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

Applying lazy learning algorithms to tackle concept drift in spam filtering

A great amount of machine learning techniques have been applied to problems where data is collected over an extended period of time. However, the disadvantage with many real-world applications is that the distribution underlying the data is likely to change over time. In these situations, a problem that many global eager learners face is their inability to adapt to local concept drift. Concept drift in spam is particularly difficult as the spammers actively change the nature of their messages to elude spam filters. Algorithms that track concept drift must be able to identify a change in the target concept (spam or legitimate e-mails) without direct knowledge of the underlying shift in distribution. In this paper we show how a previously successful instance-based reasoning e-mail filtering model can be improved in order to better track concept drift in spam domain. Our proposal is based on the definition of two complementary techniques able to select both terms and e-mails representative of the current situation. The enhanced system is evaluated against other well-known successful lazy learning approaches in two scenarios, all within a cost-sensitive framework. The results obtained from the experiments carried out are very promising and back up the idea that instance-based reasoning systems can offer a number of advantages tackling concept drift in dynamic problems, as in the case of the anti-spam filtering domain

SpamHunting: An instance-based reasoning system for spam labelling and filtering

n this paper we show an instance-based reasoning e-mail filtering model that outperforms classical machine learning techniques and other successful lazy learners approaches in the domain of anti-spam filtering. The architecture of the learning-based anti-spam filter is based on a tuneable en-hanced instance retrieval network able to accurately generalize e-mail representations. The reuse of similar messages is carried out by a simple unanimous voting mechanism to determine whether the tar-get case is spam or not. Previous to the final response of the system, the revision stage is only performed when the assigned class is spam whereby the system employs general knowledge in the form of meta-rules

Relaxing Feature Selection in Spam Filtering by Using Case-Based Reasoning Systems

This paper presents a comparison between two alternative strategies for addressing feature selection on a well known case-based reasoning spam filtering system called SpamHunting. We present the usage of the k more predictive features and a percentage-based strategy for the exploitation of our amount of information measure. Finally, we confirm the idea that the percentage feature selection method is more adequate for spam filtering domain

Managing irrelevant knowledge in CBR models for unsolicited e-mail classification

The problem of unsolicited e-mail has been increasing during recent years. Fortunately, some advanced technologies have been successfully applied to spam filtering, achieving promising results. Recently, we have introduced SpamHunting, a successful spam filter able to address the concept drift problem by combining a relevant term identification technique with an evolving sliding window strategy. Several successful spam filtering techniques use continuous learning strategies to achieve better adaptation capabilities and address concept drift issues. Nevertheless, due to the presence of concept drift and hidden changes in the environment, the presence of obsolete and irrelevant knowledge becomes a serious drawback. Soon after the launch of the filter, many decisions are made based on irrelevant and/or obsolete knowledge. Therefore, in such a situation, the use of forgetting strategies is as important as the implementation of continuous learning approaches. In this paper we introduce a novel technique designed for identifying and removing the obsolete and irrelevant knowledge that has accumulated over to the passage of time. We have carried out several experiments to test for the suitability of our proposal showing the results obtained and its applicability

Tracking Concept Drift at Feature Selection Stage in SpamHunting: An Anti-spam Instance-Based Reasoning System

In this paper we propose a novel feature selection method able to handle concept drift problems in spam filtering domain. The proposed technique is applied to a previous successful instance-based reasoning e-mail filtering system called SpamHunting. Our achieved information criterion is based on several ideas extracted from the well-known information measure introduced by Shannon. We show how results obtained by our previous system in combination with the improved feature selection method outperforms classical machine learning techniques and other well-known lazy learning approaches. In order to evaluate the performance of all the analysed models, we employ two different corpus and six well-known metrics in various scenarios

A Comparative Performance Study of Feature Selection Methods for the Anti-spam Filtering Domain

In this paper we analyse the strengths and weaknesses of the mainly used feature selection methods in text categorization when they are applied to the spam problem domain. Several experiments with different feature selection methods and content-based filtering techniques are carried out and discussed. Information Gain, χ 2-text, Mutual Information and Document Frequency feature selection methods have been analysed in conjunction with Naïve Bayes, boosting trees, Support Vector Machines and ECUE models in different scenarios. From the experiments carried out the underlying ideas behind feature selection methods are identified and applied for improving the feature selection process of SpamHunting, a novel anti-spam filtering software able to accurate classify suspicious e-mails

Assessing Classification Accuracy in the Revision Stage of a CBR Spam Filtering System

In this paper we introduce a quality metric for characterizing the solutions generated by a successful CBR spam filtering system called SpamHunting. The proposal is denoted as relevant information amount rate and it is based on combining estimations about relevance and amount of information recovered during the retrieve stage of a CBR system. The results obtained from experimentation show how this measure can successfully be used as a suitable complement for the classifications computed by our SpamHunting system. In order to evaluate the performance of the quality estimation index, we have designed a formal benchmark procedure that can be used to evaluate any accuracy metric. Finally, following the designed test procedure, we show the behaviour of the proposed measure using two well-known publicly available corpus

Immediate Breast Reconstruction (IBR) With Direct, Anatomic, Extra-Projection Prosthesis 102 Cases

Abstract: There are different methods described until now for immediate breast reconstruction. Despite the use of autologous flaps considered by many authors, implants are considered as an option by others. A prospective study of 102 clinical cases was designed, including a 1-year follow-up in which glands were reconstructed by immediate breast reconstruction (IBR) with direct, extra projection, anatomic prostheses located in a submuscular pocket after a skin-sparing mastectomy. The prosthesis coverage was made by the muscle in its upper two thirds and by using the skin from the mastectomy in its lower third. The cosmetic results obtained were evaluated according to the volume, form, and symmetry achieved using a linear numeric analogical score. This evaluation had an averaged value of 2.79 Ϯ 0.8 in our scale from poor (0) to excellent result (4). The overall rate of complications was 15.7% of the cases, with seroma being the most frequent. In conclusion, this preliminary study demonstrates that immediate breast reconstruction with a direct, extra projection, anatomic prosthesis is a good alternative. Nevertheless, more long-term studies with a higher number of patients and using an SF-36 for patient satisfaction are needed to confirm these results