A Practical Approach to Protect IoT Devices against Attacks and Compile Security Incident Datasets

open access articleThe Internet of Things (IoT) introduced the opportunity of remotely manipulating home appliances (such as heating systems, ovens, blinds, etc.) using computers and mobile devices. This idea fascinated people and originated a boom of IoT devices together with an increasing demand that was difficult to support. Many manufacturers quickly created hundreds of devices implementing functionalities but neglected some critical issues pertaining to device security. This oversight gave rise to the current situation where thousands of devices remain unpatched having many security issues that manufacturers cannot address after the devices have been produced and deployed. This article presents our novel research protecting IOT devices using Berkeley Packet Filters (BPFs) and evaluates our findings with the aid of our Filter.tlk tool, which is able to facilitate the development of BPF expressions that can be executed by GNU/Linux systems with a low impact on network packet throughput

Active and assisted living ecosystem for the elderly

A novel ecosystem to promote the physical, emotional and psychic health and well-being of the elderly is presented. Our proposal was designed to add several services developed to meet the needs of the senior population, namely services to improve social inclusion and increase contribution to society. Moreover, the solution monitors the vital signs of elderly individuals, as well as environmental parameters and behavior patterns, in order to seek eminent danger situations and predict potential hazardous issues, acting in accordance with the various alert levels specified for each individual. The platform was tested by seniors in a real scenario. The experimental results demonstrated that the proposed ecosystem was well accepted and is easy to use by seniors

Multi-objective evolutionary optimization for dimensionality reduction of texts represented by synsets

Despite new developments in machine learning classification techniques, improving the accuracy of spam filtering is a difficult task due to linguistic phenomena that limit its effectiveness. In particular, we highlight polysemy, synonymy, the usage of hypernyms/hyponyms, and the presence of irrelevant/confusing words. These problems should be solved at the pre-processing stage to avoid using inconsistent information in the building of classification models. Previous studies have suggested that the use of synset-based representation strategies could be successfully used to solve synonymy and polysemy problems. Complementarily, it is possible to take advantage of hyponymy/hypernymy-based to implement dimensionality reduction strategies. These strategies could unify textual terms to model the intentions of the document without losing any information ( e.g. , bringing together the synsets “viagra”, “ciallis”, “levitra” and other representing similar drugs by using “virility drug” which is a hyponym for all of them). These feature reduction schemes are known as lossless strategies as the information is not removed but only generalised. However, in some types of text classification problems (such as spam filtering) it may not be worthwhile to keep all the information and let dimensionality reduction algorithms discard information that may be irrelevant or confusing. In this work, we are introducing the feature reduction as a multi-objective optimisation problem to be solved using a Multi-Objective Evolutionary Algorithm (MOEA). Our algorithm allows, with minor modifications, to implement lossless (using only semantic-based synset grouping), low-loss (discarding irrelevant information and using semantic-based synset grouping) or lossy (discarding only irrelevant information) strategies. The contribution of this study is two-fold: (i) to introduce different dimensionality reduction methods (lossless, low-loss and lossy) as an optimization problem that can be solved using MOEA and (ii) to provide an experimental comparison of lossless and low-loss schemes for text representation. The results obtained support the usefulness of the low-loss method to improve the efficiency of classifiers.Agencia Estatal de Investigación | Ref. TIN2017-84658-C2-1-RAgencia Estatal de Investigación | Ref. TIN2017-84658-C2-2-RXunta de Galicia | Ref. ED431C 2022/03-GRCEusko Jaurlaritza | Ref. IT1676-22Fundação para a Ciência e a Tecnologia | Ref. UIDB/04466/2020Fundação para a Ciência e a Tecnologia | Ref. UIDP/04466/202

A practical approach to protect IoT devices against attacks and compile security incident datasets

The Internet of Things (IoT) introduced the opportunity of remotely manipulating home appliances (such as heating systems, ovens, blinds, etc.) using computers and mobile devices. This idea fascinated people and originated a boom of IoT devices together with an increasing demand that was difficult to support. Many manufacturers quickly created hundreds of devices implementing functionalities but neglected some critical issues pertaining to device security. This oversight gave rise to the current situation where thousands of devices remain unpatched having many security issues that manufacturers cannot address after the devices have been produced and deployed. This article presents our novel research protecting IOT devices using Berkeley Packet Filters (BPFs) and evaluates our findings with the aid of our Filter.tlk tool, which is able to facilitate the development of BPF expressions that can be executed by GNU/Linux systems with a low impact on network packet throughput.Xunta de Galicia | Ref. ED481B 2017/018Ministerio de Economía y Competitividad | Ref. TIN2017-84658-C2-1-RXunta de Galicia | Ref. ED431C2018/55-GR

Using Live Spam Beater (LiSB) Framework for Spam Filtering during SMTP Transactions

This study introduces the Live Spam Beater (LiSB) framework for the execution of email filtering techniques during SMTP (Simple Mail Transfer Protocol) transactions. It aims to increase the effectiveness and efficiency of existing proactive filtering mechanisms, mainly based on simple blacklists. Since it implements some proactive filtering schemes (during SMTP transaction), when an email message is classified as spam, the sender can be notified by an SMTP response code as a result of the transaction itself. The presented framework is written in Python programming language, works as an MTA (Mail Transfer Agent) server that implements an SMTP (Simple Mail Transfer Protocol) reverse proxy and allows the use of plugins to easily incorporate new filtering techniques designed to operate proactively. We also include a plugin to perform proactive content-based filtering through the analysis of words included in the body of the email message. Finally, we measured the performance of the plugin and the framework (time required for operation and accuracy) obtaining values suitable for their use during SMTP transactions.Xunta de Galicia | Ref. ED431C 2022/03-GR

Active and Assisted Living Ecosystem for the Elderly

A novel ecosystem to promote the physical, emotional and psychic health and well-being of the elderly is presented. Our proposal was designed to add several services developed to meet the needs of the senior population, namely services to improve social inclusion and increase contribution to society. Moreover, the solution monitors the vital signs of elderly individuals, as well as environmental parameters and behavior patterns, in order to seek eminent danger situations and predict potential hazardous issues, acting in accordance with the various alert levels specified for each individual. The platform was tested by seniors in a real scenario. The experimental results demonstrated that the proposed ecosystem was well accepted and is easy to use by seniors

Multi-Objective Evolutioary Algorithms for Synset Dimensionality Reduction

Multi-Objective Evolutioary Algorithms for Synset Dimensionality Reduction This work has been developed to discover the usage of Multi-Objective Evolutionary computation to reduce the dimensionality of synset-based datatsets. The objective of this code is to introduce different dimensionality reduction methods (lossless, low-loss and lossy) as an optimization problem that can be solved using Multi-Objective Evolutionary Algorithms (MOEA)

Enzymatic protein digests do not assist in E. coli discrimination at the strain level using mass spectrometry

Different procedures for matrix assisted laser desorption ionization time of flight mass spectrometry-based E. coli classification at the strain level using the enzymatic digestion of proteins from the cell lysate have been studied. The effects of ultrasonic energy as well as the effects of protein reduction and protein alkylation in the sample treatment and in the subsequent classification were assessed. The final optimal method for classification was then compared with an intact cell-based approach in a different set of samples. Our results show that E. coli classification at the strain level is possible as 12 different strains were correctly classified using intact cell analysis. The classification done using protein digestion does not classify the strains with the same level of confidence than intact cell analysis does.