Deep-IDS: A Real-Time Intrusion Detector for IoT Nodes Using Deep Learning

The Internet of Things (IoT) represents a swiftly expanding sector that is pivotal in driving the innovation of today's smart services. However, the inherent resource-constrained nature of IoT nodes poses significant challenges in embedding advanced algorithms for cybersecurity, leading to an escalation in cyberattacks against these nodes. Contemporary research in Intrusion Detection Systems (IDS) predominantly focuses on enhancing IDS performance through sophisticated algorithms, often overlooking their practical applicability. This paper introduces Deep-IDS, an innovative and practically deployable Deep Learning (DL)-based IDS. It employs a Long-Short-Term-Memory (LSTM) network comprising 64 LSTM units and is trained on the CIC-IDS2017 dataset. Its streamlined architecture renders Deep-IDS an ideal candidate for edge-server deployment, acting as a guardian between IoT nodes and the Internet against Denial of Service, Distributed Denial of Service, Brute Force, Man-in-the-Middle, and Replay Attacks. A distinctive aspect of this research is the trade-off analysis between the intrusion Detection Rate (DR) and the False Alarm Rate (FAR), facilitating the real-time performance of the Deep-IDS. The system demonstrates an exemplary detection rate of 96.8% at the 70% threshold of DR-FAR trade-off and an overall classification accuracy of 97.67%. Furthermore, Deep-IDS achieves precision, recall, and F1-scores of 97.67%, 98.17%, and 97.91%, respectively. On average, Deep-IDS requires 1.49 seconds to identify and mitigate intrusion attempts, effectively blocking malicious traffic sources. The remarkable efficacy, swift response time, innovative design, and novel defense strategy of Deep-IDS not only secure IoT nodes but also their interconnected sub-networks, thereby positioning Deep-IDS as a leading IDS for IoT-enhanced computer networks.</p

A Privacy-preserving Mobile and Fog Computing Framework to Trace and Prevent COVID-19 Community Transmission

To slow down the spread of COVID-19, governments around the world are trying to identify infected people and to contain the virus by enforcing isolation and quarantine. However, it is difficult to trace people who came into contact with an infected person, which causes widespread community transmission and mass infection. To address this problem, we develop an e-government Privacy Preserving Mobile and Fog computing framework entitled PPMF that can trace infected and suspected cases nationwide. We use personal mobile devices with contact tracing app and two types of stationary fog nodes, named Automatic Risk Checkers (ARC) and Suspected User Data Uploader Node (SUDUN), to trace community transmission alongside maintaining user data privacy. Each user's mobile device receives a Unique Encrypted Reference Code (UERC) when registering on the central application. The mobile device and the central application both generate Rotational Unique Encrypted Reference Code (RUERC), which broadcasted using the Bluetooth Low Energy (BLE) technology. The ARCs are placed at the entry points of buildings, which can immediately detect if there are positive or suspected cases nearby. If any confirmed case is found, the ARCs broadcast pre-cautionary messages to nearby people without revealing the identity of the infected person. The SUDUNs are placed at the health centers that report test results to the central cloud application. The reported data is later used to map between infected and suspected cases. Therefore, using our proposed PPMF framework, governments can let organizations continue their economic activities without complete lockdown.Comment: 12 pages, 9 figures, 1 table, 1 algorith

A study on anatomy of smartphone,”

Abstract: The latest smartphones are attributed with the quality graphics, portable size and user applications support and multimode connectivity features. Smartphone incorporatesthe capabilities of both computing and communication devices. The latest distributed computing models are focused on employing smartphone as a significant stakeholder for enabling complicated and ubiquitous applications in the changing mobile computing world. Therefore, it is necessary to understand the components of smartphone and its working behavior for operation in the cellular and data networks. In this paper we study the anatomy of the smartphone by discussing its major components such as application processor and baseband processor. We describe different subcategories of smartphone components and highlight the behavior and significance of each component for dual mode functionalities of smartphone. The paper provides tutorial for understanding the architecture of the smartphone and exploring the functionalities of the dual processors of smartphone which are used for accessing different types of wireless networks. It helps in developing optimal procedures for deploying the components of the smartphone while accessing cellular and data network

A Review on VANET Security: Future Challenges and Open Issues

VANET is a well-established vehicular communication technology well-aligned with emerging vehicular technologies such as the Internet of Vehicles (IoV), Unmanned Aerial Vehicle (UAV), etc. Although VANET offers improved methods for addressing contemporary technology, it confronts significant challenges in supplying adequate security measures for intended access. VANET operates on multiple execution platforms as a service delivery mechanism, namely roadside units, vehicle-to-vehicle, vehicle-to-device, and vehicle-to-everything (V2X). Using these communication platforms, VANET must establish its security measures for future purposes and bolster protocol authentications. Without adequate security conformance, VANET data delivery and network-wide execution become insecure. Consequently, VANET requires correct security configurations and device engagements for improved protocol working environments and changes. In this work, we describe some recent security problems for the VANET mechanism so that developers and engineers will be aware of the specific security needs and can avoid errors or intruders when deploying VANETS across cities and urban roadsides. This study describes the primary security components and categorical difficulties for VANET through the following themes: classification of security attacks, standard or timely requested security protocol problems and solutions, and the best feasible security criteria for extended VANETs. We finish this paper by discussing many open topics and future VANET security developments or concerns

Performance enhancement framework for cloudlet in mobile cloud computing / Md Whaiduzzaman

