Abstract: Distributed Denial of Service (DDoS) attacks are an ongoing problem in today's Internet, where packets from a large number of compromised hosts thwart the paths to the victim site and/or overload the victim machines. In a newly proposed future Internet architecture, Named Data Networking (NDN), end users request desired data by sending Interest packets, and the network delivers Data packets upon request only, effectively eliminating many existing DDoS attacks. However, an NDN network can be subject to a new type of DDoS attack, namely Interest packet flooding. In this paper we investigate effective solutions to mitigate Interest flooding. We show that NDN's inherent properties of storing per packet state on each router and maintaining flow balance (i.e., one Interest packet retrieves at most one Data packet) provides the basis for effective DDoS mitigation algorithms. Our evaluation through simulations shows that the solution can quickly and effectively respond and mitigate Interest flooding.

Abstract: Distributed Denial of Service (DDoS) attacks still pose a significant threat to critical infrastructure and Internet services alike. In this paper, we propose MOTAG, a moving target defense mechanism that secures service access for authenticated clients against flooding DDoS attacks. MOTAG employs a group of dynamic packet indirection proxies to relay data traffic between legitimate clients and the protected servers. Our design can effectively inhibit external attackers' attempts to directly bombard the network infrastructure. As a result, attackers will have to collude with malicious insiders in locating secret proxies and then initiating attacks. However, MOTAG can isolate insider attacks from innocent clients by continuously "moving" secret proxies to new network locations while shuffling client-to-proxy assignments. We develop a greedy shuffling algorithm to minimize the number of proxy re- allocations (shuffles) while maximizing attack isolation. Simulations are used to investigate MOTAG's effectiveness on protecting services of different scales against intensified DDoS attacks.

Abstract: Safety and reliability are important in the cloud computing environment. This is especially true today as distributed denial-of-service (DDoS) attacks constitute one of the largest threats faced by Internet users and cloud computing services. DDoS attacks target the resources of these services, lowering their ability to provide optimum usage of the network infrastructure. Due to the nature of cloud computing, the methodologies for preventing or stopping DDoS attacks are quite different compared to those used in traditional networks. In this paper, we investigate the effect of DDoS attacks on cloud resources and recommend practical defense mechanisms against different types of DDoS attacks in the cloud environment.

Abstract: This brief provides readers a complete and self-contained resource for information about DDoS attacks and how to defend against them. It presents the latest developments in this increasingly crucial field along with background context and survey material. The book also supplies an overview of DDoS attack issues, DDoS attack detection methods, DDoS attack source traceback, and details on how hackers organize DDoS attacks. The author concludes with future directions of the field, including the impact of DDoS attacks on cloud computing and cloud technology. The concise yet comprehensive nature of this brief makes it an ideal reference for researchers and professionals studying DDoS attacks. It is also a useful resource for graduate students interested in cyberterrorism and networking.

Abstract: Distributed denial of service (DDoS) attacks continues to grow as a threat to organizations worldwide. From the first known attack in 1999 to the highly publicized Operation Ababil, the DDoS attacks have a history of flooding the victim network with an enormous number of packets, hence exhausting the resources and preventing the legitimate users to access them. After having standard DDoS defense mechanism, still attackers are able to launch an attack. These inadequate defense mechanisms need to be improved and integrated with other solutions. The purpose of this paper is to study the characteristics of DDoS attacks, various models involved in attacks and to provide a timeline of defense mechanism with their improvements to combat DDoS attacks. In addition to this, a novel scheme is proposed to detect DDoS attack efficiently by using MapReduce programming model.

