An intrusion detection system serves as the backbone for providing high-level network security. Different forms of network attacks have been discovered and they continue to become gradually more sophisticated and complicated. With the wide use of internet-based applications, cyber security has become an important research area. Despite the availability of many existing intrusion detection systems, intuitive cybersecurity systems are needed due to alarmingly increasing intrusion attacks. Furthermore, with new intrusion attacks, the efficacy of existing systems depletes unless they evolve. The lack of real datasets adds further difficulties to properly investigating this problem. This study proposes an intrusion detection approach for the modern network environment by considering the data from satellite and terrestrial networks. Incorporating machine learning models, the study proposes an ensemble model RFMLP that integrates random forest (RF) and multilayer perceptron (MLP) for increasing intrusion detection performance. For analyzing the efficiency of the proposed framework, three different datasets are used for experiments and validation, namely KDD-CUP 99, NSL-KDD, and STIN. In addition, performance comparison with state-of-the-art models is performed which suggests that the RFMLP can detect intrusion attacks with high accuracy than the existing approaches.

The critical environment of the underground mines is a risky zone for mining applications and it is very hazardous to engage the miners without a sophisticated communication system. The existing wired networks are susceptible to damage and the wireless radio systems experience severe fading that restricts the complete access to the entire assembly of a mine. Wireless optical communication is a better approach that can be incorporated in the erratic atmosphere of underground mines to overcome such issues, as lights are already used to illuminate the mine galleries. This study is focused on investigating the characteristics of visible light communication (VLC) in an underground coal mine. The entire scope of VLC is elaborated along with the influence of coal dust particles and the scattering model. The impact of coal dust clouds on visibility and attenuation is analyzed for visible light transmission. The shadowing effect generated by the pillars and mining machinery is estimated by employing the bimodal Gaussian distribution (BGD) approach in coal mines. The characteristic model of VLC for underground coal mines is presented by classifying the area of the mine into mine gallery and sub-galleries. The transmission links of VLC are categorized as the line of sight (LOS) link for direct propagation and the non-LOS (NLOS) link for reflected propagation. The scenarios of LOS and NLOS propagation are considered for each evaluating parameter. Furthermore, the performance of the proposed framework is examined by computing the received signal power, path loss, delay spread (DS), and signal to noise ratio (SNR).

With the fast advancement in cloud computing, progressively more users store their applications and data on the cloud. Cloud computing has lots of features, e.g. virtualization, multi-user, efficiency, cost savings, and most importantly security. Machine learning approaches based on neural networks are being widely applied in cloud infrastructure when training is performed however this may produce possible privacy and security risk as direct access to raw data is required. To address this problem, we propose a new security design using Artificial Neural Networks (ANN) and encryption to confirm a safe communication system in the cloud environment, by letting the third parties access the data in an encrypted form for processing without disclosing the data of the provider party to secure important information. In this paper, to train neural networks using encrypted data we considered the Matrix Operation-based Randomization and Encipherment (MORE) technique, based on Fully Homomorphic Encryption (FHE). This technique allows the computations to be performed directly on floating-point data within a neural network with a minor computational overhead. We examined the speech and voice recognition problem and the performance of the proposed method has been validated in MATLAB simulation. Results showed that applying neural network training with MORE offers improved accuracy, runtime, and performance. These results highlight the potential of the proposed method to protect privacy and provide high accuracy in a reasonable amount of time when compared to other state-of-the-art techniques.

The environment of underground coal mines has challenging properties that makes this zone inadaptable for a stable communication system. Additionally, various deteriorating physical parameters strongly affect the performance of wireless networks, which leads to limited network coverage and poor quality of data communication. This study investigates the communication capability in underground coal mines by optimizing the wireless link to develop a stable network for an underground hazardous environment. A hybrid channel-modeling scheme is proposed to characterize the environment of underground mines for wireless communication by classifying the area of a mine into the main gallery and sub-galleries. The complex segments of mine are evaluated by categorizing the wireless links for the line-of-sight (LOS) zones and hybrid modeling is employed to examine the characteristics of electromagnetic signal propagation. For hybrid channel modeling, the multimode waveguide model and geometrical optic (GO) model are used for developing an optimal framework that improves the accessibility of the network in the critical time-varying environment of mines. Moreover, the influence of various deteriorating factors is analyzed using 2.4 GHz to 5 GHz frequency band to study its relationship with the vital constraints of an underground mine. The critical factors such as path loss, roughness loss, delay spread, and shadow fading are examined under detailed analysis with variation in link structure for the mine.

Optical Wireless Communication (OWC) wirelessly transfers data over long distances in the form of light using laser beam with faster speed, low implementation cost, license free spectrum, high security and higher bandwidth. However, atmospheric attenuations like fog, snow and rain highly effect OWC performance. Important factors that affect its performance include scintillation and communication channel. In this paper, we have presented our own approach of Single Input Multiple Channels Single Output (SIMCSO) for mitigating the snow attenuation effects caused in OWC performance. Focus is on the availability and power level of the received signals under bad weather conditions. Our technique uses a transmitter, receiver, a fork device in free space and generates multiple copies of same data to combine all signals coming from sender using combiner before passing to the receiver. It enhances the Q-factor, Received Power and reduces the BER. We simulated the model of OWC system with multiple channels using Optic-System over range of 1-5km and analyzed the performance of SIMCSO configuration against different parameters.