Isolation of DDoS Attacks and Flash Events in Internet Traffic Using Deep Learning Techniques

The adoption of network function visualization (NFV) and software-defined radio (SDN) has created a tremendous increase in Internet traffic due to flexibility brought in the network layer. An increase in traffic flowing through the network poses a security threat that becomes tricky to detect and hence selects an appropriate mitigation strategy. Under such a scenario occurrence of the distributed denial of service (DDoS) and flash events (FEs) affect the target servers and interrupt services. Isolating the attacks is the first step before selecting an appropriate mitigation technique. However, detecting and isolating the DDoS attacks from FEs when happening simultaneously is a challenge that has attracted the attention of many researchers. This study proposes a deep learning framework to detect the FEs and DDoS attacks occurring simultaneously in the network and isolates one from the other. This step is crucial in designing appropriate mechanisms to enhance network resilience against such cyber threats. The experiments indicate that the proposed model possesses a high accuracy level in detecting and isolating DDoS attacks and FEs in networked systems

Profile of human resources and skills needs in the portuguese tourism sector

The tourism sector has been presented as one of the most important sectors of diverse economies due to its capacity to contribute to economic growth and job creation. Being an industry of people, it directly depends on the performance of activities, skills, professionalism, quality and competitiveness, so it is essential to answer with precise planning politics which should be the most approximated ones to the real needs of the sector. In Portugal, the tourism sector continues to reinforce its importance in society and in the national economy since it remains the main exporting economic sector. On the other hand, the main challenge of this sector is qualifying and increasing the level of qualification of its workers due to their inadequate level of qualification, since 50% of the employed population in this sector has a primary education level. Therefore, it has defined, in its public policies, the goal of duplicating, in the next decade, the number of employees with high school education qualifications. In this sense, once skills are becoming the global currency of the 21st century, this study aims to feature the main soft skills that touristic human resources should hold, based on the importance given to them by the national entrepreneurs of the sector. This study is based on a sample of 555 answers and used a qualitative methodology throughout a profound review of the literature as well as a quantitative methodology where an online survey was implemented, expecting to develop the ideal profile of the tourism human resources. The results of the study suggest that the profile of human resources in the tourism sector should include skills such as teamwork, knowledge of market trends, ability to conduct efficient strategic processes and decisions, as well as language skills, sales skills and digital communication.info:eu-repo/semantics/publishedVersio

Soteria: An Approach for Detecting Multi-Institution Attacks

We present Soteria, a data processing pipeline for detecting multi-institution attacks. Multi-institution attacks contact large number of potential targets looking for vulnerabilities that span multiple institutions. Soteria uses a set of Machine Learning techniques to detect future attacks, predict their future targets, and ranks attacks based on their predicted severity. Our evaluation with real data from Canada wide institutions networks shows that Soteria can predict future attacks with 95% recall rate, predict the next targets of an attack with 97% recall rate, and can detect attacks in the first 20% of their life span. Soteria is deployed in production at CANARIE Canada wide network that connects tens of Canadian academic institutions

HC Tourism. Profile and trends of human capital in the tourism sector

The economic crisis, the need for new skills and the demographic changes have contributed to the recognition that the learning strategies of adults and lifelong learning must play a key role in the policies of competitiveness and employability, social inclusion and active citizenship. Skills will determine competitiveness and will play a crucial and essential role in social cohesion through economic growth and job creation, thus intensifying the need for continuous improvement of skills to meet the growing needs of the labour market in knowledge-based economies (European Commission, 2017; World Economic Forum, 2018).European Investment Funds, FEDER funds through COMPETE 2020: POCI-01-0145-FEDER-023622; National Funds, Portuguese Foundation for Science and Technology (FCT): SAICTPOL/23622/2016.info:eu-repo/semantics/publishedVersio

Cryptography and Its Applications in Information Security

Nowadays, mankind is living in a cyber world. Modern technologies involve fast communication links between potentially billions of devices through complex networks (satellite, mobile phone, Internet, Internet of Things (IoT), etc.). The main concern posed by these entangled complex networks is their protection against passive and active attacks that could compromise public security (sabotage, espionage, cyber-terrorism) and privacy. This Special Issue “Cryptography and Its Applications in Information Security” addresses the range of problems related to the security of information in networks and multimedia communications and to bring together researchers, practitioners, and industrials interested by such questions. It consists of eight peer-reviewed papers, however easily understandable, that cover a range of subjects and applications related security of information

Detection of DDoS Attacks and Flash Events Using Shannon Entropy, KOAD and Mahalanobis Distance

The growing number of internet based services and applications along with increasing adoption rate of connected wired and wireless devices presents opportunities as well as technical challenges and threads. Distributed Denial of Service (DDoS) attacks have huge devastating effects on internet enabled services. It can be implemented diversely with a variety of tools and codes. Therefore, it is almost impossible to define a single solution to prevent DDoS attacks. The available solutions try to protect internet services from DDoS attacks, but there is no accepted best-practice yet to this security breach. On the other hand, distinguishing DDoS attacks from analogous Flash Events (FEs) wherein huge number of legitimate users try to access a specific internet based services and applications is a tough challenge. Both DDoS attacks and FEs result in unavailability of service, but they should be treated with different countermeasures. Therefore, it is worthwhile to investigate novel methods which can detect well disguising DDoS attacks from similar FE traffic. This paper will contribute to this topic by proposing a hybrid DDoS and FE detection scheme; taking 3 isolated approaches including Kernel Online Anomaly Detection (KOAD), Shannon Entropy and Mahalanobis Distance. In this study, Shannon entropy is utilized with an online machine learning technique to detect abnormal traffic including DDoS attacks and FE traffic. Subsequently, the Mahalanobis distance metric is employed to differentiate DDoS and FE traffic. the purposed method is validated using simulated DDoS attacks, real normal and FE traffic. The results revealed that the Mahalanobis distance metric works well in combination with machine learning approach to detect and discriminate DDoS and FE traffic in terms of false alarms and detection rate

A Hybrid Approach to Detect DDoS Attacks Using KOAD and the Mahalanobis Distance

Distributed Denial of Service (DDoS) attacks continue to adversely affect internet-based services and applications. Various approaches have been proposed to detect different types of DDoS attacks. The computational and memory complexities of most algorithms, however prevent them from being employed in online manner. In this paper, we propose a novel victim end online DDoS attack detection framework based on the celebrated Kernel-based Online Anomaly Detection (KOAD) algorithm and the Mahalanobis distance. We have employed the KOAD algorithm to adaptively model the normal behavior of network traffic, and then constructed the normal and abnormal datasets based on the results of KOAD. Subsequently, the Mahalanobis distance metric was calculated between datapoints of the abnormal and normal subsets. Finally, the chi-square test was used on the Mahalanobis distance values to segregate the DDoS attack datapoints from the normal ones. We have validated our algorithm on simulated DDoS scenarios, as well as real baseline data from a company operating in cyber security. Our results have revealed that our proposed hybrid approach boosts the performance of sole KOAD algorithm and Mahalanobis distance in detecting DDoS traffic in terms of both false positive and detection rates.IEEE; IEEE Tech Comm Distributed Proc; Akamai Technologies Inc; Int Res Inst Auton Network Comp; IEEE Comp So