Abstract: Distributed Denial of Service (DDoS) attacks in the networks needs to be prevented or handled if it occurs, as early as possible and before reaching the victim. Dealing with DDoS attacks is difficult due to their properties such as dynamic attack rates, various kinds of targets, big scale of botnet, etc. Distributed Denial of Service (DDoS) attack is hard to deal with because it is difficult to distinguish legitimate traffic from malicious traffic, especially when the traffic is coming at a different rate from distributed sources. DDoS attack becomes more difficult to handle if it occurs in wireless network because of the properties of ad hoc network such as dynamic topologies, low battery life, multicast routing, frequency of updates or network overhead, scalability, mobile agent based routing, and power aware routing, etc. Therefore, it is better to prevent the distributed denial of service attack rather than allowing it to occur and then taking the necessary steps to handle it. This paper discusses various the attack mechanisms and problems due to DDoS attack, also how MANET can be affected by these attacks. In addition to this, a novel solution is proposed to handle DDoS attacks in mobile ad hoc networks (MANETs).

Abstract: DDoS attacks are one of the major threats to Internet services. Sophisticated hackers are mimicking the features of legitimate network events, such as flash crowds, to fly under the radar. This poses great challenges to detect DDoS attacks. In this paper, we propose an attack feature independent DDoS flooding attack detection method at local area networks. We employ flow entropy on local area network routers to supervise the network traffic and raise potential DDoS flooding attack alarms when the flow entropy drops significantly in a short period of time. Furthermore, information distance is employed to differentiate DDoS attacks from flash crowds. In general, the attack traffic of one DDoS flooding attack session is generated by many bots from one botnet, and all of these bots are executing the same attack program. As a result, the similarity among attack traffic should higher than that among flash crowds, which are generated by many random users. Mathematical models have been established for the proposed detection strategies. Analysis based on the models indicates that the proposed methods can raise the alarm for potential DDoS flooding attacks and can differentiate DDoS flooding attacks from flash crowds with conditions. The extensive experiments and simulations confirmed the effectiveness of our proposed detection strategies.

Abstract: This work proposes a novel approach to infer and characterize Internet-scale DNS amplification DDoS attacks by leveraging the darknet space. Complementary to the pioneer work on inferring Distributed Denial of Service (DDoS) activities using darknet, this work shows that we can extract DDoS activities without relying on backscattered analysis. The aim of this work is to extract cyber security intelligence related to DNS Amplification DDoS activities such as detection period, attack duration, intensity, packet size, rate and geo-location in addition to various network-layer and flow-based insights. To achieve this task, the proposed approach exploits certain DDoS parameters to detect the attacks. We empirically evaluate the proposed approach using 720 GB of real darknet data collected from a /13 address space during a recent three months period. Our analysis reveals that the approach was successful in inferring significant DNS amplification DDoS activities including the recent prominent attack that targeted one of the largest anti-spam organizations. Moreover, the analysis disclosed the mechanism of such DNS amplification DDoS attacks. Further, the results uncover high-speed and stealthy attempts that were never previously documented. The case study of the largest DDoS attack in history lead to a better understanding of the nature and scale of this threat and can generate inferences that could contribute in detecting, preventing, assessing, mitigating and even attributing of DNS amplification DDoS activities.

Abstract: Cloud computing is still a juvenile and most dynamic field characterized by a buzzing IT industry. Virtually every industry and even some parts of the public sector are taking on cloud computing today, either as a provider or as a consumer. It has now become essentially an inseparable part of everyone's life. The cloud thus has become a part of the critical global infrastructure but is unique in that it has no customary borders to safeguard it from attacks. Once weakened these web servers can serve as a launching point for conducting further attacks against users in the cloud. One such attack is the DoS or its version DDOS attack. Distributed Denial of Service (DdoS) Attacks have recently emerged as one of the most newsworthy, if not the greatest weaknesses of the Internet. DDoS attacks cause economic losses due to the unavailability of services and potentially serious security problems due to incapacitation of critical infrastructures. This paper presents a simple distance estimation based technique to detect and prevent the cloud from flooding based DDoS attack and thereby protect other servers and users from its adverse effects.

