Konstantinos Demertzis

Fast and low cost prediction of extreme air pollution values with hybrid unsupervised learning

Air pollution is the problem of adding harmful substances or other agents into the atmosphere and it is caused by industrial, transport or household activities. It is one of the most serious problems of our times and the determination of the conditions under which we have extreme pollutants’ values is a crucial challenge for the modern scientific community. The innovative and effective hybrid algorithm designed and employed in this research effort is entitled Easy Hybrid Forecasting (EHF). The main advantage of the EHF is that each forecasting does not require measurements from sensors, other hardware devices or data that require the use of expensive software. This was done intentionally because the motivation for this work was the development of a hybrid application that can be downloaded for free and used easily by everyday common people with no additional financial cost, running in devices like smart phones. From this point of view it does not require data from sensors or specialized software and it can offer people reliable information about extreme cases.

HISYCOL a hybrid computational intelligence system for combined machine learning: the case of air pollution modeling in Athens

The analysis of air quality and the continuous monitoring of air pollution levels are important subjects of the environmental science and research. This problem actually has real impact in the human health and quality of life. The determination of the conditions which favor high concentration of pollutants and most of all the timely forecast of such cases is really crucial, as it facilitates the imposition of specific protection and prevention actions by civil protection. This research paper discusses an innovative threefold intelligent hybrid system of combined machine learning algorithms HISYCOL (henceforth). First, it deals with the correlation of the conditions under which high pollutants concentrations emerge. On the other hand, it proposes and presents an ensemble system using combination of machine learning algorithms capable of forecasting the values of air pollutants. What is really important and gives this modeling effort a hybrid nature is the fact that it uses clustered datasets. Moreover, this approach improves the accuracy of existing forecasting models by using unsupervised machine learning to cluster the data vectors and trace hidden knowledge. Finally, it employs a Mamdani fuzzy inference system for each air pollutant in order to forecast even more effectively its concentrations.

Bio-inspired Hybrid Intelligent Method for Detecting Android Malware

Today’s smartphones are capable of doing much more than the previous generation of mobile phones. However this extended range of capabilities is coming together with some new security risks. Also, mobile platforms often contain small, insecure and less well controlled applications from various single developers. Due to the open usage model of the Android market, malicious applications cannot be avoided completely. Especially pirated applications or multimedia content in popular demand, targeting user groups with typically low awareness levels are predestined to spread too many devices before being identified as malware. Generally malware applications utilizing root exploits to escalate their privileges can inject code and place binaries outside applications storage locations. This paper proposes a novel approach, which uses minimum computational power and resources, to indentify Android malware or malicious applications. It is a bio-inspired Hybrid Intelligent Method for Detecting Android Malware (HIMDAM). This approach performs classification by employing Extreme Learning Machines (ELM) in order to properly label malware applications. At the same time, Evolving Spiking Neural Networks (eSNNs) are used to increase the accuracy and generalization of the entire model.

Adaptive Elitist Differential Evolution Extreme Learning Machines on Big Data: Intelligent Recognition of Invasive Species

One of the direct consequences of climate change lies in the spread of invasive species, which constitute a serious and rapidly worsening threat to ecology, preservation of natural biodiversity and protection of flora and fauna. It can even be a potential threat to the health of humans. These species, do not appear to have serious morphological variations, despite their strong biological differences. Due to this fact their identification process is often quite difficult. The need to protect the environment and safeguard public health, requires the development of advanced methods for early and accurate identification of some particularly dangerous invasive species, in order to plan and apply specific and effective management measures. The aim of this study is to create an advanced computer vision system for the automatic recognition of invasive or other unknown species, based on their phenotypes. More specifically, this research proposes an innovative and very effective Extreme Learning Machine (ELM) model, which is optimized by the Adaptive Elitist Differential Evolution algorithm (AEDE). The AEDE is an improved version of the differential evolution (DE) algorithm and it is proper for big data resolution. Feature selection is done by using deep learning Convolutional Neural Networks. A Geo Location system is used to detect the invasive species by Comparing with the local species of the region under research.

Intelligent Bio-Inspired Detection of Food Borne Pathogen by DNA Barcodes: The Case of Invasive Fish Species Lagocephalus Sceleratus

Climate change combined with the increase of extreme weather phenomena, has significantly influenced marine ecosystems, resulting in water overheating, increase of sea level and rising of the acidity of surface waters. The potential impacts in the biodiversity of sensitive ecosystems (such as Mediterranean sea) are obvious. Many organisms are under extinction, whereas other dangerous invasive species are multiplied and thus they are destroying the ecological equilibrium. This research paper presents the development of a sophisticated, fast and accurate Food Pathogen Detection (FPD) system, which uses the biologically inspired Artificial Intelligence algorithm of Extreme Learning Machines. The aim is the automated identification and control of the extremely dangerous for human health invasive fish species “Lagocephalus Sceleratus”. The matching is achieved through extensive comparisons of protein and DNA sequences, known also as DNA barcodes following an ensemble learning approach.

