Longitudinal performance analysis of machine learning based Android malware detectors

This paper presents a longitudinal study of the performance of machine learning classifiers for Android malware detection. The study is undertaken using features extracted from Android applications first seen between 2012 and 2016. The aim is to investigate the extent of performance decay over time for various machine learning classifiers trained with static features extracted from date-labelled benign and malware application sets. Using date-labelled apps allows for true mimicking of zero-day testing, thus providing a more realistic view of performance than the conventional methods of evaluation that do not take date of appearance into account. In this study, all the investigated machine learning classifiers showed progressive diminishing performance when tested on sets of samples from a later time period. Overall, it was found that false positive rate (misclassifying benign samples as malicious) increased more substantially compared to the fall in True Positive rate (correct classification of malicious apps) when older models were tested on newer app samples

A Daily Dose of Wisdom: Globalization and SMS Proverbs in Nigeria

Globalization ensures connectedness, the sharing of knowledge across nations and across continents. This is facilitated mainly by growth in information technology which, to a large extent, is dominated by the developed world. The SMS text message is a product of technology as people can send written messages through their cell phones. The language of text messages is of necessity brief and full of abbreviations and symbols because the cell phone SMS facility has a limited capacity for containing written texts. In relation to artistic production the SMS facility is grossly inadequate. It is however appropriate for containing and relating gnomic narrative forms such as the proverb. This paper acknowledges the receipt of SMS proverbs on a daily basis from centre 5020 and centre 5810. The paper looks at one year’s collection of such proverbs, analyses its distribution, and examines its form, particularly the pseudo-proverbs that are presented alongside the proverbs and comments on some of the major themes embedded in them. The paper concludes that modern technology is a career of culture and that a developing world like Nigeria needs to be an active participant in the global world lest it be culturally swallowed by the technologically advanced countries

High Accuracy Phishing Detection Based on Convolutional Neural Networks

The persistent growth in phishing and the rising volume of phishing websites has led to individuals and organizations worldwide becoming increasingly exposed to various cyber-attacks. Consequently, more effective phishing detection is required for improved cyber defence. Hence, in this paper we present a deep learning-based approach to enable high accuracy detection of phishing sites. The proposed approach utilizes convolutional neural networks (CNN) for high accuracy classification to distinguish genuine sites from phishing sites. We evaluate the models using a dataset obtained from 6,157 genuine and 4,898 phishing websites. Based on the results of extensive experiments, our CNN based models proved to be highly effective in detecting unknown phishing sites. Furthermore, the CNN based approach performed better than traditional machine learning classifiers evaluated on the same dataset, reaching 98.2% phishing detection rate with an F1-score of 0.976. The method presented in this pa-per compares favourably to the state-of-the art in deep learning based phishing website detection

A Daily Dose of Wisdom: Globalization and SMS Proverbs in Nigeria

Globalization ensures connectedness, the sharing of knowledge across nations and across continents. This is facilitated mainly by growth in information technology which, to a large extent, is dominated by the developed world. The SMS text message is a product of technology as people can send written messages through their cell phones. The language of text messages is of necessity brief and full of abbreviations and symbols because the cell phone SMS facility has a limited capacity for containing written texts. In relation to artistic production the SMS facility is grossly inadequate. It is however appropriate for containing and relating gnomic narrative forms such as the proverb. This paper acknowledges the receipt of SMS proverbs on a daily basis from centre 5020 and centre 5810. The paper looks at one year’s collection of such proverbs, analyses its distribution, and examines its form, particularly the pseudo-proverbs that are presented alongside the proverbs and comments on some of the major themes embedded in them. The paper concludes that modern technology is a career of culture and that a developing world like Nigeria needs to be an active participant in the global world lest it be culturally swallowed by the technologically advanced countries

DL-Droid: Deep learning based android malware detection using real devices

open access articleThe Android operating system has been the most popular for smartphones and tablets since 2012. This popularity has led to a rapid raise of Android malware in recent years. The sophistication of Android malware obfuscation and detection avoidance methods have significantly improved, making many traditional malware detection methods obsolete. In this paper, we propose DL-Droid, a deep learning system to detect malicious Android applications through dynamic analysis using stateful input generation. Experiments performed with over 30,000 applications (benign and malware) on real devices are presented. Furthermore, experiments were also conducted to compare the detection performance and code coverage of the stateful input generation method with the commonly used stateless approach using the deep learning system. Our study reveals that DL-Droid can achieve up to 97.8% detection rate (with dynamic features only) and 99.6% detection rate (with dynamic + static features) respectively which outperforms traditional machine learning techniques. Furthermore, the results highlight the significance of enhanced input generation for dynamic analysis as DL-Droid with the state-based input generation is shown to outperform the existing state-of-the-art approaches

Transcendent Kingdom

No Abstract

Psoriatic arthritis mutilans in a black Nigerian patient: a case report

Psoriatic Arthritis Mutilans (PAM) is a rare destructive form of arthritis, especially in blacks and its diagnosis and treatment still remains a challenge. Case Report: A 55-year-old house wife with 30-years history of psoriatic skin lesions, developed swellings and pain of the small joints of the hands and feet, wrists, elbows, shoulders and knees associated with low back pain and alternating buttock pain shortly after onset of the rashes. She had dystrophic, pitting, yellowish nail changes with sub-ungual hyperkeratosis. The joint swellings rapidly progress into shortening of the digits of her hands and feet, with resorption of the interphalangeal joints and subluxation of the metarcarpophalangeal joints (MCPJs). Her Body Mass Index at presentation was 19.8kg/m, she had subluxation of the MCPJs with shortening and telescoping of the 3 , 4 and 5 digits of both hands and bilateral knee swelling with ankylosis. Her rheumatoid factor was negative, CRP was 36mg/dl, white cell count of 3.0×10 cells/L with predominant lymphocytosis (63%). Her serum urea, creatinine and uric acid were normal. Radiographs of the hands and feet showed 'pencil in cup' appearance with marked periosteal reaction and osteolysis, complete joint erosion and subluxation. A diagnosis of PAM was made using CASPAR) criteria. She was placed on methotrexate 10mg weekly and topical steroids with short course of naproxen and was advised for total knee replacement and biologic agents for her treatment. Conclusion: ClAS sification for Psoriatic AR thritis Conclusion: PAM still remains a challenge in terms of early detection of the characteristic phenotype and treatment

High Accuracy Detection of Mobile Malware Using Machine Learning

open access articleAs smartphones and other mobile and IoT devices have become pervasive in everyday life, malicious software (malware) authors are increasingly targeting the operating systems that are at the core of these mobile systems. Malware targeting mobile platforms has witnessed an explosive growth in the last decade. As a result of this rapid increase in mobile malware, the limits of traditional signature-based antivirus scanning have been stretched. This has led to the emergence of machine learning-based detection as a complementary solution to traditional antivirus scanning. Although machine learning-based malware detection has continued to attract great research interest, many challenges remain as emerging malware families continue to evolve with more sophisticated capabilities and stealthy evasive techniques. This Special Issue in Electronics presents some of the most recent research results and innovative machine learning-based approaches to detecting malicious software and attacks that can compromise mobile platforms