Cluster Computing

Latif, Rana M. Amir; Farhan, Muhammad; Rizwan, Osama; Hussain, Majid; Jabbar, Sohail; Khalid, Shahzad

doi: 10.1007/s10586-020-03165-4pmid: N/A

In any business network for record transactions, validation, and track assets, we have used a Blockchain platform, which is shared a distributed ledger that used cryptography techniques. Globally, there are different rules and operating procedures in the supply chain network. These regulations are used for end-to-end tracking between industries of different countries. The major problem faced today is asset traceability, and it is addressed with the usage of Blockchain technology. Greater asset traceability is provided using this technology. Throughout this research study, we suggest a commodity traceability network focused on blockchain technologies, which permanently stores all commodity history in a global database by way of smart contracts and creates a chain that can trace back to the source of goods. In particular, we built an incident response system to check the parties’ identity and ensure the legitimacy of the transaction. And all events are stored permanently in the form of logs to manage disputes and track accountable entities. Also, a device prototype is designed utilizing truffle research nets. Initially, a user login form is developed to make a part of the Blockchain, followed with a function to add an entry of the stock-keeping unit (SKU) into the Blockchain. The SKU is used for adding the product or item into the Blockchain, i.e., the product information is added like “Dairy Milk”, then one transaction is complete. The local languages like Urdu and Hindi are supported the SKU. All users of the retail network can be accessed the SKU “Id” of that product, and it can update the status of the product. So, the history of the product is saved in the block until the customer block in-network, and the customer can access it by using the QR scan code. Customer confidence is increased, and customer satisfaction reflects in sales.

Siddiqui, Abdul Jabbar; Boukerche, Azzedine

doi: 10.1007/s10586-020-03153-8pmid: N/A

In the recent years, the Internet of Things has been becoming a vulnerable target of intrusion attacks. As the academia and industry move towards bringing the Internet of Things (IoT) to every sector of our lives, much attention needs to be given to develop advanced Intrusion Detection Systems (IDS) to detect such attacks. In this work, we propose a novel network-based intrusion detection method which learns patterns of benign flows in a temporal codebook. Based on the temporally learnt codebook, we propose a feature representation method to transform the raw flow-based statistical features into more discriminative representations, called TempoCode-IoT. We develop an ensemble of machine learning-based classifiers optimized to discriminate the malicious flows from the benign ones, based on the proposed TempoCode-IoT. The effectiveness of the proposed method is empirically evaluated on a state-of-the-art realistic intrusion detection dataset as well as on a real botnet-infected IoT dataset, achieving high accuracies and low false positive rates across a variety of intrusion attacks. Moreover, the proposed method outperforms several state-of-the-art works based on the used datasets, proving the effectiveness of Tempo-Code-IoT over raw flow features, both in terms of accuracies and processing speeds.

Alfandi, Omar; Khanji, Salam; Ahmad, Liza; Khattak, Asad

doi: 10.1007/s10586-020-03137-8pmid: N/A

The constant development of interrelated computing devices and the emergence of new network technologies have caused a dramatic growth in the number of Internet of Things (IoT) devices. It has brought great convenience to people’s lives where its applications have been leveraged to revolutionize everyday objects connected in different life aspects such as smart home, healthcare, transportation, environment, agriculture, and military. This interconnectivity of IoT objects takes place through networks on centralized cloud infrastructure that is not constrained to national or jurisdictional boundaries. It is crucial to maintain security, robustness, and trustless authentication to guarantee secure exchange of critical user data among IoT objects. Consequently, blockchain technology has recently emerged as a tenable solution to offer such prominent features. Blockchain’s secure decentralization can overcome security, authentication, and maintenance limitations of current IoT ecosystem. In this paper we conduct a comprehensive literature review to address recent security and privacy challenges related to IoT where they are categorized according to IoT layered architecture: perception, network, and application layer. Further, we investigate blockchain technology as a key pillar to overcome many of IoT security and privacy problems. Additionally, we explore the blockchain technology and its added values when combined with other new technologies as machine learning especially in intrusion detection systems. Moreover, we highlight challenges and privacy issues resulted due to integration of blockchain in IoT applications. Finally, we propose a framework of IoT security and privacy requirements via blockchain technology. Our main contribution is to exhaust the literature to highlight the recent IoT security and privacy issues and how blockchain can be utilized to overcome these issues, nevertheless; we address challenges and open security issues that blockchain may impose on the current IoT systems. Research findings formulate a rigid foundation upon which an efficient and secure adoption of IoT and blockchain is highlighted accordingly.

