Security Management System for 4G Heterogeneous Networks

There is constant demand for the development of mobile networks to meet the service requirements of users, and their development is a significant topic of research. The current fourth generation (4G) of mobile networks are expected to provide high speed connections anywhere at any time. Various existing 4G architectures such as LTE and WiMax support only wireless technologies, while an alternative architecture, Y-Comm, has been proposed to combine both existing wired and wireless networks. Y-Comm seeks to meet the main service requirements of 4G by converging the existing networks, so that the user can get better service anywhere and at any time. One of the major characteristics of Y-Comm is heterogeneity, which means that networks with different topologies work together to provide seamless communication to the end user. However, this heterogeneity leads to technical issues which may compromise quality of service, vertical handover and security. Due to the convergence characteristic of Y-Comm, security is considered more significant than in the existing LTE and WiMax networks. These security concerns have motivated this research study to propose a novel security management system. The research aims to meet the security requirements of 4G mobile networks, e.g. preventing end user devices from being used as attack tools. This requirement has not been met clearly in previous studies of Y-Comm, but this study proposes a security management system which does this. This research follows the ITU-T recommendation M.3400 dealing with security violations within Y-Comm networks. It proposes a policy-based security management system to deal with events that trigger actions in the system and uses Ponder2 to implement it. The proposed system, located in the top layer of the Y-Comm architecture, interacts with components of Y-Comm to enforce the appropriate policies. Its four main components are the Intelligent Agent, the Security Engine, the Security Policies Database and the Security Administrator. These are represented in this research as managed objects to meet design considerations such as extensibility and modifiability. This research demonstrates that the proposed system meets the security requirements of the Y-Comm environment. Its deployment is possible with managed objects built with Ponder2 for all of the components of Y-Comm, which means that the security management system is able to prevent end user devices from being used as attack tools. It can also achieve other security goals of Y-Comm networks

Deep Neural Networks based Meta-Learning for Network Intrusion Detection

The digitization of different components of industry and inter-connectivity among indigenous networks have increased the risk of network attacks. Designing an intrusion detection system to ensure security of the industrial ecosystem is difficult as network traffic encompasses various attack types, including new and evolving ones with minor changes. The data used to construct a predictive model for computer networks has a skewed class distribution and limited representation of attack types, which differ from real network traffic. These limitations result in dataset shift, negatively impacting the machine learning models' predictive abilities and reducing the detection rate against novel attacks. To address the challenges, we propose a novel deep neural network based Meta-Learning framework; INformation FUsion and Stacking Ensemble (INFUSE) for network intrusion detection. First, a hybrid feature space is created by integrating decision and feature spaces. Five different classifiers are utilized to generate a pool of decision spaces. The feature space is then enriched through a deep sparse autoencoder that learns the semantic relationships between attacks. Finally, the deep Meta-Learner acts as an ensemble combiner to analyze the hybrid feature space and make a final decision. Our evaluation on stringent benchmark datasets and comparison to existing techniques showed the effectiveness of INFUSE with an F-Score of 0.91, Accuracy of 91.6%, and Recall of 0.94 on the Test+ dataset, and an F-Score of 0.91, Accuracy of 85.6%, and Recall of 0.87 on the stringent Test-21 dataset. These promising results indicate the strong generalization capability and the potential to detect network attacks.Comment: Pages: 15, Figures: 10 and Tables:

FFRP: Dynamic firefly mating optimization inspired energy efficient routing protocol for internet of underwater wireless sensor networks

Energy-efficient and reliable data gathering using highly stable links in underwater wireless sensor networks (UWSNs) is challenging because of time and location-dependent communication characteristics of the acoustic channel. In this paper, we propose a novel dynamic firefly mating optimization inspired routing scheme called FFRP for the internet of UWSNs-based events monitoring applications. The proposed FFRP scheme during the events data gathering employs a self-learning based dynamic firefly mating optimization intelligence to find the highly stable and reliable routing paths to route packets around connectivity voids and shadow zones in UWSNs. The proposed scheme during conveying information minimizes the high energy consumption and latency issues by balancing the data traffic load evenly in a large-scale network. In additions, the data transmission over highly stable links between acoustic nodes increases the overall packets delivery ratio and network throughput in UWSNs. Several simulation experiments are carried out to verify the effectiveness of the proposed scheme against the existing schemes through NS2 and AquaSim 2.0 in UWSNs. The experimental outcomes show the better performance of the developed protocol in terms of high packets delivery ratio (PDR) and network throughput (NT) with low latency and energy consumption (EC) compared to existing routing protocols in UWSNs

