The V-network: a testbed for malware analysis

This paper presents a virtualised network environment that serves as a stable and re-usable platform for the analysis of malware propagation. The platform, which has been developed using VMware virtualisation technology, enables the use of either a graphical user interface or scripts to create virtual networks, clone, restart and take snapshots of virtual machines, reset experiments, clean virtual machines and manage the entire infrastructure remotely. The virtualised environment uses open source routing software to support the deployment of intrusion detection systems and other malware attack sensors, and is therefore suitable for evaluating countermeasure systems before deployment on live networks. An empirical analysis of network worm propagation has been conducted using worm outbreak experiments on Class A size networks to demonstrate the capability of the developed platform

Early detection and containment of network worm

This paper presents a network security framework for containing the propagation of network worms. The framework employs a detection mechanism at the network layer to identify the presence of a network worm and a data-link containment solution to block the infected host. A prototype of the mechanism has been used to demonstrate the effectiveness of the developed framework. An empirical analysis of network worm propagation has been conducted to test the framework. The results show that the developed framework is effective in containing network worms with almost no false positives

Early containment of fast network worm malware

This paper presents a countermeasure mechanism for the propagation of fast network worm malware. The mechanism uses a cross layer architecture with a detection technique at the network layer to identify worm infection and a data-link containment solution to block an identified infected host. A software prototype of the mechanism has been used to demonstrate its effective. An empirical analysis of network worm propagation has been conducted to test the mechanism. The results show that the developed mechanism is effective in containing self-propagating malware with almost no false positives

Botnet detection from drive-by downloads

The advancement in Information Technology has brought about an advancement in the development and deployment of malware. Bot Malware have brought about immense compromise in computer security. Various ways for the deployment of such bots have been devised by attackers and they are becoming stealthier and more evasive by the day. Detecting such bots has proven to be difficult even though there are various detection techniques. In this work, a packet capturing and analysis technique for detecting host-based bots on their characteristics and behavior is proposed. The system captures network traffic first, to establish normal traffic, then already captured botnet traffic was used to test the system. The system filters out HTTP packets and analyses these packets to further filter out botnet traffic from normal internet traffic. The system was able to detect malicious packets with a False Positive Rate of 0.2 and accuracy of 99.91%

An assessment of the contemporary threat posed by network worm malware

The cost of a zero-day network worm outbreak has been estimated to be up to US$2.6 billion. Additionally zeroday network worm outbreaks have been observed that spread at a significant pace across the global Internet, with an observed infection level of more than 90 percent of vulnerable hosts within 10 minutes. The threat posed by such fast-spreading malware is therefore significant, particularly given the fact that network operator / administrator intervention is not likely to take effect within the typical epidemiological timescale of such infections. This paper presents a classification of wormable vulnerabilities, demonstrating a method to determine if a vulnerability is wormable, and presents a survey into the cause of the reduction of worm outbreaks in recent years, as well as their viability in the future. It then goes on to explore recent wormable vulnerabilities, and points out the issues with operating system security in relation to techniques used by zero-day worms

Validating measures of driver behavior’s training factors for prime decision-making

This paper validates the Driver Behavior’s Training Instrument (BDTI) for measuring training factors that influence prime decision-making in a driving domain. First, the training factors were developed to evaluate Computational Rabi’s Driver Training (C-RDT) model for prime decision-making in driving. In order to validate the model, a three-phase validation method has been used in this paper. In the first phase, items were generated from the literature to measure driver behavior’s training factors. In the 2nd phase, 4 academic experts and 3 experts from a driving institution were consulted for face and content validity. A Content Validity Index (CVI) of both the items-level and the scale-level CVIs was conducted from the ratings of the seven (7) experts. Finally, the items were subjected to a reliability test and an Exploratory Factor Analysis (EFA) with Varimax rotation in the 3rd phase. The findings presented in this study revealed 10 valid scales for measuring driver behavior’s training factors namely; basic skills, basic practice, sensory ability, driving goal, driving intention, potential hazardous information, exposure to task complexity, perception about risk, driving knowledge, and involuntary/voluntary automaticity. The scales validated in this paper should assist other model developers; particularly driver behavior’s training modelers to validate their factors for prime decision-making. In literature the measures of driver behavior and training factors that influence drivers’ prime decision are limited. Hence, this paper considers the validation of driver behavior’s training instrument that measures the training factors for prime decision-making important