Abstract: Distributed denial of service (DDoS) attacks are one of the major threats to the current Internet, and application-layer DDoS attacks utilizing legitimate HTTP requests to overwhelm victim resources are more undetectable. Consequently, neither intrusion detection systems (IDS) nor victim server can detect malicious packets. In this paper, a novel approach to detect application-layer DDoS attack is proposed based on entropy of HTTP GET requests per source IP address (HRPI). By approximating the adaptive autoregressive (AAR) model, the HRPI time series is transformed into a multidimensional vector series. Then, a trained support vector machine (SVM) classifier is applied to identify the attacks. The experiments with several databases are performed and results show that this approach can detect application-layer DDoS attacks effectively.

Abstract: The promise of pay-as-you-go and scalable model of Cloud Computing has attracted a large number of medium and small enterprises to adopt E-Commerce model of conducting on-line businesses. While E-Commerce applications on the Cloud expand businesses by making them more widely accessible, they also makes these applications susceptible to economic denial of sustainability attacks - a form of application layer DDoS attack that drive up the cost of Cloud computing by using up application resources. This paper focuses on detection and mitigation of EDoS for E-Commerce based applications. EDoS is different from traditional DDoS in that, the intention of the latter is to consume all the resources (like memory, bandwidth, CPU etc) of the Web Server thus making it unavailable to its legitimate users. EDoS on the other hand is caused by malicious users who are not interested in following the regular workflow of an E-commerce application by purchasing items but by employing it for their own purposes of entertainment, price-checks and idle surfing. We have a twofold solution, (i) admission control and (ii) congestion control. In the first, we limit number of clients that can simultaneously send requests, thus allowing only enough clients that can be served easily within available resources on the Web server. In the second, we change the priority of allowed clients based on the type of resources they visit and type of activities they perform, thus making the maximum resources available to good clients. We have integrated and evaluated this solution in a Web Application Firewall and found it quite effective in term of resources distribution among clients ranging from good and bad clients.

Abstract: Distributed Denial of Service (DDoS) attacks exhaust victim’s bandwidth or services. Traditional architecture of Internet is vulnerable to DDoS attacks and an ongoing cycle of attack & defense is observed. A recent attack report of year 2013 — ‘Quarter 1’ from Prolexic Technologies identifies that 1.75 percent increase in total number of DDoS attacks has been recorded as compared to similar attacks of previous year’s last quarter. In this paper, different types and techniques of DDoS attacks and their countermeasures are surveyed. The significance of this paper is the coverage of many aspects of countering DDoS attacks including new research on the topic. We survey different papers describing methods of defense against DDoS attacks based on entropy variations, traffic anomaly parameters, neural networks, device level defense, botnet flux identifications, application layer DDoS defense and countermeasures in wireless networks, CCN & cloud computing environments. We also discuss some traditional methods of defense such as traceback and packet filtering techniques, so that readers can identify major differences between traditional and current techniques of defense against DDoS attacks. We identify that application layer DDoS attacks possess the ability to produce greater impact on the victim as they are driven by legitimate-like traffic, making it quite difficult to identify and distinguish from legitimate requests. The need of improved defense against such attacks is therefore more demanding in research. The study conducted in this paper can be helpful for readers and researchers to recognize better techniques of defense in current times against DDoS attacks and contribute with more research on this topic in the light of future challenges identified in this paper.

Abstract: Distributed denial-of-service attack (DDoS Attack) is one of the types of attacks that use multiple hosts as attacker against a system. There is a difference between Distributed Denial-of-Service (DDoS Attack) and Denial-of-Service (DoS Attack). DDoS attacks are distributed, meaning spread using multiple hosts, while the DoS attack is one-on-one. DoS attacks requires a powerful host, either from the resource or operating system used to carry out the attack. In this study, we discuss how to handle DDoS attacks in the form of detection method based on the pattern of flow entries and handling mechanism using layered firewall. Tests carried out using three scenario that is simulations on normal network environment, unsecured network, and secure network. Then, we analyze the simulations result that has been done. The method used successfully filtering incoming packet, by dropped packets from the attacker when DDoS attack happen, while still be able to receive packets from legitimate hosts.

