A survey of intrusion detection techniques in Cloud

Cloud computing provides scalable, virtualized on-demand services to the end users with greater flexibility and lesser infrastructural investment. These services are provided over the Internet using known networking protocols, standards and formats under the supervision of different managements. Existing bugs and vulnerabilities in underlying technologies and legacy protocols tend to open doors for intrusion. This paper, surveys different intrusions affecting availability, confidentiality and integrity of Cloud resources and services. It examines proposals incorporating Intrusion Detection Systems (IDS) in Cloud and discusses various types and techniques of IDS and Intrusion Prevention Systems (IPS), and recommends IDS/IPS positioning in Cloud architecture to achieve desired security in the next generation networks

Common Mechanism for Detecting Multiple DDoS Attacks

An important principle of an internet-based system is information security. Information security is a very important aspect of distributed systems and IoT (Internet of Things) based wireless systems. The attack which is more harmful to the distributed system and IoT-based wireless system is a DDoS (Distributed Denial of Service) attack since in this attack, an attacker can stop the work of all other connected devices or users to the network. For securing distributed applications, various intrusion detection mechanisms are used. But most existing mechanisms are only concentrated on one kind of DDoS attack. This paper focuses on the basic architecture of IoT systems and an overview of single intrusion detection systems. This paper presents a single detection method for different DDoS attacks on distributed systems with an IoT interface. In the future, the system will provide support for detecting and preventing different DDoS attacks in IoT-based systems

SIEM-based detection and mitigation of IoT-botnet DDoS attacks

The Internet of Things (IoT) is becoming an integral part of our daily life including health, environment, homes, military, etc. The enormous growth of IoT in recent years has attracted hackers to take advantage of their computation and communication capabilities to perform different types of attacks. The major concern is that IoT devices have several vulnerabilities that can be easily exploited to form IoT botnets consisting of millions of IoT devices and posing significant threats to Internet security. In this context, DDoS attacks originating from IoT botnets is a major problem in today’s Internet that requires immediate attention. In this paper, we propose a Security Information and Event Management-based IoT botnet DDoS attack detection and mitigation system. This system detects and blocks DDoS attack traffic from compromised IoT devices by monitoring specific packet types including TCP SYN, ICMP and DNS packets originating from these devices. We discuss a prototype implementation of the proposed system and we demonstrate that SIEM based solutions can be configured to accurately identify and block malicious traffic originating from compromised IoT devices

Real time DDoS detection using fuzzy estimators

We propose a method for DDoS detection by constructing a fuzzy estimator on the mean packet inter arrival times. We divided the problem into two challenges, the first being the actual detection of the DDoS event taking place and the second being the identification of the offending IP addresses. We have imposed strict real time constraints for the first challenge and more relaxed constraints for the identification of addresses. Through empirical evaluation we confirmed that the detection can be completed within improved real time limits and that by using fuzzy estimators instead of crisp statistical descriptors we can avoid the shortcomings posed by assumptions on the model distribution of the traffic. In addition we managed to obtain results under a 3 sec detection window. © 2012 Elsevier Ltd. All rights reserved

Distributed Denial-of-Service Defense System

Distributed denial-of-service (DoS) attacks present a great threat to the Internet, and existing security mechanisms cannot detect or stop them successfully. The problem lies in the distributed nature of attacks, which engages the power of a vast number of coordinated hosts. To mitigate the impacts of DDoS attacks, it is important to develop such defenses system that canbothdetect andreact against ongoing attacks. The attacks ideally should be stopped as close to the sources as possible, saving network resources andreducing congestion. The DDoS defense system that is deployed at the source-end should prevent the machines at associated network from participating in DDoS attacks. The primary objective of this project, which is developing a DDoS defense system, is to provide good service to a victim's legitimate clients during the attack, thus canceling the denial-of-service effect. The scope of study will coverthe aspect of howthe attack detection algorithms work and identify the attack traffic, hence develop appropriate attack responses. As a source-end defense against DDoS attacks, the attack flows can be stopped before they enter the Internet core and before they aggregate with other attack flows. The methodology chosen for this project is the combination of sequential and iterative approaches of the software development process, which comprises of six main phases, which are initial planning phase, requirement definition phase, system design phase, coding and testing phase, implementation phase, and lastly maintenance and support phase. The system used a source router approach, in which the source router serves as a gateway between the source network containing some of the attack nodes and the rest of the Internet, to detectand limitDDoS streams long before they reach the target. This will be covered in the Findings section of the report. TheDiscussion section will be focus more onthe architecture onthe system, which having three important component; observation, rate-limiting and traffic-policing

Encountering distributed denial of service attack utilizing federated software defined network

