Biblio

Everyday., the DoS/DDoS attacks are increasing all over the world and the ways attackers are using changing continuously. This increase and variety on the attacks are affecting the governments, institutions, organizations and corporations in a bad way. Every successful attack is causing them to lose money and lose reputation in return. This paper presents an introduction to a method which can show what the attack and where the attack based on. This is tried to be achieved with using clustering algorithm DBSCAN on network traffic because of the change and variety in attack vectors.

In this paper, we propose a hybrid collaborative filtering recommendation algorithm based on heuristic similarity and trust measure, in order to alleviate the problem of data sparsity, cold start and trust measure. Firstly, a new similarity measure is implemented by weighted fusion of multiple similarity influence factors obtained from the rating matrix, so that the similarity measure becomes more accurate. Then, a user trust relationship computing model is implemented by constructing the user's trust network based on the trust propagation theory. On this basis, a SIMT collaborative filtering algorithm is designed which integrates trust and similarity instead of the similarity in traditional collaborative filtering algorithm. Further, an improved K nearest neighbor recommendation based on clustering algorithm is implemented for generation of a better recommendation list. Finally, a comparative experiment on FilmTrust dataset shows that the proposed algorithm has improved the quality and accuracy of recommendation, thus overcome the problem of data sparsity, cold start and trust measure to a certain extent.

Corpora used to learn open-domain Question-Answering (QA) models are typically collected from a wide variety of topics or domains. Since QA requires understanding natural language, open-domain QA models generally need very large training corpora. A simple way to alleviate data demand is to restrict the domain covered by the QA model, leading thus to domain-specific QA models. While learning improved QA models for a specific domain is still challenging due to the lack of sufficient training data in the topic of interest, additional training data can be obtained from related topic domains. Thus, instead of learning a single open-domain QA model, we investigate domain adaptation approaches in order to create multiple improved domain-specific QA models. We demonstrate that this can be achieved by stratifying the source dataset, without the need of searching for complementary data unlike many other domain adaptation approaches. We propose a deep architecture that jointly exploits convolutional and recurrent networks for learning domain-specific features while transferring domain-shared features. That is, we use transferable features to enable model adaptation from multiple source domains. We consider different transference approaches designed to learn span-level and sentence-level QA models. We found that domain-adaptation greatly improves sentence-level QA performance, and span-level QA benefits from sentence information. Finally, we also show that a simple clustering algorithm may be employed when the topic domains are unknown and the resulting loss in accuracy is negligible.

Information centric network (ICN) based Mobile Edge Computing (MEC) network has drawn growing attentions in recent years. The distributed network architecture brings new security problems, especially the identity security problem. Because of the cloud platform deployed on the edge of the MEC network, multiple channel attributes can be easily obtained and processed. Thus this paper proposes a multiple channel attributes based spoofing detection mechanism. To further reduce the complexity, we also propose an improved clustering algorithm. The simulation results indicate that the proposed spoofing detection method can provide near-optimal performance with extremely low complexity.

In order to improve the spectrum utilization rate of Device-to-Device (D2D) communication, we study the hybrid resource allocation problem, which allows both the resource reuse and resource dedicated mode to work simultaneously. Meanwhile, multiple D2D devices are permitted to share uplink cellular resources with some designated cellular user equipment (CUE). Combined with the transmission requirement of different users, the optimized resource allocation problem is built which is a NP-hard problem. A heuristic greedy throughput maximization (HGTM) based on clustering algorithm is then proposed to solve the above problem. Numerical results demonstrate that the proposed HGTM outperforms existing algorithms in the sum throughput, CUEs SINR performance and the number of accessed D2D deceives.

Volume anomaly such as distributed denial-of-service (DDoS) has been around for ages but with advancement in technologies, they have become stronger, shorter and weapon of choice for attackers. Digital forensic analysis of intrusions using alerts generated by existing intrusion detection system (IDS) faces major challenges, especially for IDS deployed in large networks. In this paper, the concept of automatically sifting through a huge volume of alerts to distinguish the different stages of a DDoS attack is developed. The proposed novel framework is purpose-built to analyze multiple logs from the network for proactive forecast and timely detection of DDoS attacks, through a combined approach of Shannon-entropy concept and clustering algorithm of relevant feature variables. Experimental studies on a cyber-range simulation dataset from the project industrial partners show that the technique is able to distinguish precursor alerts for DDoS attacks, as well as the attack itself with a very low false positive rate (FPR) of 22.5%. Application of this technique greatly assists security experts in network analysis to combat DDoS attacks.