Abstract: Intrusion Detection System (IDS) is the process of monitoring the events that occur in a system or network and process them for possible intrusions where as Intrusion Prevention System (IPS) has the capability to attempt to stop such possible intrusions. Combining the two systems will result in IDPS which not only detects the attacks but also prevent such attacks to occur in the networks. Distributed Denial of Service (DDOS) attacks are the major concern for security in the collaborative networks. Although non DDOS attacks are also make the network performances poor, the effect of DDOS attacks is severe. In DDOS attacks, flooding of the particular node as victim and jam it with massive traffic happens and the complete network performance is affected. In this paper, a novel Intrusion Detection and Prevention System is designed which detects the flooding DDOS attacks based on Firecol and prevents the attacks based on Dynamic Growing Self Organizing Tree (DGSOT) for collaborative networks. Simulation results show that DGSOT with Firecol (DGSOTFC) produces better intrusion detection and prevention system. Performance metrics based on the parameters delay and energy conservation are better in DGSOT-FC than the traditional IDPS systems.

Abstract: An important trend in the computer science is towards Cloud Computing and we can see that many cloud services are proposed and developed in the Internet. An important cloud service like the IaaS as AWS EC2 can help many companies to build data centers with high performance computing resources and reduce the cost of maintaining the computing hardware. A data center which provides internet service may suffer from many security risks including Distributed Denial of Service (DDOS) attack. We believe that most of the cloud services, like Gmail, Drop box, Google Document, and etc., are based on HTTP connection. Hence, we aim at HTTP-based connection and propose a low reflection ratio mitigation system against the DDoS attacks. Our system is in the front of an IaaS that all of the virtual data centers in the IaaS are our protection targets. Our system consists of Source Checking, Counting, Attack Detection, Turing Test, and Question Generation modules. We provide a multi-stage detection to more precisely detect the possible attackers and a text-based turing test with question generation module to challenge the suspected requesters who are detected by the detection module. We implemented the proposed system and evaluated the performance to show that our system works efficiently to mitigate the DDoS traffic from the Internet.

Abstract: Distributed Denial of Service (DDoS) is defined as an attack in which mutiple compromised systems are made to attack a single target to make the services unavailable foe legitimate users.It is an attack designed to render a computer or network incapable of providing normal services. DDoS attack uses many compromised intermediate systems, known as botnets which are remotely controlled by an attacker to launch these attacks. DDOS attack basically results in the situation where an entity cannot perform an action for which it is authenticated. This usually means that a legitimate node on the network is unable to reach another node or their performance is degraded. The high interruption and severance caused by DDoS is really posing an immense threat to entire internet world today. Any compromiseto computing, communication and server resources such as sockets, CPU, memory, disk/database bandwidth, I/O bandwidth, router processing etc. for collaborative environment would surely endanger the entire application. It becomes necessary for researchers and developers to understand behaviour of DDoSattack because it affects the target network with little or no advance warning. Hence developing advanced intrusion detection and prevention systems for preventing, detecting, and responding to DDOS attack is a critical need for cyber space. Our rigorous survey study presented in this paper describes a platform for the study of evolution of DDoS attacks and their defense mechanisms.

Abstract: Cloud computing is an internet based pay as use service which provides three type of layered services (Software as a Service, Platform as a Service and Infrastructure as a Service) to its consumer on demand. These on demand service facilities is being provide by cloud to its consumers in multitenant environment but as facility increases complexity and security problems also increase. Here all the resources are at one place in data centers. Cloud uses public and private APIs (Application Programming Interface) to provide services to its consumer in multitenant environment. In this environment Distributed Denial of Service attack (DDoS), especially HTTP, XML or REST based DDoS attacks may be very dangerous and may provide very harmful effects for availability of services and all consumers may get affected at the same time. One other reason is that because the cloud computing users make their request in XML and then send this request using HTTP protocol and build their system interface with REST protocol (such as Amazon EC2 or Microsoft Azure) hence XML attack more vulnerable. So the threaten coming from distributed REST attacks are more and easy to implement by the attacker, but to security expert very difficult to resolve. So to resolve these attacks this paper introduces a comber approach for security services called filtering tree. This filtering tree has five filters to detect and resolve XML and HTTP DDoS attack.

