Network Traffic Behavioral Analytics for Detection of DDoS Attacks

As more organizations and businesses in different sectors are moving to a digital transformation, there is a steady increase in malware, facing data theft or service interruptions caused by cyberattacks on network or application that impact their customer experience. Bot and Distributed Denial of Service (DDoS) attacks consistently challenge every industry relying on the internet. In this paper, we focus on Machine Learning techniques to detect DDoS attack in network communication flows using continuous learning algorithm that learns the normal pattern of network traffic, behavior of the network protocols and identify a compromised network flow. Detection of DDoS attack will help the network administrators to take immediate action and mitigate the impact of such attacks. DDoS attacks are costing enterprises anywhere between $50,000 to$2.3 million per year. We performed experiments with Intrusion Detection Evaluation Dataset (CICIDS2017) available from Canadian Institute for Cybersecurity to detect anomalies in network traffic. We use flow based traffic characteristics to analyze the difference in pattern between normal vs anomaly packet.We evaluate several supervised classification algorithms using metrics like maximum detection accuracy, lowest false negatives prediction, time taken to train and run. We prove that decision tree based Random Forest is the most promising algorithm whereas Dense Neural network performs equally well on certain DDoS types but require more samples to improve the accuracy of low sampled attacks

From cyber-security deception to manipulation and gratification through gamification

Over the last two decades the field of cyber-security has experienced numerous changes associated with the evolution of other fields, such as networking, mobile communications, and recently the Internet of Things (IoT) [3]. Changes in mindsets have also been witnessed, a couple of years ago the cyber-security industry only blamed users for their mistakes often depicted as the number one reason behind security breaches. Nowadays, companies are empowering users, modifying their perception of being the weak link, into being the center-piece of the network design [4]. Users are by definition "in control" and therefore a cyber-security asset. Researchers have focused on the gamification of cyber- security elements, helping users to learn and understand the concepts of attacks and threats, allowing them to become the first line of defense to report anoma- lies [5]. However, over the past years numerous infrastructures have suffered from malicious intent, data breaches, and crypto-ransomeware, clearly showing the technical "know-how" of hackers and their ability to bypass any security in place, demonstrating that no infrastructure, software or device can be consid- ered secure. Researchers concentrated on the gamification, learning and teaching theory of cyber-security to end-users in numerous fields through various techniques and scenarios to raise cyber-situational awareness [2][1]. However, they overlooked the users’ ability to gather information on these attacks. In this paper, we argue that there is an endemic issue in the the understanding of hacking practices leading to vulnerable devices, software and architectures. We therefore propose a transparent gamification platform for hackers. The platform is designed with hacker user-interaction and deception in mind enabling researchers to gather data on the techniques and practices of hackers. To this end, we developed a fully extendable gamification architecture allowing researchers to deploy virtualised hosts on the internet. Each virtualised hosts contains a specific vulnerability (i.e. web application, software, etc). Each vulnerability is connected to a game engine, an interaction engine and a scoring engine

Data Science and Big Data in Energy Forecasting

This editorial summarizes the performance of the special issue entitled Data Science and Big Data in Energy Forecasting, which was published at MDPI’s Energies journal. The special issue took place in 2017 and accepted a total of 13 papers from 7 different countries. Electrical, solar and wind energy forecasting were the most analyzed topics, introducing new methods with applications of utmost relevance.Ministerio de Competitividad TIN2014-55894-C2-RMinisterio de Competitividad TIN2017-88209-C2-

Distributed smart camera network for safety and security

Current CCTV surveillance solutions are generally retrospective tools. Because real time use of CCTV requires human monitors to view a potentially exorbitant number of video feeds, CCTV is usually only useful after an incident has occurred. However, new technologies are making it possible for machines to perform some tasks that previously required a human monitor. The proposed project seeks to augment existing CCTV systems with behavioral analytics. The system uses a series of cameras, FPGAs, and computers to track object movement throughout a facility. This information is used to build a model of normal movement. Object movements are compared against this model and any ones that diverge from the model are flagged for review by security personnel