Developers of resource-allocation and scheduling algorithms share test datasets (i.e., benchmarks) to enable others to compare the performance of newly developed algorithms. However, mostly it is hard to acquire real cloud datasets due to the users’ data confidentiality issues and policies maintained by Cloud Service Providers (CSP). Accessibility of large-scale test datasets, depicting the realistic high-performance computing requirements of cloud users, is very limited. Therefore, the publicly available real cloud dataset will significantly encourage other researchers to compare and benchmark their applications using an open-source benchmark. To meet these objectives, the contemporary state of the art has been scrutinized to explore a real workload behavior in Google cluster traces. Starting from smaller- to moderate-size cloud computing infrastructures, the dataset generation process is demonstrated using the Monte Carlo simulation method to produce a Google Cloud Jobs (GoCJ) dataset based on the analysis of Google cluster traces. With this article, the dataset is made publicly available to enable other researchers in the field to investigate and benchmark their scheduling and resource-allocation schemes for the cloud. The GoCJ dataset is archived and available on the Mendeley Data repository.

Distributed denial of service (DDoS) attacks as well as botnet-based attacks are among the most important security vulnerabilities in Internet of Things (IoT) environments. Most of the existing research approaches use centralized defense mechanisms to prevent DDoS attacks in IoT environments. However, it is important to provide a reliable and scalable solution to prevent DDoS attacks. Combining technologies such as distributed blockchain-based mechanisms and smart contracts facilitates the construction of a trusted distributed framework that can defend against DDoS attacks in IoT. In this article, we have proposed a multilevel DDoS mitigation approach (ML-DDoS) to protect IoT devices and other computing resources or machines using the blockchain-based framework. The core concept of the proposed system is to use a device-based verification mechanism using blockchain and exclude malicious devices from IoT environments. The proposed framework was developed using Hyperledger Caliper (a blockchain benchmark tool) and its performance was evaluated using three benchmark applications. Compared to the state of the art, the results show that the proposed framework achieves up to 35% improvement in throughput, up to 40% improvement in latency, and up to 25% better utilization of CPU.

With the rise in popularity and usage of Android operating systems, malicious applications are targeted by applying innovative ways and techniques. Today, malware becomes intelligent that uses several ways of obfuscation techniques to hide its functionality and evade anti-malware engines. For mainstream smartphone users, Android malware poses a severe security danger. An obfuscation approach, however, can produce malware versions that can evade current detection strategies and dramatically lower the detection accuracy. Attempting to identify Android malware obfuscation variations, this paper proposes an approach to address the challenges and issues related to the classification and detection of malicious obfuscated variants. The employed detection and classification scheme uses both static and dynamic analysis using an ensemble voting mechanism. Moreover, this study demonstrates that a small subset of features performs consistently well when they are derived from the basic malware (non-obfuscated), however, after applying a novel feature-based obfuscation approach, the study shows a drastic change indicating the relative importance of these features in obfuscating benign and malware applications. For this purpose, we present a fast, scalable, and accurate mechanism for obfuscated Android malware detection based on the Deep learning algorithm using real and emulator-based platforms. The experiments show that the proposed model detects malware effectively and accurately along with the identification of features that are usually obfuscated by malware attackers.

The technology works as a catalyst through which many changes occur in the learning approaches, teaching methodologies, research arena, in the work environment and in the use of information and knowledge. Helping students to learn using technological tools rather than using conventional...