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

N-opcode Analysis for Android Malware Classification and Categorization

The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.Malware detection is a growing problem particularly on the Android mobile platform due to its increasing popularity and accessibility to numerous third party app markets. This has also been made worse by the increasingly sophisticated detection avoidance techniques employed by emerging malware families. This calls for more effective techniques for detection and classification of Android malware. Hence, in this paper we present an n-opcode analysis based approach that utilizes machine learning to classify and categorize Android malware. This approach enables automated feature discovery that eliminates the need for applying expert or domain knowledge to define the needed features. Our experiments on 2520 samples that were performed using up to 10-gram opcode features showed that an f-measure of 98% is achievable using this approach

The Relationship of Amelogenesis Imperfecta and Nephrocalcinosis Syndrome

Aim: To analyze the prevalence and associated oral findings of nephrocalcinosis in a group of patients affected with amelogenesis imperfecta (AI). The relationship between types of AI and nephrocalcinosis were also evaluated. Design: This study examines patients who were referred to Pediatric Dentistry Department of SDU between the years of 2002-2007 and who, upon clinical and radiological examination, were diagnosed with AI and treated. Patients were offered information about the possibility of nephrocalcinosis syndrome. Patients who agreed to have tests carried out on their renal system were advised to visit the department of nephrology at the clinic. Results: Suspicious radiopacity was observed during renal ultrasonography of a controlled number of patients with hypoplastic type AI. Laboratory results revealed low Ca values (100-300 mg/days) and normal P values (0.4-1.3 g/days). Delayed eruption, gingival hyperplasia, pulp stones and orthodontic problems were also observed in the same patient groups. Conclusion: Although renal findings were observed in a few patients, pediatric dentists are the doctors who are the first to have early contact with this patient group. Because of the potential risk of nephrocalcinosis, early diagnosis may offer good prognosis

Optimum feedback patterns in multivariable control systems

The following problem is considered: 'Given a multivariable system with nl inputs and r outputs and an external matrix whose non-negative (i, j)th element represents the cost of setting LIP a feedback link from the, jth output to the ith input, find a set of feedback links with minimum total cost, which does not give rise to fixed modes'. Utilizing the graph-theoretic churiicterization of structurally fixed modes, the problem is decomposed into two subproblems. which are then solved by using concepts and results from network theory. A combination of the optimum solutions of the subproblems provides a suboptimal solution to the original problem. © 1989 Taylor & Francis Group, LLC

Close Binary System GO Cyg

In this study, we present long term photometric variations of the close binary system \astrobj{GO Cyg}. Modelling of the system shows that the primary is filling Roche lobe and the secondary of the system is almost filling its Roche lobe. The physical parameters of the system are $M_1 = 3.0\pm0.2 M_{\odot}$, $M_2 = 1.3 \pm 0.1 M_{\odot}$, $R_1 = 2.50\pm 0.12 R_{\odot}$, $R_2 = 1.75 \pm 0.09 R_{\odot}$, $L_1 = 64\pm 9 L_{\odot}$, $L_2 = 4.9 \pm 0.7 L_{\odot}$, and $a = 5.5 \pm 0.3 R_{\odot}$. Our results show that \astrobj{GO Cyg} is the most massive system near contact binary (NCB). Analysis of times of the minima shows a sinusoidal variation with a period of $92.3\pm0.5$ years due to a third body whose mass is less than 2.3$M_{\odot}$. Finally a period variation rate of $-1.4\times10^{-9}$ d/yr has been determined using all available light curves.Comment: Accepted for publication in New Astronomy, 18 pages, 4 figures, 7 table

Multi-Attribute SCADA-Specific Intrusion Detection System for Power Networks

The increased interconnectivity and complexity of supervisory control and data acquisition (SCADA) systems in power system networks has exposed the systems to a multitude of potential vulnerabilities. In this paper, we present a novel approach for a next-generation SCADA-specific intrusion detection system (IDS). The proposed system analyzes multiple attributes in order to provide a comprehensive solution that is able to mitigate varied cyber-attack threats. The multiattribute IDS comprises a heterogeneous white list and behavior-based concept in order to make SCADA cybersystems more secure. This paper also proposes a multilayer cyber-security framework based on IDS for protecting SCADA cybersecurity in smart grids without compromising the availability of normal data. In addition, this paper presents a SCADA-specific cybersecurity testbed to investigate simulated attacks, which has been used in this paper to validate the proposed approach