Improved detection of Probe Request Attacks : Using Neural Networks and Genetic Algorithm

The Media Access Control (MAC) layer of the wireless protocol, Institute of Electrical and Electronics Engineers (IEEE) 802.11, is based on the exchange of request and response messages. Probe Request Flooding Attacks (PRFA) are devised based on this design flaw to reduce network performance or prevent legitimate users from accessing network resources. The vulnerability is amplified due to clear beacon, probe request and probe response frames. The research is to detect PRFA of Wireless Local Area Networks (WLAN) using a Supervised Feedforward Neural Network (NN). The NN converged outstandingly with train, valid, test sample percentages 70, 15, 15 and hidden neurons 20. The effectiveness of an Intruder Detection System depends on its prediction accuracy. This paper presents optimisation of the NN using Genetic Algorithms (GA). GAs sought to maximise the performance of the model based on Linear Regression (R) and generated R > 0.95. Novelty of this research lies in the fact that the NN accepts user and attacker training data captured separately. Hence, security administrators do not have to perform the painstaking task of manually identifying individual frames for labelling prior training. The GA provides a reliable NN model and recognises the behaviour of the NN for diverse configurations

An ANFIS approach to transmembrane protein prediction

This paper is concerned with transmembrane prediction analysis. Most of novel drug design requires the use of Membrane proteins. Transmembrane protein structure allows pharmaceutical industry to design new drugs based on structural layout. However, laboratory experimental structure determination by X-ray crystallography is difficult to be achieved as the hydrophobic molecules do not crystalize easily. Moreover, the sheer number of proteins demands a computational solution to transmembrane regions identifications. This research therefore presents a novel Adaptive Neural Fuzzy Inference System (ANFIS) approach to predict and analyze of membrane helices in amino acid sequences. The ANFIS technique is implemented to predict membrane helices using sliding window data capturing. The paper uses hydrophobicity and propensity to encode the datasets using the conventional one letter symbol of amino acid residues. The computer simulation results show that the offered ANFIS methodology predicts transmembrane regions with high accuracy for randomly selected proteins

Automating the process of identifying the preferred representational system in Neuro Linguistic Programming using Natural Language Processing

Neuro Linguistic Programming (NLP) is a methodology used for recognition of human behavioral patterns and modification of the behavior. A significant part of this process is influenced by the theory of representational systems which equates to the five main senses. The preferred representational system of an individual can explain a large part of exhibited behaviors and characteristics. There are different methods to recognize the representational systems, one of which is to investigate the sensory-based words in the used language during the conversation. However, there are difficulties during this process since there is not a single reference method used for identification of representational systems and existing ones are subject to human interpretations. Some human errors like lack of experience, personal judgment, different levels of skill and personal mistakes may also affect the accuracy and reliability of the existing methods. This research aims to apply a new approach that is to automate the identification process in order to remove human errors, thereby increasing the accuracy and precision. Natural Language Processing has been used for automating this process, and an intelligent software has been developed to identify the preferred representational system with increased accuracy and reliability. This software has been tested and compared to human identification of representational systems. The results of the software are similar to a NLP practitioner, and the software responds more accurately than a human practitioner in various parts of the process. This novel methodology will assist the NLP practitioners to obtain an improved understanding of their clients’ behavioral patterns and the associated cognitive and emotional processes

Enhancement of capacity, detectability and distortion of BMP, GIF and JPEG images with distributed steganography

The advance of Big Data and Internet growth has driven the need for more abundant storage to hold and share data. People are sending more messages to one another and paying attention to the aspects of privacy and security as opposed to previous decades. One of the types of files that are widely shared and instantaneous available over the web are images. They can become available as soon as a shot is taken and keep this closely related to the owner; it is not easy. It has been proposed here to use Steganography to embed information of the author, image description, license of usage and any other secrete information related to it. Thinking of this, an analysis of the best file types, considering capacity, detectability, and distortion was necessary to determine the best solution to tackle current algorithm weaknesses. The performance of BMP, GIF, and JPEG initialises the process of addressing current weaknesses of Steganographic algorithms. The main weaknesses are capacity, detectability and distortion to secure copyright images. Distributed Steganography technique also plays a crucial part in this experiment. It enhances all the file formats analysed. It provided better capacity and less detectability and distortion, especially with BMP. BMP has found to be the better image file format. The unique combination of Distributed Steganography and the use of the best file format approach to address the weaknesses of previous algorithms, especially increasing the capacity. It will undoubtedly be beneficial for the day to day user, social media creators and artists looking to protect their work with copyright

Protecting against eavesdropping on mobile phones to snip data with Information Security Awareness and Steganography principles