This research defines the distributed denial of service (DDoS) problem in software-defined-networks (SDN) environments. The proposes solution uses Software defined networks capabilities to reduce risk, introduces a collaborative, distributed defense mechanism rather than server-side filtration. Our proposed network detection and prevention agent (NDPA) algorithm negotiates the maximum amount of traffic allowed to be passed to server by reconfiguring network switches and routers to reduce the ports' throughput of the network devices by the specified limit ratio. When the passed traffic is back to normal, NDPA starts network recovery to normal throughput levels, increasing ports' throughput by adding back the limit ratio gradually each time cycle. The simulation results showed that the proposed algorithms successfully detected and prevented a DDoS attack from overwhelming the targeted server. The server was able to coordinate its operations with the SDN controllers through a communication mechanism created specifically for this purpose. The system was also able to determine when the attack was over and utilize traffic engineering to improve the quality of service (QoS). The solution was designed with a sophisticated way and high level of separation of duties between components so it would not be affected by the design aspect of the network architecture

Resilience Strategies for Network Challenge Detection, Identification and Remediation

The enormous growth of the Internet and its use in everyday life make it an attractive target for malicious users. As the network becomes more complex and sophisticated it becomes more vulnerable to attack. There is a pressing need for the future internet to be resilient, manageable and secure. Our research is on distributed challenge detection and is part of the EU Resumenet Project (Resilience and Survivability for Future Networking: Framework, Mechanisms and Experimental Evaluation). It aims to make networks more resilient to a wide range of challenges including malicious attacks, misconfiguration, faults, and operational overloads. Resilience means the ability of the network to provide an acceptable level of service in the face of significant challenges; it is a superset of commonly used definitions for survivability, dependability, and fault tolerance. Our proposed resilience strategy could detect a challenge situation by identifying an occurrence and impact in real time, then initiating appropriate remedial action. Action is autonomously taken to continue operations as much as possible and to mitigate the damage, and allowing an acceptable level of service to be maintained. The contribution of our work is the ability to mitigate a challenge as early as possible and rapidly detect its root cause. Also our proposed multi-stage policy based challenge detection system identifies both the existing and unforeseen challenges. This has been studied and demonstrated with an unknown worm attack. Our multi stage approach reduces the computation complexity compared to the traditional single stage, where one particular managed object is responsible for all the functions. The approach we propose in this thesis has the flexibility, scalability, adaptability, reproducibility and extensibility needed to assist in the identification and remediation of many future network challenges

Botnet detection from drive-by downloads

The advancement in Information Technology has brought about an advancement in the development and deployment of malware. Bot Malware have brought about immense compromise in computer security. Various ways for the deployment of such bots have been devised by attackers and they are becoming stealthier and more evasive by the day. Detecting such bots has proven to be difficult even though there are various detection techniques. In this work, a packet capturing and analysis technique for detecting host-based bots on their characteristics and behavior is proposed. The system captures network traffic first, to establish normal traffic, then already captured botnet traffic was used to test the system. The system filters out HTTP packets and analyses these packets to further filter out botnet traffic from normal internet traffic. The system was able to detect malicious packets with a False Positive Rate of 0.2 and accuracy of 99.91%

Mitigation Model for DDoS Attack in Wireless Sensor Networks

A Denial-of-Service is an attack in which the attackers send certain messages to the target systems or target servers with a purpose and intention of shutting down those system or servers. Those messages cause such an impact to the victim that it makes its servicesunavailable or not responding for the users. When a DoS attack is implemented in large number, then it is referred to as a DDoS or Distributed enial-f-Service attack. In this,the attackers uses a large number of controlled bots called zombies and reflectors which are the innocent computers exploited to generate the attack. There are various kinds of DDoS attacks which depletes network bandwidth as well as its resources. We have particularly focused upon flooding kind of attacks. In this server’s capacity is exploited by sending huge number of unwanted requests with a purpose of failure of server’s processing efficiency. Since there is a limit to number of packet requests a server can effectively process. If that limit is exceeded, servers performance gets egraded. In this thesis, we have followed an approach for mitigating DoS/DDoS attack based on the Rate Limiting algorithm, used to mitigate flooding resulting to the attack applied at the server-side. Packet filtering has been done on the basis of legitimate TTL values of the incoming ackets followed by the ordering of packets to be sent to the server. Ordering of packets is performed with two approaches, one with the existing FCFS approach and other Priority queue approach and the server performance is compared. The implementation is carried out on the simulation tool MATLAB. The results show that there is considerable decrease in the two host and network based performance metrics that are Packet drop and Response time under DoS and DDoS attacks. When only legitimate packets are passed to the server after packet filtering, response time and throughput improves and after packet scheduling it even gets better