Random-based Approach Research Articles

DiPri : Distance-based Seed Prioritization for Greybox Fuzzing

Greybox fuzzing is a powerful testing technique. Given a set of initial seeds, greybox fuzzing continuously generates new test inputs to execute the program under test and drives executions with code coverage as feedback. Seed prioritization is an important step of greybox fuzzing that helps greybox fuzzing choose promising seeds for input generation in priority. However, mainstream greybox fuzzers like AFL++ and Zest tend to neglect the importance of seed prioritization. They may pick seeds plainly according to the sequential order of the seeds being queued or an order produced with a random-based approach, which may consequently degrade their performance in exploring code and exposing bugs. In the meantime, existing state-of-the-art techniques like Alphuzz and K-Scheduler adopt complex strategies to schedule seeds. Although powerful, such strategies also inevitably incur great overhead and will reduce the scalability of the proposed technique. In this paper, we propose a novel distance-based seed prioritization approach named DiPri to facilitate greybox fuzzing. Specifically, DiPri evaluates the queued seeds according to seed distances and chooses the outlier ones, which are the farthest from the others, in priority to improve the probabilities of discovering previously unexplored code regions. To make a profound evaluation of DiPri , we prototype DiPri on AFL++ and conduct large-scale experiments with four baselines and 24 C/C++ fuzz targets, where eight are from widely adopted real-world projects, eight are from the coverage-based benchmark FuzzBench, and eight are from the bug-based benchmark Magma. The results obtained through a fuzzing exceeding 50,000 CPU hours suggest that DiPri can (1) insignificantly influence the host fuzzer’s capability of code coverage by slightly improving the branch coverage on the eight targets from real-world projects and slightly reducing the branch coverage on the eight targets from FuzzBench, and (2) improve the host fuzzer’s capability of finding bugs by triggering five more Magma bugs. Besides the evaluation with the three C/C++ benchmarks, we integrate DiPri into the Java fuzzer Zest and conduct experiments on a Java benchmark composed of five real-world programs for more than 8,000 CPU hours to empirically study the scalability of DiPri . The results with the Java benchmark demonstrate that DiPri is pretty scalable and can help the host fuzzer find bugs more consistently.

Multi-objective communication-aware optimization for virtual machine placement in cloud datacenters

This paper formulates a new multi-objective virtual machine placement (VMP) problem, which is a challenging task in cloud datacenters (DCs). In cloud environment, there are two stakeholders, namely, users and providers. Both sides try to take more benefit whereas a trade-off between conflicting benefits is crucial. From providers’ perspective, power consumption and resource wastage are two objectives to be optimized whereas gaining high quality of service (QoS) is a critical point for users. The unpleasant issue that a user endures in the cloud environment is network delay; this is affected by common bandwidth linkage which is shared between different users’ applications; the reason for considering bandwidth usage optimization as the third objective function in users’ viewpoint. However, inefficient network bandwidth usage has drastic impact on overall performance even makes network links to get saturated and throttles communication-intensive applications. Therefore, VMs with high affinity and traffic dependency must be physically placed as close as possible so less traffic is sent on network layers. To figure out this combinatorial multi-objective problem, we extend a hybrid multi-objective genetic-based optimization solution. To evaluate this work, we conducted extensive scenarios with variable correlation coefficients between resources in requested VMs. The simulation results prove that our proposed hybrid meta-heuristic algorithm outperforms against state-of-the-art ACO-based, well-known heuristic-based FFD algorithms, and random-based approach in terms of total power consumption, resource wastage, the total data transfer rate in network, and number of active servers in use. Also, the simulations in larger search space demonstrated proposed approach has high potential of scalability.

Machine learning-based dynamic analysis of Android apps with improved code coverage

This paper investigates the impact of code coverage on machine learning-based dynamic analysis of Android malware. In order to maximize the code coverage, dynamic analysis on Android typically requires the generation of events to trigger the user interface and maximize the discovery of the run-time behavioral features. The commonly used event generation approach in most existing Android dynamic analysis systems is the random-based approach implemented with the Monkey tool that comes with the Android SDK. Monkey is utilized in popular dynamic analysis platforms like AASandbox, vetDroid, MobileSandbox, TraceDroid, Andrubis, ANANAS, DynaLog, and HADM. In this paper, we propose and investigate approaches based on stateful event generation and compare their code coverage capabilities with the state-of-the-practice random-based Monkey approach. The two proposed approaches are the state-based method (implemented with DroidBot) and a hybrid approach that combines the state-based and random-based methods. We compare the three different input generation methods on real devices, in terms of their ability to log dynamic behavior features and the impact on various machine learning algorithms that utilize the behavioral features for malware detection. Experiments performed using 17,444 applications show that overall, the proposed methods provide much better code coverage which in turn leads to more accurate machine learning-based malware detection compared to the state-of- the- art approach.

FoldX as Protein Engineering Tool: Better Than Random Based Approaches?

Improving protein stability is an important goal for basic research as well as for clinical and industrial applications but no commonly accepted and widely used strategy for efficient engineering is known. Beside random approaches like error prone PCR or physical techniques to stabilize proteins, e.g. by immobilization, in silico approaches are gaining more attention to apply target-oriented mutagenesis. In this review different algorithms for the prediction of beneficial mutation sites to enhance protein stability are summarized and the advantages and disadvantages of FoldX are highlighted. The question whether the prediction of mutation sites by the algorithm FoldX is more accurate than random based approaches is addressed.

