As any software system, a self-adaptive system is subject to security threats. However, applying self-adaptation may introduce additional threats. So far, little research has been devoted to this important problem. In this paper, we propose an approach for vulnerability analysis of architecturebased adaptations in self-adaptive systems using threat modeling and analysis techniques. To this end, we specify components' vulnerabilities and the system architecture formally and generate an attack model that describes the attacker's strategies to attack the system by exploiting different types of vulnerabilities. We use a set of security metrics to quantitatively assess the security risks of adaptations based on the produced attack model which enables the system to consider security aspects while choosing an adaptation to apply to the system. We automate and incorporate our approach into the Rainbow framework, allowing secure architectural adaptations at runtime. To evaluate the effectiveness of our approach, we apply it on a simple document storage system and on the ZNN system.

Adaptive identification of the bandpass finite impulse response (FIR) filtering system is proposed through this paper using variable step-size least mean square (VSS-LMS) algorithm called absolute average error-based adjusted step-size LMS as an adapted algorithm. This algorithm used to design an adaptive FIR filter by calculating the absolute averaged value for the recently assessed error with the previous one. Then, the step size has been attuned accordingly with consideration of the slick transition of the step size from bigger to smaller to score an achievement through high convergence rate and low steady-state misadjustment over the other algorithms used for the same purpose. The simulation results through the computer demonstrate remarkable performance compared to the traditional algorithm of LMS and another VSS-LMS algorithm (normalized LMS) which used in this paper for the designed filter. The powerful of the filter has been served in the identification system, bandpass filter has been chosen to be identified in the proposed adaptive system identification. It reports conceivable enhancements in the modeling system concerning the time of convergence, which is well-defined as a fast and steady-state adjustment defined with a low level. The designed filter identified the indefinite system with less than 10 samples; meanwhile, other algorithms were taking more than 20 samples for identification. Alongside the fine behavior of preserving the tradeoff between miss adjustment and the capability of tracking, the fewer calculations and computations regarding the algorithm requirement make the applied real-time striking.

Different distributed generation (DG) technologies, active loads, and storage devices create an independent microgrid (MG). Scheduling of an MG is an important issue in renewable energy sources (RESs) based systems. In this paper, MGs include RESs, plug-in hybrid electric vehicles (PHEVs), and electrical energy storage systems. The proposed scheduling framework utilizes the Monte Carlo simulation (MCS) to characterize the uncertain parameters of PHEVs and RESs. Three different charging strategies are investigated for modeling the impact of different behaviors of PHEVs in MGs. These schemes are smart, controlled, and uncontrolled charging. Due to the nonlinear feature of the suggested optimization problem, it needs an efficient optimization tool to tackle the problem appropriately. So, this paper uses the backtracking search optimization (BSO) algorithm for the short-term scheduling of an MG. The proper performance of the offered scheme is investigated in two scenarios with different time horizons. The BSO algorithm and other optimization algorithms are used for comparing the results to verify the presented method in solving the energy management problem of the MGs.

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In most pattern recognition models, the accuracy of the recognition plays a major role in the efficiency of those models. The feature extraction phase aims to sum up most of the details and findings contained in those patterns to be informational and non-redundant in a way that is sufficient to fen to the used classifier of that model and facilitate the subsequent learning process. This work proposes a highly accurate offline handwritten English alphabet (OHEAR) model for recognizing through efficiently extracting the most informative features from constructed self-collected dataset through three main phases: Pre-processing, features extraction, and classification. The features extraction is the core phase of OHEAR based on combining both statistical and structural features of the certain alphabet sample image. In fact, four feature extraction portions, this work has utilized, are tracking adjoin pixels, chain of redundancy, scaled-occupancy-rate chain, and density feature. The feature set of 27 elements is constructed to be provided to the multi-class support vector machine (MSVM) for the process of classification. The OHEAR resultant revealed an accuracy recognition of 98.4%.