Biblio

This research proposes a system for detecting known and unknown Distributed Denial of Service (DDoS) Attacks. The proposed system applies two different intrusion detection approaches anomaly-based distributed artificial neural networks(ANNs) and signature-based approach. The Amazon public cloud was used for running Spark as the fast cluster engine with varying cores of machines. The experiment results achieved the highest detection accuracy and detection rate comparing to signature based or neural networks-based approach.

As the Internet of Things (IoT) continues to grow, there arises concerns and challenges with regard to the security and privacy of the IoT system. In this paper, we propose a FOg CompUting-based Security (FOCUS) system to address the security challenges in the IoT. The proposed FOCUS system leverages the virtual private network (VPN) to secure the access channel to the IoT devices. In addition, FOCUS adopts a challenge-response authentication to protect the VPN server against distributed denial of service (DDoS) attacks, which can further enhance the security of the IoT system. FOCUS is implemented in fog computing that is close to the end users, thus achieving a fast and efficient protection. We demonstrate FOCUS in a proof-of-concept prototype, and conduct experiments to evaluate its performance. The results show that FOCUS can effectively filter out malicious attacks with a very low response latency.

Internet of Thing (IoT) provide services by linking the different platform devices. They have the limitation in providing intelligent service. The IoT devices are heterogeneous which includes wireless sensors to less resource constrained devices. These devices are prone to hardware/software and network attacks. If not properly secured, it may lead to security issues like privacy and confidentiality. To resolve the above problem, an Intelligent Security Framework for IoT Devices is proposed in this paper. The proposed method is made up of (1) the light weight Asymmetric cryptography for securing the End-To-End devices which protects the IoT service gateway and the low power sensor nodes and (2) implements Lattice-based cryptography for securing the Broker devices/Gateway and the cloud services. The proposed architecture implements Asymmetric Key Encryption to share session key between the nodes and then uses this session key for message transfer This protects the system from Distributed Denial of Service Attacks, eavesdropping and Quantum algorithm attacks. The proposed protocol uses the unique Device ID of the sensors to generate key pair to establish mutual authentication between Devices and Services. Finally, the Mutual authentication mechanism is implemented in the gateway.

Distributed Denial of Service (DDoS) attacks are a popular and inexpensive form of cyber attacks. Application layer DDoS attacks utilize legitimate application layer requests to overwhelm a web server. These attacks are a major threat to Internet applications and web services. The main goal of these attacks is to make the services unavailable to legitimate users by overwhelming the resources on a web server. They look valid in connection and protocol characteristics, which makes them difficult to detect. In this paper, we propose a detection method for the application layer DDoS attacks, which is based on user behavior anomaly detection. We extract instances of user behaviors requesting resources from HTTP web server logs. We apply the Principle Component Analysis (PCA) subspace anomaly detection method for the detection of anomalous behavior instances. Web server logs from a web server hosting a student resource portal were collected as experimental data. We also generated nine different HTTP DDoS attacks through penetration testing. Our performance results on the collected data show that using PCAsubspace anomaly detection on user behavior data can detect application layer DDoS attacks, even if they are trying to mimic a normal user's behavior at some level.

Aiming at the problem of internal attackers of database system, anomaly detection method of user behaviour is used to detect the internal attackers of database system. With using Discrete-time Markov Chains (DTMC), an anomaly detection system of user behavior is proposed, which can detect the internal threats of database system. First, we make an analysis on SQL queries, which are user behavior features. Then, we use DTMC model extract behavior features of a normal user and the detected user and make a comparison between them. If the deviation of features is beyond threshold, the detected user behavior is judged as an anomaly behavior. The experiments are used to test the feasibility of the detction system. The experimental results show that this detction system can detect normal and abnormal user behavior precisely and effectively.

