Browsing by Author "Kazim, Muhammad"

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    Energy-Aware Scheduling of Streaming Applications on Edge-Devices in IoT-Based Healthcare
    (IEEE, 2021-02-02) Tariq, Umair Ullah; Ali, Haider; Liu, Lu; Hardy, James; Kazim, Muhammad; Ahmed, Waqar
    The reliance on Network-on-Chip (NoC)-based Multiprocessor Systems-on-Chips (MPSoCs) is proliferating in modern embedded systems to satisfy the higher performance requirement of multimedia streaming applications. Task level coarse grained software pipeling also called re-timing when combined with Dynamic Voltage and Frequency Scaling (DVFS) has shown to be an effective approach in significantly reducing energy consumption of the multiprocessor systems at the expense of additional delay. In this article we develop a novel energy-aware scheduler considering tasks with conditional constraints on Voltage Frequency Island (VFI)-based heterogeneous NoC-MPSoCs deploying re-timing integrated with DVFS for real-time streaming applications. We propose a novel task level re-timing approach called R-CTG and integrate it with non linear programming-based scheduling and voltage scaling approach referred to as ALI-EBAD. The R-CTG approach aims to minimize the latency caused by re-timing without compromising on energy-efficiency. Compared to R-DAG, the state-of-the-art approach designed for traditional Directed Acyclic Graph (DAG)-based task graphs, R-CTG significantly reduces the re-timing latency because it only re-times tasks that free up the wasted slack. To validate our claims we performed experiments on using 12 real benchmarks, the results demonstrate that ALI-EBAD out performs CA-TMES-Search and CA-TMES-Quick task schedulers in terms of energy-efficiency.
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    A Framework for Orchestrating Secure and Dynamic Access of IoT Services in Multi-Cloud Environments
    (IEEE, 2018-10-16) Kazim, Muhammad; Liu, Lu; Zhu, Shao Ying
    IoT devices have complex requirements but their limitations in terms of storage, network, computing, data analytics, scalability, and big data management require it to be used it with a technology like cloud computing. IoT backend with cloud computing can present new ways to offer services that are massively scalable, can be dynamically configured, and delivered on demand with large scale infrastructure resources. However, a single cloud infrastructure might be unable to deal with the increasing demand of cloud services in which hundreds of users might be accessing cloud resources, leading to a big data problem and the need for efficient frameworks to handle a large number of user requests for IoT services. These challenges require new functional elements and provisioning schemes. To this end, we propose the usage of multi-clouds with IoT which can optimize the user requirements by allowing them to choose best IoT services from many services hosted in various cloud platforms and provide them with more infrastructure and platform resources to meet their requirements. This paper presents a novel framework for dynamic and secure IoT services access across multi-clouds using the cloud on-demand model. To facilitate multi-cloud collaboration, novel protocols are designed and implemented on cloud platforms. The various stages involved in the framework for allowing users access to IoT services in multi-clouds are service matchmaking (i.e., to choose the best service matching user requirements), authentication (i.e., a lightweight mechanism to authenticate users at runtime before granting them service access), and SLA management (including, SLA negotiation, enforcement, and monitoring). SLA management offers benefits like negotiating required service parameters, enforcing mechanisms to ensure that service execution in the external cloud is according to the agreed SLAs and monitoring to verify that the cloud provider complies with those SLAs. The detailed system design to establish secure multi-cloud collaboration has been presented. Moreover, the designed protocols are empirically implemented on two different clouds, including OpenStack and Amazon AWS. Experiments indicate that the proposed system is scalable, authentication protocols result only in a limited overhead compared to standard authentication protocols, and any SLA violation by a cloud provider could be recorded and reported back to the user.
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    Human-Centric Cyber Social Computing Model for Hot-Event Detection and Propagation
    (IEEE, 2019-05-21) Shi, Lei-Lei; Liu, Lu; Wu, Yan; Jiang, Liang; Kazim, Muhammad; Ali, Haider; Panneerselvam, John
    Microblogging networks have gained popularity in recent years as a platform enabling expressions of human emotions, through which users can conveniently produce contents on public events, breaking news, and/or products. Subsequently, microblogging networks generate massive amounts of data that carry opinions and mass sentiment on various topics. Herein, microblogging is regarded as a useful platform for detecting and propagating new hot events. It is also a useful channel for identifying high-quality posts, popular topics, key interests, and high-influence users. The existence of noisy data in the traditional social media data streams enforces to focus on human-centric computing. This paper proposes a human-centric social computing (HCSC) model for hot-event detection and propagation in microblogging networks. In the proposed HCSC model, all posts and users are preprocessed through hypertext induced topic search (HITS) for determining high-quality subsets of the users, topics, and posts. Then, a latent Dirichlet allocation (LDA)-based multiprototype user topic detection method is used for identifying users with high influence in the network. Furthermore, an influence maximization is used for final determination of influential users based on the user subsets. Finally, the users mined by influence maximization process are generated as the influential user sets for specific topics. Experimental results prove the superiority of our HCSC model against similar models of hot-event detection and information propagation.
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    Network Intrusion Detection based on Amino Acid Sequence Structure Using Machine Learning
    (MDPI, 2023-10-17) Ibaisi, Thaer AL; Kuhn, Stefan; Kaiiali, Mustafa; Kazim, Muhammad
    The detection of intrusions in computer networks, known as Network-Intrusion-Detection Systems (NIDSs), is a critical field in network security. Researchers have explored various methods to design NIDSs with improved accuracy, prevention measures, and faster anomaly identification. Safeguarding computer systems by quickly identifying external intruders is crucial for seamless business continuity and data protection. Recently, bioinformatics techniques have been adopted in NIDSs’ design, enhancing their capabilities and strengthening network security. Moreover, researchers in computer science have found inspiration in molecular biology’s survival mechanisms. These nature-designed mechanisms offer promising solutions for network security challenges, outperforming traditional techniques and leading to better results. Integrating these nature-inspired approaches not only enriches computer science, but also enhances network security by leveraging the wisdom of nature’s evolution. As a result, we have proposed a novel Amino-acid-encoding mechanism that is bio-inspired, utilizing essential Amino acids to encode network transactions and generate structural properties from Amino acid sequences. This mechanism offers advantages over other methods in the literature by preserving the original data relationships, achieving high accuracy of up to 99%, transforming original features into a fixed number of numerical features using bio-inspired mechanisms, and employing deep machine learning methods to generate a trained model capable of efficiently detecting network attack transactions in real-time.
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    A survey on top security threats in cloud computing
    (SAI Organisation, 2015) Kazim, Muhammad; Zhu, Shao Ying
    Cloud computing enables the sharing of resources such as storage, network, applications and software through internet. Cloud users can lease multiple resources according to their requirements, and pay only for the services they use. However, despite all cloud benefits there are many security concerns related to hardware, virtualization, network, data and service providers that act as a significant barrier in the adoption of cloud in the IT industry. In this paper, we survey the top security concerns related to cloud computing. For each of these security threats we describe, i) how it can be used to exploit cloud components and its effect on cloud entities such as providers and users, and ii) the security solutions that must be taken to prevent these threats. These solutions include the security techniques from existing literature as well as the best security practices that must be followed by cloud administrators.