

Nowadays, the most distinct threat to the web applications is HTTP-based Denial of Service (DoS) and Distributed Denial of Service (DDoS) attack, which it causes a massive damage to them and corrupt their works completely. Up to now, various researches are accomplished in this field for the sake of identifying and determining the most convenientrequired specifications for a protective solution that helps to protect web applications from HTTP-based DoS and DDoS attacks. Unfortunately, despite of that,the optimal specifications of the protective solution have not been identified, and therefore this paper comes out to state and identify these missing specifications. In this paper, a comprehensive survey to all sorts of HTTP-based DoS and DDoS attacks is conducted in order to figure out and identify the optimal specifications for a protective framework against HTTP-based DoS and DDoS attacks.

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.

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.

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.

Currently, the internet serves as the predominant means of communication and is utilized by a vast number of individuals worldwide. Simultaneously, the commercial aspect of the internet is contributing to a rise in susceptibility to cybercrimes, leading to a significant surge in the occurrence of distributed Denial of Service (DDoS) assaults over the last decade. DoS/DDoS assaults primarily target network resources such as network bandwidth, CPU time, memory consumption, web servers, and network switches. Network security is an essential and crucial problem in the modern interconnected society. Numerous studies have been undertaken by multiple researchers thus far in order to identify this attack. However, there is still room for improvement in past investigations. This paper presents a novel approach for detecting and simulating DoS/DDoS attacks in modern networking environments, introducing a new paradigm. It is done in a controlled environment. The primary focus of this work is to simulate an attacker's perspective of a DoS/DDoS attack by repeatedly sending huge SYN flood packets to a specific target or network server using the hping3 tool. On the server side, the proposed attacker detector script continuously monitors incoming network connections on the network server using the netstat command. It identifies potential DoS/DDoS attacks by analyzing the connection count and comparing connections count with an assumed threshold. This experiment results in 61% CPU usage and 7.1% memory consumption while a DDoS attack triggers on the target server. Additionally, the proposed script performs statistical analysis and displays warning messages on the console when suspicious activity is detected on the network server. Wireshark is also utilized in this work to detect anomalous network traffic patterns in order to identify distributed denial-ofservice (DDoS) attacks that are targeting a network server. Additionally, it offers the capability to block the IP address of the attacker if the configuration allows for it. This proposed approach efficiently identifies DDoS activity in real-time network traffic, further helping to improve network security.

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.

Distributed denial of service (DDoS) attacks pose a significant threat to the smooth operations of today's online critical services and applications. Existing mechanisms to detect these attacks have had limited success. With the rapid growth in size and bandwidth of contemporary computer networks, an efficient and effective distributed solution is needed for detecting DDoS attacks. In this study, the authors propose a multiagent pattern recognition mechanism for detecting DDoS attacks, in adistributed fashion. Our proposed solution is very effective in detecting such attacks launched against victim servers residing inside a production network which has multiple gateways to the Internet. Using simulation, the authors show that our proposed mechanism achieves a high degree of accuracy in detecting DDoS attacks, with low false alarm rates, using a reasonable numbers of attack detection agents collaboratively operating in a typical production network. The authors also study the relationship of the number of agents participating in the attack detection process and the false alarm rate of the detection scheme.

Denial of service (DoS) attacks pose one of the most challenging security issues in computer networks. We propose a defense strategy against DoS attacks, which is based on a local detection component and a feedback control component. The former uses queue content information to detect attacks, and the latter controls the sending rate of upstream nodes. We include simulation results to illustrate the behavior of a network when using this strategy under both single-source and distributed DoS attacks, and to show its effectiveness in detecting potential attacks at an early stage, identifying attacking flows, and reducing the damage caused by such attacks. Finally, we identify performance metrics appropriate for optimizing the defense mechanism.