Distributed Denial of Service attacks against high-profile targets have become more frequent in recent years. In response to such massive attacks, several architectures have adopted proxies to introduce layers of indirection between end users and target services and reduce the impact of a DDoS attack by migrating users to new proxies and shuffling clients across proxies so as to isolate malicious clients. However, the reactive nature of these solutions presents weaknesses that we leveraged to develop a new attack - the proxy harvesting attack - which enables malicious clients to collect information about a large number of proxies before launching a DDoS attack. We show that current solutions are vulnerable to this attack, and propose a moving target defense technique consisting in periodically and proactively replacing one or more proxies and remapping clients to proxies. Our primary goal is to disrupt the attacker's reconnaissance effort. Additionally, to mitigate ongoing attacks, we propose a new client-to-proxy assignment strategy to isolate compromised clients, thereby reducing the impact of attacks. We validate our approach both theoretically and through simulation, and show that the proposed solution can effectively limit the number of proxies an attacker can discover and isolate malicious clients.

Denial of Service (DoS) attacks is one of the major threats and among the hardest security problems in the Internet world. Of particular concern are Distributed Denial of Service (DDoS) attacks, whose impact can be proportionally severe. With little or no advance warning, an attacker can easily exhaust the computing resources of its victim within a short period of time. In this paper, we study the impact of a UDP flood attack on TCP throughput, round-trip time, and CPU utilization for a Web Server with the new generation of Linux platform, Linux Ubuntu 13. This paper also evaluates the impact of various defense mechanisms, including Access Control Lists (ACLs), Threshold Limit, Reverse Path Forwarding (IP Verify), and Network Load Balancing. Threshold Limit is found to be the most effective defense.

In cyberspace, availability of the resources is the key component of cyber security along with confidentiality and integrity. Distributed Denial of Service (DDoS) attack has become one of the major threats to the availability of resources in computer networks. It is a challenging problem in the Internet. In this paper, we present a detailed study of DDoS attacks on the Internet specifically the attacks due to protocols vulnerabilities in the TCP/IP model, their countermeasures and various DDoS attack mechanisms. We thoroughly review DDoS attacks defense and analyze the strengths and weaknesses of different proposed mechanisms.

Distributed Denial of Service (DoS) attacks is one of the major threats and among the hardest security problems in the Internet world. In this paper, we study the impact of a UDP flood attack on TCP throughputs, round-trip time, and CPU utilization on the latest version of Windows and Linux platforms, namely, Windows Server 2012 and Linux Ubuntu 13. This paper also evaluates several defense mechanisms including Access Control Lists (ACLs), Threshold Limit, Reverse Path Forwarding (IP Verify), and Network Load Balancing. Threshold Limit defense gave better results than the other solutions.

Botnet is one of the most widespread and serious malware which occur frequently in today's cyber attacks. A botnet is a group of Internet-connected computer programs communicating with other similar programs in order to perform various attacks. HTTP-based botnet is most dangerous botnet among all the different botnets available today. In botnets detection, in particularly, behavioural-based approaches suffer from the unavailability of the benchmark datasets and this lead to lack of precise results evaluation of botnet detection systems, comparison, and deployment which originates from the deficiency of adequate datasets. Most of the datasets in the botnet field are from local environment and cannot be used in the large scale due to privacy problems and do not reflect common trends, and also lack some statistical features. To the best of our knowledge, there is not any benchmark dataset available which is infected by HTTP-based botnet (HBB) for performing Distributed Denial of Service (DDoS) attacks against Web servers by using HTTP-GET flooding method. In addition, there is no Web access log infected by botnet is available for researchers. Therefore, in this paper, a complete test-bed will be illustrated in order to implement a real time HTTP-based botnet for performing variety of DDoS attacks against Web servers by using HTTP-GET flooding method. In addition to this, Web access log with http bot traces are also generated. These real time datasets and Web access logs can be useful to study the behaviour of HTTP-based botnet as well as to evaluate different solutions proposed to detect HTTP-based botnet by various researchers.