QoSRP: A cross-layer QoS channel-aware routing protocol for the internet of underwater acoustic sensor networks

Quality of service (QoS)-aware data gathering in static-channel based underwater wireless sensor networks (UWSNs) is severely limited due to location and time-dependent acoustic channel communication characteristics. This paper proposes a novel cross-layer QoS-aware multichannel routing protocol called QoSRP for the internet of UWSNs-based time-critical marine monitoring applications. The proposed QoSRP scheme considers the unique characteristics of the acoustic communication in highly dynamic network topology during gathering and relaying events data towards the sink. The proposed QoSRP scheme during the time-critical events data-gathering process employs three basic mechanisms, namely underwater channel detection (UWCD), underwater channel assignment (UWCA) and underwater packets forwarding (UWPF). The UWCD mechanism finds the vacant channels with a high probability of detection and low probability of missed detection and false alarms. The UWCA scheme assigns high data rates channels to acoustic sensor nodes (ASNs) with longer idle probability in a robust manner. Lastly, the UWPF mechanism during conveying information avoids congestion, data path loops and balances the data traffic load in UWSNs. The QoSRP scheme is validated through extensive simulations conducted by NS2 and AquaSim 2.0 in underwater environments (UWEs). The simulation results reveal that the QoSRP protocol performs better compared to existing routing schemes in UWSNs

Dispatching-Rule Variants Algorithms for Used Spaces of Storage Supports

The paper is regarding the fair distribution of several files having different sizes to several storage supports. With the existence of several storage supports and different files, we search for a method that makes an appropriate backup. The appropriate backup guarantees a fair distribution of the big data (files). Fairness is related to the used spaces of storage support distribution. The problem is how to find a fair method that stores all files on the available storage supports, where each file is characterized by its size. We propose in this paper some fairness methods that seek to minimize the gap between used spaces of all storage supports. In this paper, several algorithms are developed to solve the proposed problem, and the experimental study shows the performance of these developed algorithms

Multimodal Speaker Diarization Using a Pre-Trained Audio-Visual Synchronization Model

Speaker diarization systems aim to find ‘who spoke when?’ in multi-speaker recordings. The dataset usually consists of meetings, TV/talk shows, telephone and multi-party interaction recordings. In this paper, we propose a novel multimodal speaker diarization technique, which finds the active speaker through audio-visual synchronization model for diarization. A pre-trained audio-visual synchronization model is used to find the synchronization between a visible person and the respective audio. For that purpose, short video segments comprised of face-only regions are acquired using a face detection technique and are then fed to the pre-trained model. This model is a two streamed network which matches audio frames with their respective visual input segments. On the basis of high confidence video segments inferred by the model, the respective audio frames are used to train Gaussian mixture model (GMM)-based clusters. This method helps in generating speaker specific clusters with high probability. We tested our approach on a popular subset of AMI meeting corpus consisting of 5.4 h of recordings for audio and 5.8 h of different set of multimodal recordings. A significant improvement is noticed with the proposed method in term of DER when compared to conventional and fully supervised audio based speaker diarization. The results of the proposed technique are very close to the complex state-of-the art multimodal diarization which shows significance of such simple yet effective technique

