Dynamic Task Scheduling based on Burst Time Requirement for Cloud Environment

Cloud computing has an indispensable role in the modern digital scenario. The fundamental challenge of cloud systems is to accommodate user requirements which keep on varying. This dynamic cloud environment demands the necessity of complex algorithms to resolve the trouble of task allotment. The overall performance of cloud systems is rooted in the efficiency of task scheduling algorithms. The dynamic property of cloud systems makes it challenging to find an optimal solution satisfying all the evaluation metrics. The new approach is formulated on the Round Robin and the Shortest Job First algorithms. The Round Robin method reduces starvation, and the Shortest Job First decreases the average waiting time. In this work, the advantages of both algorithms are incorporated to improve the makespan of user tasks.

Privacy-Preserving Authentication Scheme for Roaming Service in Global Mobility Networks

With the rapid development of mobile intelligent technologies and services, users can freely experience ubiquitous services in global mobility networks. It is necessary to provide authentications and protection to the privacy of mobile users. Until now, many authentication and privacy schemes were proposed. However, most of the schemes have been exposed to some security problems. Recently, Madhusudhan and Shashidhara (M&S) proposed a lightweight authentication scheme, denoted as the M&S scheme, for roaming services in global mobility networks. This paper shows that the M&S scheme has security flaws including two masquerading attacks and a mobile user trace attack. After that, we propose a privacypreserving authentication scheme for global mobility networks. The proposed scheme not only focused on the required security but also added privacy concerns focused on anonymity based on a dynamic pseudonym, which is based on exclusive-or operation, hash operation and symmetric key cryptography. Formal security analysis is performed based on Burrow-Abadi-Needdham (BAN) logic and the ProVerif tool, which concludes that the proposed scheme is secure. The analysis shows that the proposed authentication scheme is secure and provides privacy with a reasonable performance.

Investigating Multilayer Omega-Type Networks Operating with the Cut-Through Technique under Uniform or Hotspot Traffic Conditions

The continuous increase in the complexity of data networks has motivated the development of more effective Multistage Interconnection Networks (MINs) as important factors in providing higher data transfer rates in various switching divisions. In this paper, semi-layer omega-class networks operating with a cut-through forwarding technique are chosen as test-bed subjects for detailed evaluation, and this network architecture is modelled, inspected, and simulated. The results are examined for relevant singlelayer omega networks operating with cut-through or ‘store and forward’ forwarding techniques. Two series of experiments are carried out: one concerns the case of uniform traffic, while the other is related to hotspot traffic. The results quantify the way in which this network outperforms the corresponding singlelayer network architectures for the same network size and buffer size. Furthermore, the effects of the dimensions of the switch elements and their corresponding reliability on the overall interconnection system are investigated, and the complexity and the relevant cost are examined. The data yielded by this investigation can be valuable to MIN engineers and can allow them to achieve more productive networks with lower overall implementation costs.

Securing BGP by Handling Dynamic Network Behavior and Unbalanced Datasets

The Border Gateway Protocol (BGP) provides crucial routing information for the Internet infrastructure. A problem with abnormal routing behavior affects the stability and connectivity of the global Internet. The biggest hurdles in detecting BGP attacks are extremely unbalanced data set category distribution and the dynamic nature of the network. This unbalanced class distribution and dynamic nature of the network results in the classifier's inferior performance. In this paper we proposed an efficient approach to properly managing these problems, the proposed approach tackles the unbalanced classification of datasets by turning the problem of binary classification into a problem of multiclass classification. This is achieved by splitting the majority-class samples evenly into multiple segments using Affinity Propagation, where the number of segments is chosen so that the number of samples in any segment closely matches the minority-class samples. Such sections of the dataset together with the minor class are then viewed as different classes and used to train the Extreme Learning Machine (ELM). The RIPE and BCNET datasets are used to evaluate the performance of the proposed technique. When no feature selection is used, the proposed technique improves the F1 score by 1.9% compared to state-of-the-art techniques. With the Fischer feature selection algorithm, the proposed algorithm achieved the highest F1 score of 76.3%, which was a 1.7% improvement over the compared ones. Additionally, the MIQ feature selection technique improves the accuracy by 3.5%. For the BCNET dataset, the proposed technique improves the F1 score by 1.8% for the Fisher feature selection technique. The experimental findings support the substantial improvement in performance from previous approaches by the new technique.

Threshold based VM Placement Technique for Load Balanced Resource Provisioning using Priority Scheme in Cloud Computing

The unbalancing load issue is a multi-variation, multi-imperative issue that corrupts the execution and productivity of processing assets. Workload adjusting methods give solutions of load unbalancing circumstances for two bothersome aspects over-burdening and under-stacking. Cloud computing utilizes planning and workload balancing for a virtualized environment, resource partaking in cloud foundation. These two factors must be handled in an improved way in cloud computing to accomplish ideal resource sharing. Henceforth, there requires productive resource, asset reservation for guaranteeing load advancement in the cloud. This work aims to present an incorporated resource, asset reservation, and workload adjusting calculation for effective cloud provisioning. The strategy develops a Priority-based Resource Scheduling Model to acquire the resource, asset reservation with threshold-based load balancing for improving the proficiency in cloud framework. Extending utilization of Virtual Machines through the suitable and sensible outstanding task at hand modifying is then practiced by intensely picking a job from submitting jobs using Priority-based Resource Scheduling Model to acquire resource asset reservation. Experimental evaluations represent, the proposed scheme gives better results by reducing execution time, with minimum resource cost and improved resource utilization in dynamic resource provisioning conditions.