Abstract: Methods and systems for a scalable solution to behavioral Distributed Denial of Service (DDoS) attacks targeting a network are provided. According to one embodiment, a method to determine the scaling treatment is provided for various granular layer parameters of the Open System Interconnection (OSI) model for communication systems. A hardware-based apparatus helps identify packet rates and determine packet rate thresholds through continuous and adaptive learning with multiple DDoS attack mitigation components. The system can be scaled up by stacking multiple DDoS attack mitigation components to provide protection against large scale DDoS attacks by distributing load across these stacked components.

Abstract: The cloud computing has grown as a promising business concept as well as one of the fastest growing segment of IT industry in the last few years However the popularity of Cloud Computing is increasing day by day but there are some challenges that are faced by it One of the main challenges of Cloud is security From past few years, DDoS attacks have been placed first on the list of cloud attacks DDos can have serious consequences, especially on the companies dependent on the internet for their business Thus, to reduce an impact of DoS DDoS is one of the alarming issues This paper focuses on the study of DDoS attacks in cloud and the Intrusion Detection Systems available to cope up with the issue

Abstract: Cloud computing is a well-known internet platform based technology that provides access to rented, remotely located and distributed IT resources such as computing infrastructure, storage, online web and utility application on a pay per usage model. As it is a widely used service by individual users to corporate organizations and contains valuable data and applications, it is known to be vulnerable to risks and threats such as network level threats, host level threats and virtualization layer vulnerabilities etc. However for counterattacking these vulnerabilities traditional defense measures exists but are not efficient, scalable and optimized to be used in cloud. The paper identifies the drawbacks in the current schemes used for handling network attacks (primarily DDOS) and provides a new direction in which the same level of security capabilities for network can be obtained with minimal expense of resources which is the prime requirement for any scheme for being applicable in cloud environment. The paper describes a prototype implementation of the concept with details of experimental setup and initial results.

Abstract: Internet threat Monitoring (ITM) is a monitoring system in the internet to detect, measure, characterize and track the security attacks against attack sources. Distributed Denial of Service (DDoS) is a serious threat to the internet. Attacker uses botnets to launch DDoS attack by sending malicious traffic and the goal is to exhaust ITM network resources such as utilization of network bandwidth, computing power of victim system, data structures used in victim operating systems. The attacker or the botmasters attempt to disable the ITMs by sending the traffic in flash crowd pattern. The Flash Crowd flows are from legitimate users and they are absolutely normal requests, the generated results are similar to the effect of DDoS attacks. Hence, it is important to distinguish DDoS attack flows from flash crowd flows in the internet traffic, for those who defend against DDoS attacks. Based on this, we used a discrimination algorithm based on entropy variations as a similarity metric among suspicious flows. We formulated the problem in the internet with botnets, and presented theoretical proofs for the feasibility of the proposed discrimination method.