Performance evaluation of AMQP over QUIC in the internet-of-thing networks

In today’s world, the use of IoT devices is growing every day. For connecting IoT devices from various vendors and supporting a variety of IoT use cases, an interoperable protocol like AMQP is necessary. Researchers are striving to minimize delay because many IoT applications are sensitive to it. The transport layer protocol that is used underneath, such as TCP or UDP, is one of the main causes of the delay. Although TCP is slower than UDP because to the three-way handshake and the usage of TLS for security, it is more reliable than UDP. The Internet Engineering Task Force has introduced a new transport layer protocol called QUIC that combines the best features of UDP and TCP to offer quick and reliable communication. In this study, we integrated QUIC and AMQP1.0 using the Go programming language. The Docker tool was used to containerize the AMQP1.0 Broker, Sender, and Receiver implementations. The performance of AMQP1.0 over TCP and AMQP1.0 over QUIC was benchmarked in the NS3 simulator over various wireless networks including WiFi, 4G/LTE, and satellite. QUIC showed considerable improvement over lossy networks. The results showed that switching from TCP to QUIC at the transport level lowered Communication Time by 8.57% over Satellite network. Although Round Trip Time was almost same yet Start up Latency showed improvement of 52%, 38% and 34% in case of WiFi, 4G/LTE and Satellite respectively. In addition, the performance of AMQP1.0 over TCP and AMQP1.0 over QUIC has been evaluated over different Packet Loss values, the results show that AMQP1.0 over QUIC outperforms AMQP1.0 over TCP in all the cases. The testing results revealed that TCP performance was degraded by 20%, 16%, and 36% over WiFi, 4G/LTE, and satellite, respectively at Packet loss of 15%, while QUIC performance was only degraded by 4%, 8%, and 9% in each of these cases

Threshold parameters selection for empirical mode decomposition-based emg signal denoising

peer reviewedEmpirical Mode Decomposition (EMD) is a data-driven and fully adaptive signal decomposition technique to decompose a signal into its Intrinsic Mode Functions (IMF). EMD has attained great attention due to its capabilities to process a signal in the frequency-time domain without altering the signal into the frequency domain. EMD-based signal denoising techniques have shown great potential to denoise nonlinear and nonstationary signals without compromising the signal’s characteristics. The denoising procedure comprises three steps, i.e., signal decomposition, IMF thresholding, and signal reconstruction. Thresholding is performed to assess which IMFs contain noise. In this study, Interval Thresh-olding (IT), Iterative Interval Thresholding (IIT), and Clear Iterative Interval Thresholding (CIIT) techniques have been explored for denoising of electromyo-graphy (EMG) signals. The effect of different thresholding operators, i.e., SOFT, HARD, and Smoothly Clipped Absolute Deviation (SCAD), on the performance of EMD-based EMG denoising techniques is also investigated. 15 EMG signals, recorded from the upper limb of 5 healthy subjects, were used to identify the best possible combination of thresholding technique and thresholding operator for denoising EMG signals. The performance of denoising techniques is assessed by calculating the Signal to Noise (SNR) ratio of the signals. The results are further evaluated using a two-way Analysis of Variance (ANOVA) statistical test. The results demonstrated that the mean SNR values yielded by the IIT threshold-ing technique outperform the IT thresholding technique (P-value < 0.05), but there is no significant difference in mean SNR values of IIT and CIIT techniques (P-value = 0.9951). For thresholding operators, there is no significant difference in mean SNR values of the HARD and SOFT operator (P-value = 0.0968), whereas the HARD operator outperforms SCAD (P-value < 0.05). It has also shown that the combination of IIT thresholding with SOFT operator and threshold value equal to half of the universal threshold denoise the EMG signals while pre-serving the original signal’s characteristics. IIT-based EMD denoising technique with HARD thresholding operator yields the highest SNR, irrespective of the level of noise embedded in the signal. Whereas IIT with SOFT operator provides comparable SNR and successfully preserves the shape of EMG signals. The identified combination of thresholding technique and thresholding operator can eliminate various noises embedded in EMG signals

Partial Bicasting with Buffering for Proxy Mobile IPV6 Mobility Management in CoAP-Based IoT Networks

Constrained application protocol (CoAP) can be used for message delivery in wireless sensor networks. Although CoAP-based proxy mobile internet protocol (PMIP) was proposed for mobility management, it resulted in handover delay and packet loss. Therefore, an enhanced PMIP version 6, with partial bicasting in CoAP-based internet of things (IoT) networks, is proposed. Here, when an IoT device moved into a new network, the corresponding mobile access gateway (MAG) updated the local mobility anchor (LMA) binding. Further, LMA initiated the &ldquo;partial&rdquo; bicasting of data packets to the new and the previous MAGs. The data packets were buffered at the new MAG during handover and were forwarded to Mobile Node (MN) after the handover operations. The proposed scheme was compared with the existing scheme, using ns-3 simulations. We demonstrated that the proposed scheme reduced handover delays, packet losses, end-to-end delay, throughput, and energy consumption, compared to the existing scheme