A Hybrid Network Anomaly and Intrusion Detection Approach Based on Evolving Spiking Neural Network Classification

The evolution of network services is closely connected to the understanding and modeling of their corresponding traffic. The obtained conclusions are related to a wide range of applications, like the design of the transfer lines’ capacity, the scalar taxing of customers, the security violations and the spotting of errors and anomalies. Intrusion Detection Systems (IDS) monitor and analyze the events in traffic, to locate indications for potential intrusion and integrity violation attacks, resulting in the violation of trust and availability of information resources. They act in a complimentary mode with the existing security infrastructure, aiming in the early warning of the administrator, offering him details that will let him reach proper decisions and correction actions. This paper proposes a network-based online system, which uses minimum computational power to analyze only the basic characteristics of network flow, so as to spot the existence and the type of a potential network anomaly. It is a Hybrid Machine Learning Anomaly Detection System (HMLADS), which employs classification performed by Evolving Spiking Neural Networks (eSNN), in order to properly label a Potential Anomaly (PAN) in the net. On the other hand it uses a Multi-Layer Feed Forward (MLFF) ANN to classify the exact type of the intrusion.

FuSSFFra, a fuzzy semi-supervised forecasting framework: the case of the air pollution in Athens

Mining hidden knowledge from available datasets is an extremely time-consuming and demanding process, especially in our era with the vast volume of high-complexity data. Additionally, validation of results requires the adoption of appropriate multifactor criteria, exhaustive testing and advanced error measurement techniques. This paper proposes a novel Hybrid Fuzzy Semi-Supervised Forecasting Framework. It combines fuzzy logic, semi-supervised clustering and semi-supervised classification in order to model Big Data sets in a faster, simpler and more essential manner. Its advantages are clearly shown and discussed in the paper. It uses as few pre-classified data as possible while providing a simple method of safe process validation. This innovative approach is applied herein to effectively model the air quality of Athens city. More specifically, it manages to forecast extreme air pollutants’ values and to explore the parameters that affect their concentration. Also it builds a correlation between pollution and general climatic conditions. Overall, it correlates the built model with the malfunctions caused to the city life by this serious environmental problem.

Semi-supervised Hybrid Modeling of Atmospheric Pollution in Urban Centers

Air pollution is directly linked with the development of technology and science, the progress of which besides significant benefits to mankind it also has adverse effects on the environment and hence on human health. The problem has begun to take worrying proportions especially in large urban centers, where 60,000 deaths are reported each year in Europe’s towns and 3,000,000 worldwide, due to long-term air pollution exposure (exposure of the European Agency for the Environment http://www.eea.europa.eu/). In this paper we propose a novel and flexible hybrid machine learning system that combines Semi-Supervised Classification and Semi-Supervised Clustering, in order to realize prediction of air pollutants outliers and to study the conditions that favor their high concentration.

Evolving Smart URL Filter in a Zone-Based Policy Firewall for Detecting Algorithmically Generated Malicious Domains

Domain Generation Algorithm (DGA) has evolved as one of the most dangerous and “undetectable” digital security deception methods. The complexity of this approach (combined with the intricate function of the fast-flux “botnet” networks) is the cause of an extremely risky threat which is hard to trace. In most of the cases it should be faced as zero-day vulnerability. This kind of combined attacks is responsible for malware distribution and for the infection of Information Systems. Moreover it is related to illegal actions, like money mule recruitment sites, phishing websites, illicit online pharmacies, extreme or illegal adult content sites, malicious browser exploit sites and web traps for distributing virus. Traditional digital security mechanisms face such vulnerabilities in a conventional manner, they create often false alarms and they fail to forecast them. This paper proposes an innovative fast and accurate evolving Smart URL Filter (eSURLF) in a Zone-based Policy Firewall (ZFW) which uses evolving Spiking Neural Networks (eSNN) for detecting algorithmically generated malicious domains names.

SAME: An Intelligent Anti-malware Extension for Android ART Virtual Machine

It is well known that cyber criminal gangs are already using advanced and especially intelligent types of Android malware, in order to overcome the out-of-band security measures. This is done in order to broaden and enhance their attacks which mainly target financial and credit foundations and their transactions. It is a fact that most applications used under the Android system are written in Java. The research described herein, proposes the development of an innovative active security system that goes beyond the limits of the existing ones. The developed system acts as an extension on the ART (Android Run Time) Virtual Machine architecture, used by the Android Lolipop 5.0 version. Its main task is the analysis and classification of the Java classes of each application. It is a flexible intelligent system with low requirements in computational resources, named Smart Anti Malware Extension (SAME). It uses the biologically inspired Biogeography-Based Optimizer (BBO) heuristic algorithm for the training of a Multi-Layer Perceptron (MLP) in order to classify the Java classes of an application as benign or malicious. SAME was run in parallel with the Particle Swarm Optimization (PSO), Ant Colony Optimization (ACO) and Genetic Algorithm (GA) and it has shown its validity.