A tremendous increase in the use of mobile devices, such as smart phones and tablets, has been observed in recent years. Mobile device resources, such as CPU, memory and storage resources, have also experienced dramatic increases in capacity. Simultaneously, a rich variety of mobile applications that require extensive computational resources for mobile application execution have been developed. These new applications are complemented with cloud resources through the emerging Mobile Cloud Computing (MCC) paradigm. MCC augments mobile device capabilities by leveraging the resources of distant clouds, nearby cloudlets or mobile ad-hoc clouds in the vicinity. However, practical utilization of MCC is hindered by limitations associated with network connectivity. In the case of distant clouds, jitter, bandwidth, and propagation delay pose a challenge to realtime application response. On the other hand, cloudlets and mobile ad-hoc clouds are not sufficiently resource-rich to be able to support rich mobile applications. In this research, we consider a mobile user in the vicinity of a cloudlet that is situated at a distance of one hop through aWi-Fi communication medium. We show that a substantial number of users accessing the cloudlet for computation-intensive tasks results in delayed task completion and ultimately diminishes the benefits of using cloudlets. The problem is referred to as the cloudlet resource scarcity problem. To alleviate this problem, various researchers have offered solutions whereby mobile device resources are used for partial task completion. However, the proposed approaches do not consider the mobile device offered-serviceto- load ratio. In this research, we propose the Mobile-device-based Cloudlet Resource Enhancement (MobiCoRE) framework for mobile application augmentation to employ nearby mobile devices while ensuring the following: (i) mobile devices always obtain time benefits for their tasks when submitted to the cloudlet, and (ii) the cloudlet-induced mobile device load is a fraction of its own service requirements. We map MobiCoRE on iii the M/M/c/K queue and model the system using a birth-death Markov chain. We show that for cloudlets, the framework always obtains the maximum advantage for mobile devices in terms of job completion time when the cloudlet service time is set to ¯ c l , where ¯ c is the cloudlet utilization and l is the application arrival rate. Furthermore, the optimal service time is independent of the application’s service requirement. We implemented the MobiCoRE framework using the Openstack cloud. The evaluation shows that MobiCoRE accommodates up to 50% more users when operating at the optimal service time and provides 50% time benefits for mobile users. The empirical analysis and statistical validation demonstrate that our proposed framework, i.e., MobiCoRE, significantly and positively impacts the cloudlet performance by exploiting and orchestrating nearby mobile device resources

A Methodological Framework on Development the Garment Payroll System (GPS) as SaaS

Management of Payroll is a challenging task for a production based Ready Made Garmentindustry to pay the employee's salary just in time with accuracy. In an existing Garment industry, calculate thousands of worker's monthly pay depending on each and every piece of production and with different grades of workers is really a tough job. Hence, it is essential to make the system automated. In cloud based garment payroll system, it keeps track of all workers' individual daily production, including style and size and after the end of each month generates worker's net salary by calculating the production amount, production bonus, attendance bonus, festival bonus, no work amount, night allowance by deducting advance if any then generate wages by a single click. This user-friendly proposed cloud Garment Payroll System (GPS) helps to generate thousands or more of a worker's salary just with a few clicks and 100% accuracy. Unlike traditional payroll system, which is installed on site, cloud-based payroll is hosted and managed by the vendor and pay as you go. The system then can be accessed from any locations from any web-enabled devices like desktop, laptop, Smartphone, tab, etc. using internet connectivity. This proposed system also increases productivity, increase efficiency, security and moreover reduce human errors.</p

Measuring security for cloud service provider:A Third Party approach

Cloud Computing (CC) is a new paradigm of utility computing and enormously growing phenomenon in the present IT industry hype. CC leverages low cost investment opportunity for the new business entrepreneur as well as business avenues for cloud service providers. As the number of the new Cloud Service Customer (CSC) increases, users require a secure, reliable and trustworthy Cloud Service Provider (CSP) from the market to store confidential data. However, a number of shortcomings in reliable monitoring and identifying security risks, threats are an immense concern in choosing the highly secure CSP for the wider cloud community. The secure CSP ranking system is currently a challenging aspect to gauge trust, privacy and security. In this paper, a Trusted Third Party (TTP) like credit rating agency is introduced for security ranking by identifying current assessable security risks. We propose an automated software scripting model by penetration testing for TTP to run on CSP side and identify the vulnerability and check security strength and fault tolerance capacity of the CSP. Using the results, several non-measurable metrics are added and provide the ranking system of secured trustworthy CSP ranking systems. Moreover, we propose a conceptual model for monitoring and maintaining such TTP cloud ranking providers worldwide called federated third party approach. Hence the model of federated third party cloud ranking and monitoring system assures and boosts up the confidence to make a feasible secure and trustworthy market of CSPs.</p

PEFC : Performance Enhancement Framework for Cloudlet in mobile cloud computing

Augmenting the computing capability to the distant cloud help us to envision a new computing era named as mobile cloud computing (MCC). By leveraging the cloud resources mobile user can computation time and energy benefit. However, distant cloud has several limitations such as communication delay and bandwidth make us to think for closer cloud which brings the idea of proximate cloud of cloudlet. Cloudlet has distinct advantages and is free from several limitations of distant cloud. However, limited resources of cloudlet negatively impact the cloudlet performance with the increasing number of substantial users. In this paper, we propose a framework which helps to enhance the finite resource cloudlet performance by increasing cloudlet resources. Our aim is to increase the cloudlet performance with this limited cloudlet resource and make the better user experience for the cloudlet user in mobile cloud computing. We analyze and explain the each section of the proposed framework. In addition, we also list the important features and salient advantages of Performance Enhancement Framework of Cloudlet (PEFC).</p