Distributed Denial Of Service Problem Research Articles

DDOS Attack Detection

A Distributed Denial of Service (DDoS) attack is an attempt to make a service unavailable by overwhelming the server with malicious traffic. DDoS attacks have become the most tedious and cumbersome issue in recent past. The number and magnitude of attacks have increased from few megabytes of data to 100s of terabytes of data these days. Due to the differences in the attack patterns or new types of attack, it is hard to detect these attacks effectively. In this paper, we devise new techniques for causing DDoS attacks and mitigation which are clearly shown to perform much better than the existing techniques. We also categorize DDoS attack techniques as well as the techniques used in their detection and thus attempt an extensive scoping of the DDoS problem. We also compare our attack module with a couple of tools available

DDoS – does size matter?

We see frequent headlines about distributed denial of service (DDoS) of ever greater volumes. But does this reflect a true image of the DDoS problem? We can look at recent history to gain some insights into this obsession with attack size.

Detection of DDoS Attacks using Concepts of Machine Learning

Abstract: Distributed Denial-of-Service (DDoS) assaults are the terrorizing preliminaries on the Internet that exhaust the organization transmission capacity. Analysts have presented different safeguard components including assault counteraction, traceback, response, identification, and portrayal against DDoS assaults, however the quantity of these assaults builds consistently, and the ideal answers for this issue have escaped us up to this point. An order of identification approaches against DDoS assaults is given the point of giving profound understanding into the DDoS problem. Although the anticipation of Distributed Denial of Service (DDoS) assaults is preposterous, location of such goes after assumes principal part in forestalling their advancement. In the flooding assaults, particularly new modern DDoS, the assailant floods the organization traffic toward the objective PC by sending pseudo-ordinary parcels. Hence, multi-reason IDSs don't offer a decent execution (and precision) in distinguishing such sorts of assaults. Keywords: Denial-of-Service (DoS), Distributed Denial-of-Service (DDoS), Machine Learning, attacks, attackers, system, cyber, bots.

A New Smart Router-Throttling Method to Mitigate DDoS Attacks

The distributed denial of service (DDoS) attack is one of the most server threats to the current Internet and brings huge losses to society. Furthermore, it is challenging to defend DDoS due to the case that the DDoS traffic can appear similar to the legitimate ones. Router throttling is an accessible approach to defend DDoS attacks. Some existing router throttling methods dynamically adjust a given threshold value to keep the server load safe. However, these methods are not ideal as they exploit the information of the current time, so the perception of time series variations is poor. The DDoS problem can be seen as a Markov decision process (MDP). Multi-agent router throttling (MART) method based on hierarchical communication mechanism has been proposed to address this problem. However, each agent is independent with each other and has no communication among them, therefore, it is hard for them to collaborate to learn an ideal policy to defend DDoS. To solve this multi-agent partially observable MDP problem, we propose a centralized reinforcement learning router throttling method based on a centralized communication mechanism. Each router sends its own traffic reading to a central router, the central router then makes a decision for each router to choose the throttling rate. We also simulate the environment of the DDoS problem more realistic while modify the reward function of the MART to make the reward function of more coherent. To decrease the communication costs, we add a deep deterministic policy gradient network for each router to decide whether or not to send information to the central agent. The experiments validate that our proposed new smart router throttling method outperforms existing methods to the DDoS instruction response.

D-FACE: An anomaly based distributed approach for early detection of DDoS attacks and flash events

In the present computer era, though the Internet-based applications are the driving force of social evolution, yet its architectural vulnerabilities proffer plethoric leisure to the attackers for conquering diversity of attacks on its services. Distributed Denial of Service (DDoS) is one of such prominent attack that constitutes a lethal threat to Internet domain that harnesses its computing and communication resources. Despite the presence of enormous defense solutions, ensuring the security and availability of data, resources, and services to end users remains an ongoing research challenge. In addition, the increase in network traffic rates of legitimate traffic and flow similarity of attack traffic with legitimate traffic has further made DDoS problem more crucial. The current research has deployed DDoS defense solutions primarily at the victim-end because of the inherent advantages of easy deployment and availability of complete attack information. However, the huge network traffic volume generated by DDoS attacks and lack of sufficient computational resources at the victim-end makes defense solution itself vulnerable to these attacks. This paper proposes an ISP level distributed, flexible, automated, and collaborative (D-FACE) defense system which not only distributes the computational and storage complexity to the nearest point of presence (PoPs) routers but also leads to an early detection of DDoS attacks and flash events (FEs). The results show that D-FACE defense system outperformed the existing Entropy-based systems on various defense system evaluation metrics.

Distributed Defense: An Edge over Centralized Defense against DDos Attacks

Distributed Denial of Service (DDoS) attack is a large-scale, coordinated attack on the availability of services of a target/victim system or network resource/service. It can be launched indirectly through many compromised machines on the Internet. The Purpose behind these attacks is exhausting the existing bandwidth and makes servers deny from providing services to legitimate users. Most detection systems depend on some type of centralized processing to analyze the data necessary to detect an attack. In centralized defense, all modules are placed on single point. A centralized approach can be vulnerable to attack. But in distributed defense, all of the defense modules are placed at different points and do not succumb to the high volume of DDoS attack and can discover the attacks timely as well as fight the attacks with more resources. These factors clearly indicate that the DDoS problem requires a distributed solution than the centralized solution. In this paper, we compare both types of defense mechanisms and identify their relative advantages and disadvantages. Later they are compared against some performance metrics to know which kind of solution is best.