A New Efficient Cache Replacement Strategy for Named Data Networking

The Information-Centric Network (ICN) is a future internet architecture with efficient content retrieval and distribution. Named Data Networking (NDN) is one of the proposed architectures for ICN. NDN’s innetwork caching improves data availability, reduce retrieval delays, network load, alleviate producer load, and limit data traffic. Despite the existence of several caching decision algorithms, the fetching and distribution of contents with minimum resource utilization remains a great challenge. In this paper, we introduce a new cache replacement strategy called Enhanced Time and Frequency Cache Replacement strategy (ETFCR) where both cache hit frequency and cache retrieval time are used to select evicted data chunks. ETFCR adds time cycles between the last two requests to adjust data chunk’s popularity and cache hits. We conducted extensive simulations using the ccnSim simulator to evaluate the performance of ETFCR and compare it to that of some well-known cache replacement strategies. Simulations results show that ETFCR outperforms the other cache replacement strategies in terms of cache hit ratio, and lower content retrieval delay.

BER Analysis for Downlink MIMO-NOMA Systems over Rayleigh Fading Channels

The Multiple-input multiple-output (MIMO) technique combined with non-orthogonal multiple access (NOMA) has been considered to enhance total system performance. This paper studies the bit error rate of two-user power-domain NOMA systems using successive interference cancellation receivers, with zeroforcing equalization over quasi-static Rayleigh fading channels. Successive interference cancellation technique at NOMA receivers has been the popular research topic due to its simple implementation, despite its vulnerability to error propagation. Closed-form expressions are derived for downlink NOMA in single-input single-output and uncorrelated quasi-static MIMO Rayleigh fading channel. Analytical results are consolidated with Monte Carlo simulation.

A Cooperative Localization Method based on V2I Communication and Distance Information in Vehicular Networks

Relative positions are recent solutions to overcome the limited accuracy of GPS in urban environment. Vehicle positions obtained using V2I communication are more accurate because the known roadside unit (RSU) locations help predict errors in measurements over time. The accuracy of vehicle positions depends more on the number of RSUs; however, the high installation cost limits the use of this approach. It also depends on nonlinear localization nature. They were neglected in several research papers. In these studies, the accumulated errors increased with time due to the linearity localization problem. In the present study, a cooperative localization method based on V2I communication and distance information in vehicular networks is proposed for improving the estimates of vehicles’ initial positions. This method assumes that the virtual RSUs based on mobility measurements help reduce installation costs and facilitate in handling fault environments. The extended Kalman filter algorithm is a well-known estimator in nonlinear problem, but it requires well initial vehicle position vector and adaptive noise in measurements. Using the proposed method, vehicles’ initial positions can be estimated accurately. The experimental results confirm that the proposed method has superior accuracy than existing methods, giving a root mean square error of approximately 1 m. In addition, it is shown that virtual RSUs can assist in estimating initial positions in fault environments.

MEKDA: Multi-Level ECC based Key Distribution and Authentication in Internet of Things

The Internet of Things (IoT) is an extensive system of networks and connected devices with minimal human interaction and swift growth. The constraints of the System and limitations of Devices pose several challenges, including security; hence billions of devices must protect from attacks and compromises. The resource-constrained nature of IoT devices amplifies security challenges. Thus standard data communication and security measures are inefficient in the IoT environment. The ubiquity of IoT devices and their deployment in sensitive applications increase the vulnerability of any security breaches to risk lives. Hence, IoT-related security challenges are of great concern. Authentication is the solution to the vulnerability of a malicious device in the IoT environment. The proposed Multi-level Elliptic Curve Cryptography based Key Distribution and Authentication in IoT enhances the security by Multi-level Authentication when the devices enter or exit the Cluster in an IoT system. The decreased Computation Time and Energy Consumption by generating and distributing Keys using Elliptic Curve Cryptography extends the availability of the IoT devices. The Performance analysis shows the improvement over the Fast Authentication and Data Transfer method.

Utilizing XAI Technique to Improve Autoencoder based Model for Computer Network Anomaly Detection with Shapley Additive Explanation(SHAP)

Machine learning (ML) and Deep Learning (DL) methods are being adopted rapidly, especially in computer network security, such as fraud detection, network anomaly detection, intrusion detection, and much more. However, the lack of transparency of ML and DL based models is a major obstacle to their implementation and criticized due to its black-box nature, even with such tremendous results. Explainable Artificial Intelligence (XAI) is a promising area that can improve the trustworthiness of these models by giving explanations and interpreting its output. If the internal working of the ML and DL based models is understandable, then it can further help to improve its performance. The objective of this paper is to show that how XAI can be used to interpret the results of the DL model, the autoencoder in this case. And, based on the interpretation, we improved its performance for computer network anomaly detection. The kernel SHAP method, which is based on the shapley values, is used as a novel feature selection technique. This method is used to identify only those features that are actually causing the anomalous behaviour of the set of attack/anomaly instances. Later, these feature sets are used to train and validate the autoencoderbut on benign data only. Finally, the built SHAP_Model outperformed the other two models proposed based on the feature selection method. This whole experiment is conducted on the subset of the latest CICIDS2017 network dataset. The overall accuracy and AUC of SHAP_Model is 94% and 0.969, respectively.