Nespoli, Pantaleone; Gómez Mármol, Félix; Maestre Vidal, Jorge

doi: 10.1007/s10586-020-03198-9pmid: N/A

Cyberattacks targeting ICT systems are becoming every day more sophisticated and disruptive. Such malevolent actions are performed by ill-motivated entities (governments, states, administrations, etc.), often featuring almost unlimited resources, but also by skilled individuals due to the accessibility of the attacks source code. In this alarming scenario, the selection of the optimal set of countermeasures to fire against those attacks represents a primary necessity. While significant effort has been made toward the standardization of the representation of security-related knowledge such as vulnerabilities, weaknesses, and attacks, the intelligence surrounding the countermeasures field received considerably less attention. The paper at hand aims at contributing to the reaction ecosystem by proposing a standard representation of the countermeasure instances. With such a proposition, we address one of the critical challenges found in the literature, that is, the absence of a commonly-shared definition of remediations. To demonstrate the feasibility of our approach, we present several scenarios where some relevant countermeasures are efficiently enforced, resulting in mitigating the ongoing cyberthreat. Then, the advantages and disadvantages of our proposal are extensively discussed, opening the debate for novel and effective contributions in this research line.

Alazab, Moutaz; Alhyari, Salah; Awajan, Albara; Abdallah, Ayman Bahjat

doi: 10.1007/s10586-020-03200-4pmid: N/A

Blockchain overcomes numerous complicated problems related to confidentiality, integrity, availability of fast and secure distributed systems. Using data from a cross-sectoral survey of 449 industries, we investigate factors that hinder or facilitate blockchain adoption in supply chains. To capture the most vital aspects of blockchain adoption in supply chains, our conceptual model integrates the unified theory of acceptance and use of technology (UTAUT) model with the task-technology fit (TTF) and information system success (ISS) models, with trust-based information technology innovation adoption constructs. Using structural equation modelling, we find that the ISS, TTF, and UTAUT models positively influence the key factors affecting supply chain employees’ willingness to adopt blockchain. Our results show that the UTAUT’s social influence factor has no significant effect on the intention to adopt blockchain, while inter-organisational trust has a significant effect on the relationship between the UTAUT dimension and intention to adopt blockchain.

Liu, Jin; Zeng, Dejiao; Guo, Rui; Lu, Mingming; Wu, Fang-Xiang; Wang, Jianxin

doi: 10.1007/s10586-020-03199-8pmid: N/A

Currently, it is still a great challenge in clinical practice to accurately detect the early state of Alzheimer’s disease (AD), i.e., mild cognitive impairment (MCI) including early MCI (EMCI) and late MCI (LMCI). To address this challenge, we propose a new MCI detection framework based on multi-atlas multi-view hybrid graph convolutional networks and ensemble learning. We first construct nine different graphs based on three brain atlases and three morphological measurements using both imaging and non-imaging data of each subject. Then, in order to integrate the information of different graphs and obtain more discriminative feature representations for detecting MCI, we propose a hybrid graph convolutional network method. Finally, a new ensemble learning method is proposed to perform MCI detection tasks. An evaluation of our proposed framework has been conducted with 369 subjects with cognitively normal (CN), 779 subjects with MCI including 310 subjects with EMCI and 469 subjects with LMCI, and 301 subjects with AD on three classification tasks. Experimental results show that our proposed framework can get an accuracy of 90.8% and an AUC of 0.932 for MCI/CN classification, an accuracy of 88.6% and an AUC of 0.908 for MCI/AD classification, and an accuracy of 83.5% and an AUC of 0.851 for EMCI/LMCI classification, respectively. Compared with some state-of-the-art methods about MCI detection, our proposed framework can get better performance. Overall, our proposed framework is effective and promising for MCI detection in clinical practice.

