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

Continuous implicit authentication for mobile devices based on adaptive neuro-fuzzy inference system

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.As mobile devices have become indispensable in modern life, mobile security is becoming much more important. Traditional password or PIN-like point-of-entry security measures score low on usability and are vulnerable to brute force and other types of attacks. In order to improve mobile security, an adaptive neuro-fuzzy inference system(ANFIS)-based implicit authentication system is proposed in this paper to provide authentication in a continuous and transparent manner. To illustrate the applicability and capability of ANFIS in our implicit authentication system, experiments were conducted on behavioural data collected for up to 12 weeks from different Android users. The ability of the ANFIS-based system to detect an adversary is also tested with scenarios involving an attacker with varying levels of knowledge. The results demonstrate that ANFIS is a feasible and efficient approach for implicit authentication with an average of 95% user recognition rate. Moreover, the use of ANFIS-based system for implicit authentication significantly reduces manual tuning and configuration tasks due to its self-learning capability

Optimum Power Allocation for Average Power Constrained Jammers in the Presense of Non-Gaussian Noise

Cataloged from PDF version of article.We study the problem of determining the optimum power allocation policy for an average power constrained jammer operating over an arbitrary additive noise channel, where the aim is to minimize the detection probability of an instantaneously and fully adaptive receiver employing the Neyman-Pearson (NP) criterion. We show that the optimum jamming performance can be achieved via power randomization between at most two different power levels. We also provide sufficient conditions for the improvability and nonimprovability of the jamming performance via power randomization in comparison to a fixed power jamming scheme. Numerical examples are presented to illustrate theoretical results

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

Multi-Stage Neural Networks with Application to Motion Planning of a Mechanical Snake

An efficient approach to nonlinear control problems in which the plant is to be driven to a desired state using a neural network controller in a number of steps is by representing the whole process as a multi - stage neural network. In this paper, an explicit formulation of the back propagation algorithm is developed for such networks. Later, this approach is used to build up a path planner for a mechanical snake (a robot composed of a sequence of articulated links). This path planner, together with a tracking algorithm, is shown to get the mechanical snake out of a collision-free closed region

Physical Parameters of Some Close Binaries: ET Boo, V1123 Tau, V1191 Cyg, V1073 Cyg and V357 Peg

With the aim of providing new and up-to-date absolute parameters of some close binary systems, new BVR CCD photometry was carried out at the Ankara University Observatory (AUG) for five eclipsing binaries, ET Boo, V1123 Tau, V1191 Cyg, V1073 Cyg and V357 Peg between April, 2007 and October, 2008. In this paper, we present the orbital solutions for these systems obtained by simultaneous light and radial velocity curve analyses. Extensive orbital solution and absolute parameters for ET Boo system were given for the first time through this study. According to the analyses, ET Boo is a detached binary while the parameters of four remaining systems are consistent with the nature of contact binaries. The evolutionary status of the components of these systems are also discussed by referring to their absolute parameters found in this study.Comment: this accepted paper will be published in New Astronom