A survey of denial-of-service and distributed denial of service attacks and defenses in cloud computing

Cloud Computing is a computingmodel that allows ubiquitous, convenient and on-demand access to a shared pool of highly configurable resources (e.g., networks, servers, storage, applications and services). Denial-of-Service (DoS) and Distributed Denial-of-Service (DDoS) attacks are serious threats to the Cloud services’ availability due to numerous new vulnerabilities introduced by the nature of the Cloud, such as multi-tenancy and resource sharing. In this paper, new types of DoS and DDoS attacks in Cloud Computing are explored, especially the XML-DoS and HTTP-DoS attacks, and some possible detection and mitigation techniques are examined. This survey also provides an overview of the existing defense solutions and investigates the experiments and metrics that are usually designed and used to evaluate their performance, which is helpful for the future research in the domain

A trust-based game theoretical model for cooperative intrusion detection in multi-cloud environments

Cloud systems are becoming more complex and vulnerable to attacks. Cyber attacks are also becoming more sophisticated and harder to detect. Therefore, it is increasingly difficult for a single cloud-based intrusion detection system (IDS) to detect all attacks, because of limited and incomplete knowledge about attacks. The recent researches in cyber-security have shown that a co-operation among IDSs can bring higher detection accuracy in such complex computer systems. Through collaboration, a cloud-based IDS can consult other IDSs about suspicious intrusions and increase the decision accuracy. The problem of existing cooperative IDS approaches is that they overlook having untrusted (malicious or not) IDSs that may negatively effect the decision about suspicious intrusions in the cloud. Moreover, they rely on a centralized architecture in which a central agent regulates the cooperation, which contradicts the distributed nature of the cloud. In this paper, we propose a framework that enables IDSs to distributively form trustworthy IDSs communities. We devise a novel decentralized algorithm, based on coalitional game theory, that allows a set of cloud-based IDSs to cooperatively set up their coalition in such a way to make their individual detection accuracy increase, even in the presence of untrusted IDSs

An Emergency Event Detection Ensemble Model Based on Big Data

Emergency events arise when a serious, unexpected, and often dangerous threat affects normal life. Hence, knowing what is occurring during and after emergency events is critical to mitigate the effect of the incident on humans’ life, on the environment and our infrastructures, as well as the inherent financial consequences. Social network utilization in emergency event detection models can play an important role as information is shared and users’ status is updated once an emergency event occurs. Besides, big data proved its significance as a tool to assist and alleviate emergency events by processing an enormous amount of data over a short time interval. This paper shows that it is necessary to have an appropriate emergency event detection ensemble model (EEDEM) to respond quickly once such unfortunate events occur. Furthermore, it integrates Snapchat maps to propose a novel method to pinpoint the exact location of an emergency event. Moreover, merging social networks and big data can accelerate the emergency event detection system: social network data, such as those from Twitter and Snapchat, allow us to manage, monitor, analyze and detect emergency events. The main objective of this paper is to propose a novel and efficient big data-based EEDEM to pinpoint the exact location of emergency events by employing the collected data from social networks, such as “Twitter” and “Snapchat”, while integrating big data (BD) and machine learning (ML). Furthermore, this paper evaluates the performance of five ML base models and the proposed ensemble approach to detect emergency events. Results show that the proposed ensemble approach achieved a very high accuracy of 99.87% which outperform the other base models. Moreover, the proposed base models yields a high level of accuracy: 99.72%, 99.70% for LSTM and decision tree, respectively, with an acceptable training time