Eavesdropping on Mobile Devices is the primary concern here. Since the mobility of computers, including laptops, tablets, PDAs and smartphone, are demanding and criminals now start to target these devices as the usage is more than desktops. This initial research is focusing on drawing attention to this topic. Let set how to combat mobile interception by criminals tends to investigate whether steganography applications can benefit digital criminals’ interception on such devices. The concentration is on mobile phones interception by criminals to steal personal data; therefore, it consists of developing a framework, mechanism and algorithm to prevent it. The anticipated implications imply Legal and Ethical Issues. Everyone should familiarise and follow this carefully to make sure it does not cause any particular privacy concerns to general individuals. Only personal and authorised devices were used to test the technical work produced by this research

Trapdoor-indistinguishable secure channel free public key encryption with multi-keywords search (student contributions)

Public Key Encryption with Keyword Search (PEKS) enables users to search encrypted messages by a specific keyword without compromising the original data security. Traditional PEKS schemes allow users to search one keyword only instead of multiple keywords. Therefore, these schemes may not be applied in practice. Besides, some PEKS schemes are vulnerable to Keyword Guessing Attack (KGA). This paper formally defines a concept of Trapdoor-indistinguishable Secure Channel Free Public Key Encryption with Multi-Keywords Search (tSCF-MPEKS) and then presents a concrete construction of tSCF-MPEKS. The proposed scheme solves multiple keywords search problem and satisfies the properties of Ciphertext Indistinguishability and Trapdoor Indistinguishability. Its security is semantic security in the random oracle models under Bilinear Diffle-Hellman (BDH) and 1-Bilinear Diffie-Hellman Inversion (1-BDHI) assumptions so that it is able to resist KGA

Cascading classifier application for topology prediction of TMB proteins

This paper is concerned with the use of a cascading classifier for trans-membrane beta-barrel topology prediction analysis. Most of novel drug design requires the use of membrane proteins. Trans-membrane proteins have key roles such as active transport across the membrane and signal transduction among other functions. Given their key roles, understanding their structures mechanisms and regulation at the level of molecules with the use of computational modeling is essential. In the field of bioinformatics, many years have been spent on the trans-membrane protein structure prediction focusing on the alpha-helix membrane proteins. Technological developments have been increasingly utilized in order to understand in more details membrane protein function and structure. Various methodologies have been developed for the prediction of TMB proteins topology however the use of cascading classifier has not been fully explored. This research presents a novel approach for TMB topology prediction. The MATLAB computer simulation results show that the proposed methodology predicts transmembrane topologies with high accuracy for randomly selected proteins

Cybersecurity Index for undergraduate computer science courses in the UK

The paper proposes a novel index to classify how well UK Computer Science courses articulate cybersecurity-related content through their course/module pages. The aim of this work is to raise awareness among British Universities to pay more attention to include and standardize cybersecurity content in computer science courses. Our results show that 80% of analyzed courses scored 1 or 2-stars on a 5-Stars scale. The results also suggest the need for a formal delivery of cybersecurity content from the first year of the courses and possibly in a collaborative manner with the British Computer Society (BCS). To emphasize cybersecurity education in mitigating security lapses, the analogy is: it is better if most people know how to use a match than to train many fire-fighters

Vulnerability exploitations using steganography in PDF files

This article analyses the ways malicious executable files hides with Steganography on the most used files of our daily basis such as PDF, Word, Text, and Image. It demonstrates how data is hidden and gathers innovative ways of identifying potential attacks to prevent them by engaging the safety and exploitation of files distributed online. It is concerned with infected files that can have malicious executable applications embedded, executing itself upon the opening of the original file. Several experiments are detailed exploiting gaps in PDF, email and image files in order to draw awareness to security professionals and Ethical hackers’ trainees

Performance Optimization in Video Transmission over ZigBee using Particle Swarm Optimization

IEEE 802.15.4 - ZigBee is a wireless sensor targeted at applications that require low data rate, low power and inexpensive. IEEE 802.15.4 is limited to a throughput of 250kbps and is designed to provide highly efficient connec-tivity. Hence, IEEE 802.15.4 is not designed to transfer large amounts of da-ta or MPEG-4 as its bandwidth is too low. In engineering and computer sci-ence often use optimization techniques, as do real environment applications in order to overcome complex issues and now this paper a solution has been accomplished by applying Particle Swarm Optimization (PSO) to improve the quality of transmitted MPEG-4 over IEEE 802.15.4. The proposed intelligent system should minimize data loss and distortion. The computer simulation results confirm that applying PSO in video transmission improve the quality of picture and reduce data loss when compared with the conventional MPEG video transmission in ZigBee