Eleazar Aguirre Anaya

Network forensics with Neurofuzzy techniques

Forensics science is based on a methodology composed by a group of stages, being the analysis one of them. Analysis is responsible to determine when a data constitutes evidence; and as a consequence it can be presented to a court. When the amount of data in a Network is small, its analysis is relatively simple, but when it is huge the data analysis becomes a challenge for the forensics expert. In this paper a forensics network model is proposed, which allows to obtain the existing evidence in an involved TCP/IP network. This Model uses the Fuzzy Logic and the Artificial Neural Networks to detect the Network flows that realize suspicious activities in the network or hosts, minimizing also the cost and the time to process the information in order to discriminate which are normal network flows and which has been subjected to attacks and intrusions.

Feature selection to detect botnets using machine learning algorithms

In this paper, a novel method to do feature selection to detect botnets at their phase of Command and Control (C&C) is presented. A major problem is that researchers have proposed features based on their expertise, but there is no a method to evaluate these features since some of these features could get a lower detection rate than other. To this aim, we find the feature set based on connections of botnets at their phase of C&C, that maximizes the detection rate of these botnets. A Genetic Algorithm (GA) was used to select the set of features that gives the highest detection rate. We used the machine learning algorithm C4.5, this algorithm did the classification between connections belonging or not to a botnet. The datasets used in this paper were extracted from the repositories ISOT and ISCX. Some tests were done to get the best parameters in a GA and the algorithm C4.5. We also performed experiments in order to obtain the best set of features for each botnet analyzed (specific), and for each type of botnet (general) too. The results are shown at the end of the paper, in which a considerable reduction of features and a higher detection rate than the related work presented were obtained.

Performance analysis of a wireless sensor network with cognitive radio capabilities in structural health monitoring applications: A discrete model:

This article studies the performance of a wireless sensor network with cognitive radio capabilities to gather information about structural health monitoring of buildings in case of seismic activity. Since the use of the local area network is intensive in office and home environments, we propose the use of empty cellular channels (primary system). As such, the structural health monitoring does not degrade the local communications. Thus, the wireless sensor network for structural health monitoring acts as secondary network. Two discrete-time analytical approaches are proposed and developed to evaluate the system performance in terms of both the average packet delay and average energy consumption. The first one is an approximation suitable for the case when the time slot duration is small relative to the mean call inter-arrival time. The second model is accurate for any time slot duration and inter-arrival times.

Possible Keyloggers Without Implementing a Keyboard in Android

Like the main input way to introduce information in the majority mobile devices nowadays is the screen, it is the main source where a malware could get private information. A keylogger, in this way could obtain private information. Researches of this type of malware until this moment are focused on the Android architecture application layer, leaving aside the other layers, so a keylogger could also be implemented in another layer and only use the application layer like the insertion method. An analysis of the data flow when a key is pressed on the screen is presented, from the system call by an interruption caused by hardware, the methods involved in this flow and possible generated logs and related files, performing an experimentation procedure to extract information about the keys pressed in order to determine which points can be used to get private information without the necessity of implement a third-party keyboard.

Design and Implementation of an Electronic Voting System for Choosing the Position of Advisor Student in a University

Due to the importance of choosing a representative, electoral processes have been necessaries, which have evolved with modern society, so the use of information technologies is a useful tool for such evolution. Considering that, this article presents the design and implementation of an Integrated Electronic Voting System (IEVS). The system, which was proved for choosing the position of student advisor at School of Mechanical and Electric Engineering (ESIME), Culhuacan Campus, meets needed requirements for this kind of systems, in addition to speed up the execution time of traditional process and ensure reliable results. All of this is achieved by using different cryptographic primitives such as: encryption, decryption, signature and verification and a hash function, by getting in this way a reliable Electronic Voting System.