Abstract: Different types and techniques of DDoS attacks & defense are studied in this paper with some recent information on attacks dominated in year 2012 (1st Quarter) We further provide simulation based analysis of an FTP server"s performance in a typical enterprise network under distributed denial of service attack Simulations in OPNET show noticeable variations in connection capacity, task processing and delay parameters of the attacked server as compared to the performance without attack DDoS detection and mitigation mechanisms discussed in this paper mainly focus on some recently investigated techniques Finally, conclusions are drawn on the basis of survey based study as well as simulation results

Abstract: Cloud Computing model, a large scale distributed computing infrastructure is built in order to meet the demand for power and memory storage, increasing vigorously due to scientific research and industrialization. It is a recently proposed model providing access to the distributed resources consistently. Maintaining the security, confidentiality and integrity of both users and resources is the most challenging aspect of cloud. The main disturbing element of the security of the cloud i.e. the DDoS attacks has led to the establishment of various technologies in order to gain defense against DDoS attacks. So here, this paper will propose a defense mechanism against the DDoS attacks which is known as cloud specific Intrusion Detection System. This defense mechanism will be able to detect the attack before the DDoS attack succeeds.

Abstract: Provided are methods and systems for mitigating a DDoS event. The method may comprise receiving an indication of a collapse of a collapsible virtual data circuit associated with network data traffic. In response to the received indication of the collapse, the collapse may be attributed to the DDoS event. Furthermore, the method may comprise redirecting the network data traffic to one or more DDoS mitigation services. The method may further comprise mitigating the DDoS event by the one or more DDoS mitigation services.

Abstract: Distributed Denial of Service (DDoS) attacks are an increasing threat to the Internet community. Intrusion Detection Systems (IDSs) have become a key component in ensuring the safety of systems and networks. As networks grow in size and speed, efficient scalable techniques should be available for IDSs. Gnutella is a Peer to-Peer (P2P) networking model that currently provides decentralized file-sharing capabilities to its users but the distinction between server and client is pale. Due to Gnutella’s dependence on a central unit, the program is vulnerable to security breaches. Methods/Statistical analysis: An IDS to detect DDoS attacks by simulating Artificial Immune System (AIS) is herein proposed. The proposed system uses an algorithm based on anomaly and signature-based detection mapped to AIS called “Generation of Detector (Genetic Algorithm)” to detect DDoS attacks. Each time an attack is identified, a new generation is added to the detectors dataset to detect the intrusions. Results: Simulation results show that the proposed method not only has adaptability, scalability, flexibility and variety but also has high accuracy and correctness. Conclusion/Application: The proposed algorithm efficiently reduces the false positives, thus the detection rate of intrusions is increased. Hence, the overall detection rate increases which ultimately increases the functional efficiency of the network to an acceptable level.

Abstract: DDoS attacks on the World Wide Web in broad-spectrum and predominantly in modern cloud computing has become a noticeable issue for researchers in academia and industry related to the field of computer sciences. DDoS attacks are cool to provoke but their uncovering is a very challenging and dingy task and therefore, an eye-catching weapon for hackers. Hence DDoS torrents do not have familiar appearances; therefore currently existing IDS cannot identify and discover these attacks perfectly. Correspondingly, there implementation is a bamboozling task. In practice, gossip based detection machines are used to detect such types of attacks by exchanging stream of traffic over line but still results in network congestion and have upstairs of superfluous and bonus packets. Keeping the above drawbacks in mind, we have proposed a DDoS detection and prevention mechanism in (1), that has the attractiveness of being easy to adapt and more trustworthy than existing counterparts. We have introduced entropy based detection mechanism for DDoS attack detection. In (2) we have implemented the same algorithm to grids platform, where we obtain an accuracy of 90%. Our proposed solution has no overhead of extra packets, hence resulting in good QoS. In this paper we are going to implement the same algorithm on clouds.

Abstract: An OpenFlow switch in an OpenFlow environment includes an attack determination module to collect statistical information on packet processing with respect to incoming packets to be processed in the OpenFlow switch at a predetermined period interval to determine whether a DDoS attack occurs. The Openflow switch also includes an attack responding module to perceive a feature of the DDoS attack by using the incoming packets introduced into the OpenFlow switch after the determination of the occurrence of the DDoS attack and process the incoming packets in line with the perceived feature of the DDoS attack. Therefore, it is possible to determine and responds to DDos attacks in the OpenFlow switches.