Optimal Node Deployment and Energy Provision for Wirelessly Powered Sensor Networks

In a typical wirelessly powered sensor network (WPSN), wireless chargers provide energy to sensor nodes by using wireless energy transfer (WET). The chargers can greatly improve the lifetime of a WPSN using energy beamforming by a proper charging scheduling of energy beams. However, the supplied energy still may not meet the demand of the energy of the sensor nodes. This issue can be alleviated by deploying redundant sensor nodes, which not only increase the total harvested energy but also decrease the energy consumption per node provided that an efficient scheduling of the sleep/awake of the nodes is performed. Such a problem of joint optimal sensor deployment, WET scheduling, and node activation is posed and investigated in this paper. The problem is an integer optimization that is challenging due to the binary decision variables and non-linear constraints. Based on the analysis of the necessary condition such that the WPSN be immortal, we decouple the original problem into a node deployment problem and a charging and activation scheduling problem. Then, we propose an algorithm and prove that it achieves the optimal solution under a mild condition. The simulation results show that the proposed algorithm reduces the needed nodes to deploy by approximately 16%, compared with a random-based approach. The simulation also shows that if the battery buffers are large enough, the optimality condition will be easy to meet.

A novel laminate analogy to calculate the strength of two-dimensional randomly oriented short-fiber composites

In this paper, a new laminate analogy for progressive damage modeling of two-dimensional randomly oriented short-fiber composites is developed. In the present model, two-dimensional randomly oriented short-fiber composite is replaced by an equivalent laminated composite, which contain several unidirectional layers oriented between 0° to 180°. An incremental algorithm is presented to simulate the stress-strain behavior of composites up to the final failure. In the first step, using the shear-lag theory and the Halpin-Tsai method, the on-axis stiffness and strength of each layer were calculated. Then, using the Tsai-Wu failure criterion, damaged layers were detected and the residual moduli of damaged layers were calculated by a random based approach. After failure of each layer, undamaged layers must sustain more stresses. Therefore, the continuum damage mechanics was used to calculate the effective stress in each load increment. A comparison of results of the present model with experimental data available in the literature shows the capability of the model.

Welfare implications of power rationing: An application to Germany

This study analyzes the economic consequences of power rationing at the level of German counties. Based on a common production function approach, it estimates the costs of power outages for firms and households in all counties and uses these estimates to derive hypothetical rationing plans for the scenario of a country-wide shortage of power supply. Rationing plans are introduced according to several criteria: a random-based approach, a criterion of total cost minimization, a criterion minimizing costs of the most affected region as well as a criterion minimizing the number of people affected. The implementation of each criterion is simulated for different times of the day. The spatial patterns of rationing prove to be heterogenous. Estimated cost structures differ to some extent as well. The results are discussed with regard to their enforceability in the context of the German energy transition.

Application of Compressive Sensing in LFMCW Radar

Compressive Sensing (CS) theory is a newly developed theory which combines the signal sampling and compression based on the sparsity characteristics of the signal. Applying CS theory in radar signal processing may lead to a reduction in sampling rate, complexity, power consumption, and cost. On the other hand, performance is a critical point to be considered. In the present paper, an important question of the worthy of applying CS in the signal processing of Linear Frequency Modulated Continuous Wave (LFMCW) radar is considered. Two approaches of CS are considered; Nyquist rate based approach, and pseudo random based approach. The detection performance of LFMCW radar signal processor using CS based approaches is compared to the traditional one which is based on Fast Fourier Transform (FFT) through Receiver Operating Characteristics (ROC) curves. Comparative analysis between CS approaches and the traditional one regarding the performance and complexity is presented.

Review of an Improved Audio Steganographic Technique over LSB through Random Based Approach

In this digital world, huge amount information exchange takes place due to enhanced facilities of networking. Therefore it is necessary to secure the information which we transmit. The need for secured communication introduces the concept of Steganography. Steganography, the word itself indicates that information within information; it is the best technique to hide the secret information by using cover objects. Secret information may be a text, image or an audio file. But as per secret information format there are different steganographic techniques are available. This paper proposes a method of audio steganographic system that provides a unique platform to hide the secret information in audio file though the information is in text, image or in an audio format. So there is no need to go for different techniques of steganography as per information format. Many steganographic methods follow the LSB insertion technique to hide the secret information. But there are many statistical techniques available to determine if a stego object has been subjected to LSB Embedding. The proposed system hides secret information in audio file through random based approach and provides security by using PKE algorithm. This paper focuses on combining the strengths of cryptography and steganography for secured communication.

Design and Automatic System Verification of Digital Baseband for UHF RFID Tag

This paper presents the design and automatic system verification of digital baseband for Ultra High Frequency (UHF) radio frequency identification (RFID) tag, which is complied with a modified ISO 18000-6C protocol. Module-reuse approach and low power techniques are applied in the digital baseband to reduce the power consumption. And a novel verification strategy is discussed, which decreases the verification cycle greatly via function test mode and coverage test mode, and generates testcases automatically by using coverage-driven random-based approach. The strategy has many merits, such as a hierarchical architecture for reuse, inspecting low power design though assertion, locating bugs accurately, and linking C++ via direct programming interface (DPI). The tag chip is designed in a 0.18um CMOS process with a size of 89234 um. Simulation results verify the efficiency of the proposed methods. 