Distributed reinforcement learning for adaptive and robust network intrusion response

Distributed denial of service (DDoS) attacks constitute a rapidly evolving threat in the current Internet. Multiagent Router Throttling is a novel approach to defend against DDoS attacks where multiple reinforcement learning agents are installed on a set of routers and learn to rate-limit or throttle traffic towards a victim server. The focus of this paper is on online learning and scalability. We propose an approach that incorporates task decomposition, team rewards and a form of reward shaping called difference rewards. One of the novel characteristics of the proposed system is that it provides a decentralised coordinated response to the DDoS problem, thus being resilient to DDoS attacks themselves. The proposed system learns remarkably fast, thus being suitable for online learning. Furthermore, its scalability is successfully demonstrated in experiments involving 1000 learning agents. We compare our approach against a baseline and a popular state-of-the-art throttling technique from the network security literature and show that the proposed approach is more effective, adaptive to sophisticated attack rate dynamics and robust to agent failures.

Distributed response to network intrusions using multiagent reinforcement learning

Distributed denial of service (DDoS) attacks constitute a rapidly evolving threat in the current Internet. Multiagent Router Throttling is a novel approach to defend against DDoS attacks where multiple reinforcement learning agents are installed on a set of routers and learn to throttle or rate-limit traffic towards a victim server. It has been demonstrated to perform well against DDoS attacks in small-scale network topologies. The focus of this paper is to tackle the scalability challenge. Scalability is one of the most important aspects of a defence system since a non-scalable defence mechanism will never be considered, let alone adopted, for wide deployment by a company or organisation. In this paper we introduce Coordinated Team Learning (CTL) which is a novel design to the original Multiagent Router Throttling approach. One of the novel characteristics of our approach is that it provides a decentralised coordinated response to the DDoS problem. It incorporates several mechanisms, namely, hierarchical team-based communication, task decomposition and team rewards and its scalability is successfully demonstrated in experiments involving up to 100 reinforcement learning agents. We compare our proposed approach against a baseline and a popular state-of-the-art router throttling technique from the network security literature and we show that our approach significantly outperforms both of them in a series of scenarios with increasingly sophisticated attack dynamics. Furthermore, we show that our approach is more resilient and adaptable than the existing throttling approaches.

A Defense Framework against DDoS in a Multipath Network Environment

The Internet is facing a major threat, consisting of a disruption to services caused by distributed denial-of-service (DDoS) attacks. This kind of attacks continues to evolve over the past two decades and they are well known to significantly affect companies and businesses. DDoS is a popular choice among attackers community. Such attack can easily exhaust the computing and communication resources of its victim within a short period of time. Many approaches to countering DDoS attacks have been proposed, but few have addressed the use of multipath. In this paper, we analyze, how multipath routing based solutions could be used to address the DDoS problem. The proposed framework traces back the attack to its source and blocks it. It also calculates multiple paths to the attacker (if they exist) and alerts all gateways near the attacker to block possible traffic originating from this source in case another path(s) is (are) later used to attack the victim again. We demonstrate that our scheme performs better that other single path schemes.

A SELECTIVE SURVEY OF DISTRIBUTED DENIAL-OF-SERVICE (DDOS) ATTACKS AND DEFENSE MECHANISMS

Distributed Denial of Service (DDoS) attacks, one of the latest and most powerful threats that have appeared to the Internet can disrupt the availability of Internet services completely, by eating either computational or communication resources through sheer volume of packets sent from distributed locations in a coordinated manner or graceful degradation of network performance by sending attack traffic at low rate. An overview of DDoS problem, Vulnerabilities in Internet Architecture, Attack: Modus Operandi, different types of DDoS attacks, recent defense mechanisms and their effectiveness are presented. This provides better understanding of the problem, current solution space and future to defend against DDoS attacks.

DDoS attacks and defense mechanisms: classification and state-of-the-art

Denial of Service (DoS) attacks constitute one of the major threats and among the hardest security problems in today’s Internet. Of particular concern are Distributed Denial of Service (DDoS) attacks, whose impact can be proportionally severe. With little or no advance warning, a DDoS attack can easily exhaust the computing and communication resources of its victim within a short period of time. Because of the seriousness of the problem many defense mechanisms have been proposed to combat these attacks. This paper presents a structural approach to the DDoS problem by developing a classification of DDoS attacks and DDoS defense mechanisms. Furthermore, important features of each attack and defense system category are described and advantages and disadvantages of each proposed scheme are outlined. The goal of the paper is to place some order into the existing attack and defense mechanisms, so that a better understanding of DDoS attacks can be achieved and subsequently more efficient and effective algorithms, techniques and procedures to combat these attacks may be developed.