Deep Sequence Models for Text Classification Tasks

The exponential growth of data generated on the Internet in the current information age is a driving force for the digital economy. Extraction of information is the major value in an accumulated big data. Big data dependency on statistical analysis and hand-engineered rules machine learning algorithms are overwhelmed with vast complexities inherent in human languages. Natural Language Processing (NLP) is equipping machines to understand these human diverse and complicated languages. Text Classification is an NLP task which automatically identifies patterns based on predefined or undefined labeled sets. Common text classification application includes information retrieval, modeling news topic, theme extraction, sentiment analysis, and spam detection. In texts, some sequences of words depend on the previous or next word sequences to make full meaning; this is a challenging dependency task that requires the machine to be able to store some previous important information to impact future meaning. Sequence models such as RNN, GRU, and LSTM is a breakthrough for tasks with long-range dependencies. As such, we applied these models to Binary and Multi-class classification. Results generated were excellent with most of the models performing within the range of 80% and 94%. However, this result is not exhaustive as we believe there is room for improvement if machines are to compete with humans

L’ETHIQUE EN MATIERE DE SANTE ENTRE ANTINOMIES, LIBERTE DE CHOIX ET DIFFICULTE DU QUOTIDIEN

The progressive increase of biotechnology, of more and more sophisticated and customized drugs, springs from a real requirement of citizens or instead from an offer coming from different corporations? Ethics in health care is everyday more contradictory, permeated by antinomies, freedom of choice, inequalities and problems connected to everyday life

Yield and Yield Attributes of Extra-early Maize (Zea Mays L.) as Affected by Rates of Npk Fertilizer Succeeding Chilli Pepper (Capsicum Frutescens) Supplied with Different Rates Sheep Manure

Field experiment was conducted in 2005 and 2006 to study response of extra-early maize variety (95TZEE-Y1) to rates of NPK (0, 40:20:20, 80:40:40 and 120:60:60 kg N:P2O5:K2O ha-1) and residual FYM (0, 5, 10 and 15 t ha-1 applied to chilli pepper the previous season) in the semi-arid zone of Nigeria. Randomized complete block design with three replicates was used. Higher values for soil physical and chemical properties were obtained in plots supplied with manure the previous season with soil from 2006 experiment more fertile than for the first year, hence produced 21% more grain yield. All the applied NPK rates in 2005 and except 40:20:20 ha1 in 2006 had resulted in early maize crop as compared to control. Husked and de-husked cob and 100-grain weights and grain yield/ha were higher at 120:60:60 kg NPK ha-1. Maize grown in plot supplied with 15 t FYM ha1 the previous year matured earlier. Cobs and 100-grain weights and grain yield were highest in plot supplied with 10 t FYM ha1. The 10t FYM ha-1 had 69% and 68% more grain yield than the control in 2005 and 2006, respectively. Highest maize yield was obtained at 120:60:60 kg NPK ha-1 or 10t FYM ha-1. All the parameters measured significantly and positively related to each other when the two years data were combined

Knowledge of health effects and determinants of psychoactive substance use among secondary school students in Sokoto Metropolis, Nigeria

Introduction: psychoactive substance use (PSU) is a patterned use of a drug in which the user consumes the substance in amounts or methods which are harmful to themselves or others. Psychoactive substance use takes a considerable toll on financial status, academic achievement and health status of addicts. In Nigeria, PSU is on the increase, one of the most disturbing health-related problems and a leading cause of premature death among school aged population worldwide. We therefore, determined the knowledge of health effects and determinants of psychoactive substance use among secondary school students in Sokoto Metropolis, Nigeria. Methods: we conducted a cross-sectional study among 430 secondary school students that were selected using multistage sampling in Sokoto, Northwestern, Nigeria from April to May 2019. We collected data using a semi-structured, interviewer-administered questionnaire. We calculated proportions and adjusted odds ratios (OR) with 95% confidence intervals (CI) in a binary logistic regression model. Results: knowledge of health effects of PSU was good in 38.1% of the respondents with a mean score of 19.6 ± 10.0. The overall prevalence of PSU was high among current users (16.3%), male participants (78.6%) and those aged 17-years or more (68.6%). Independent predictors of current use of psychoactive substances were poor knowledge of health effects (aOR: 4.1, 95% CI: 1.7-10.0) and father´s use of psychoactive substances (aOR: 10.3, 95% CI= 1.9-57.1). Conclusion: knowledge of health effects of psychoactive substances was generally poor among the participants with an associated high prevalence among current users. Poor knowledge of its health effects determines the use of psychoactive substances. We conducted awareness campaigns and health talk on health effects of PSU to secondary school students in the state. The Federal Ministry of Education should ensure that PSU-related topics are incorporated in the secondary school curriculum