Bugingo, Emmanuel; Zhang, Defu; Chen, Zhaobin; Zheng, Wei

doi: 10.1007/s10586-020-03208-wpmid: N/A

A workflow is a group of tasks that are processed in a particular order to complete an application. Also, it is a popular paradigm used to model complex big-data applications. Executing complex applications in a distributed system such as cloud or cluster implicates optimization of several conflicting objectives such as monetary cost, energy consumption, total execution time of the application (makespan). Regardless of this trend, most of the workflow scheduling approaches focused on single or bi-objective optimization problem. In this paper, we considered the problem of scheduling workflows in a cloud environment as a multi-objective optimization problem, and hence proposed a multi-objective workflow-scheduling algorithm based on decomposition. The proposed algorithm is capable of finding optimal solutions with a single run. Our evaluation results show that, by a single run, the proposed approach manages to obtain the Pareto Front solutions which are at least as good as schedules produced by running a single-objective scheduling algorithm with constraints for multiple times.

Rho, Seungwoo; Park, Geunchul; Choi, Ji Eun; Park, Chan-Yeol

doi: 10.1007/s10586-020-03167-2pmid: N/A

This study aimed to develop a dynamic benchmark automation suite to measure a range of benchmark performances and evaluate the various high-performance computing (HPC) systems. Our suite supports an automated scaling test and profiling data based on hardware performance counters to analyze the system characteristics. We selected four HPC benchmarks—STREAM, High-Performance Linpack, High-Performance Conjugate Gradient, and NAS Parallel Benchmark-for experiments and configured testbeds based on five different systems and an Intel Knights Landing (KNL) cluster with 16 nodes. The Intel KNL system showed both unstable memory and high benchmark performances for a specific input range. Modern Intel systems also exhibited proper characteristics on compute-intensive workloads, whereas the up-to-date AMD system showed high efficiency and proper characteristics on memory-intensive and real application workloads. We also verified that each system has an optimal environment and characteristic for various combinations of experimental variables and profiling data.

Abualigah, Laith; Diabat, Ali

doi: 10.1007/s10586-020-03075-5pmid: N/A

Efficient task scheduling is considered as one of the main critical challenges in cloud computing. Task scheduling is an NP-complete problem, so finding the best solution is challenging, particularly for large task sizes. In the cloud computing environment, several tasks may need to be efficiently scheduled on various virtual machines by minimizing makespan and simultaneously maximizing resource utilization. We present a novel hybrid antlion optimization algorithm with elite-based differential evolution for solving multi-objective task scheduling problems in cloud computing environments. In the proposed method, which we refer to as MALO, the multi-objective nature of the problem derives from the need to simultaneously minimize makespan while maximizing resource utilization. The antlion optimization algorithm was enhanced by utilizing elite-based differential evolution as a local search technique to improve its exploitation ability and to avoid getting trapped in local optima. Two experimental series were conducted on synthetic and real trace datasets using the CloudSim tool kit. The results revealed that MALO outperformed other well-known optimization algorithms. MALO converged faster than the other approaches for larger search spaces, making it suitable for large scheduling problems. Finally, the results were analyzed using statistical t-tests, which showed that MALO obtained a significant improvement in the results.

Tu, Yuanfei; Yang, Geng; Wang, Jing; Su, Qingjian

doi: 10.1007/s10586-020-03101-6pmid: N/A

As an emerging and efficient paradigm for multimedia systems, fog networking has attracted widespread attention over the last few years. However, an increasing number of attacks in the current virtualized environments underlines the importance of secure data sharing. Unfortunately, existing multimedia data sharing schemes are not suitable for the networking systems because of the heavy computational operations, latency-sensitive service, and resource-limited devices. Additionally, authenticated and secure communications are crucial issues related to privacy and trust. In this paper, we propose a secure and efficient data sharing scheme with the computation outsourcing capability in a fog networking system by employing ciphertext-policy attribute-based encryption. The scheme supports dynamic policy updating and delegates attribute revocation processes to the cloud and fog by proxy re-encryption. In particular, we build a secure communication protocol for the revocation parameter transmission. We adopt a chaotic map to generate a one-time key, by which the revocation parameters are encrypted. Then, to ensure legal user accessing to the system, we establish a privacy-preserving communication architecture between the user and cloud, which provides authentication. In addition, our scheme provides a verifiable auditing service for the decryption key and shared file, thus ensuring its correctness. Finally, we analyze the security of the scheme, evaluate its performance, and compare it with related works.