The paper focuses on the study of cyber security in Ukraine and creation of a predictive model for reducing the risk of identified cyber threats. Forecasting is performed using a linear regression model, taking into account the optimal dependence of specific threats in the field of cyber security of Ukraine on variables characterizing capabilities / vulnerabilities of cyber security. An unique empirical base was used for the analysis, which was formed on the basis of an expert survey of the cyber security system’s subjects in Ukraine. In order to increase the representativeness of the research, based on the selection of reliable expert population, data cleaning is provided. Methodological research is based on a risk-oriented approach, which provided a risk assessment of the spread of cyber threats and, on this basis, the determination of capabilities / vulnerabilities of the cyber security system in Ukraine. The value of the research is formed not only by assessing the risks of the spread of cyber threats, but by a more in-depth analysis of the dependence of the cyber threats’ level on the vulnerability of the cyber security system based on the search for optimal and statistically significant relationships. The experiment was conducted on the basis of determining the optimal model for forecasting the risk of the spread of one of the most significant threats in Ukraine – data confidentiality breach (54.67%), depending on the variables that characterize the capabilities / vulnerabilities of the cyber security system in Ukraine. The experiment showed that the optimal model emphasizes the predictors characterizing the vulnerability of the organizational cyber security system – "Departmental level of cybersecurity monitoring" and capabilities: "The level of use of risk management approaches at the operational level" and "The level of methodological support for cybersecurity of the critical infrastructure system".

Conserving energy and finding the stable path are the two vital issues in Mobile Ad Hoc Networks (MANETs) as the prior increases the network lifetime and the later increases the network throughput. The nodes which are not legitimate in terms of residual energy and packet forwarding history might be a threat to the path thereby making the path unstable. Thus, it results in frequent link failure, reduced throughput, reduced network and path life time. In order to reduce these hazards, authors have proposed an energy efficient, reliable path selecting protocol referred to as Trust Based Energy Aware Multipath Disjoint Routing Protocol (TEA-MDRP) for MANETS in this article. TEA-MDRP finds the optimum route between the source and the destination nodes using two parameters namely, the Accumulated Trust Value (ATV) and the node’s residual energy (N_res). ATV is calculated based on the packet forwarding status of the node which shows how good the node is in terms of packet forwarding point of view. TEA-MDRP allows only the nodes which have a good ATV and sufficient residual energy. A good ATV shows loyalty in forwarding the packets while a sufficiently large residual energy node avoids frequent path breakups and packet drops. Thus, the TEA-MDRP not only increases the network and path lifetime but also increases the throughput of the communication. Further, with the legitimate nodes in the paths, the TEA-MDRP considerably reduces the control packet overhead which might occur because of the frequent route re-discovery process. An extensive simulation is carried out using Network Simulator-2.35 for the quantitative and qualitative analysis of TEA-MDRP. The results obtained are compared with classical AOMDV and the results are satisfactory.

The analysis and grading of software vulnerabilities is an important process that is done manually by experts today. For this reason, there are time delays, human errors, and excessive costs involved with the process. The final result of these software vulnerability reports created by experts is the calculation of a severity score and a severity rating. The severity rating is the first and foremost value of the software’s vulnerability. The vulnerabilities that can be exploited are only 20% of the total vulnerabilities. The vast majority of exploitations take place within the first two weeks. It is therefore imperative to determine the severity rating without time delays. Our proposed model uses statistical methods and deep learning-based word embedding methods from natural language processing techniques, and machine learning algorithms that perform multi-class classification. Bag of Words, Term Frequency Inverse Document Frequency and Ngram methods, which are statistical methods, were used for feature extraction. Word2Vec, Doc2Vec and Fasttext algorithms are included in the study for deep learning based Word embedding. In the classification stage, Naive Bayes, Decision Tree, K-Nearest Neighbors, Multi-Layer Perceptron, and Random Forest algorithms that can make multi-class classification were preferred. With this aspect, our model proposes a hybrid method. The database used is open to the public and is the most reliable data set in the field. The results obtained in our study are quite promising. By helping experts in this field, procedures will speed up. In addition, our study is one of the first studies containing the latest version of the data size and scoring systems it covers.

Information security is indispensable in the transmission of multimedia data. While accumulating and distributing such multimedia data, the access of data from a third person is the real security challenging issue. Information hiding plays an important role. Scramble the data before hiding it in carrier media gives enhanced security level for the data. In this paper, bit plane slicing is used to represent an input image with eight planes at bit-level instead of pixel-level. As the least significant bit contains noisy information, only the most significant bit plane can be used to represent an image. At the first level, an input image is processed through the spatial domain. Transform domain techniques are used to process the image at the middle level. Elliptic curve cryptography is used to scramble and descramble the MSB plane image. A logistic chaotic sequence of the input image is added to the most significant bit plane image to generate the final scrambled image. The discrete wavelet transform is used to embed the scrambled image in its high-frequency sub-bands. At the last level, a least significant bit technique, a spatial domain is used to embed the scrambled image in the carrier image. Message integrity is also verified by finding the hash of an input image. The performance of the proposed method is evaluated through various security measures. It gives good results as number of pixel change rate is closer to 100% and unified average changing intensity is 33.46.

Wireless sensor networks (WSNs) when combined with Internet-of-things (IoT) enable a wide range of applications across multiple domains. Sensor nodes in these wireless sensor networks like any other Internet-connected device are resource constrained and vulnerable to a variety of malicious attacks thereby compromising security. Consequently, a secure and efficient lightweight cryptographic protocol is required that can provide a balance between end-to-end security offering all features but yet lightweight. For secure data transmission and access, newer multi-factor authentication and key management features must be developed as majority of existing techniques have high computational overheads and are vulnerable to a wide range of attacks. In this paper, we propose a Rabin-assisted three-factor authentication protocol that uses the computational asymmetry of Rabin cryptosystem in addition to user password, smartcard and biometric for increased security. NS2 based simulation proves that the proposed protocol outperforms the baseline ad-hoc on-demand distance vector (AODV) protocol in terms of throughput, computation cost, and delay performance. Also, it has the ability to tolerate most common attacks and offers additional functionality features thereby offering a lightweight and highly secure protocol that can be extended to other critical domains like Smart Transportation Systems (STS), Smart grids, Smart buildings etc.

With the emergence of deep sub-micron technology, the reliability issues in on-chip interconnect has enormously increased. This includes single event upsets, like soft errors, and hard faults which are rapidly becoming important factors to be considered. Aggressive technology scaling renders these architectures vulnerable to system performance, intermittent failure, capacitive crosstalk, and power dissipation problem. To improve the performance of the area and power consumption along with bandwidth, throughput and latency, a Buffer Potency Power Gating Technique is carried out to predict the Intermittent Failure by Presage Debacle Model in Network on Chip. In the Presage Debacle Model, the links present in the application-specific topology are provided with real application traffic to assist in prediction of the intermittent failure. By predicting the failure, the output of the model provides the power dissipated over the connection of the application link. Then the crosstalk noise in the topology is reduced by the Adaption of Simulated Annealing (SA) based Particle Swarm Optimization (PSO) congruence algorithm. The optimization congruence algorithm assigns each task in the application software to each node in the topology to determine the network's optimal output. It continuously changes the task park position in the topology system and reduces the crosstalk of the NoC. Consecutively, the area and power consumption are minimized by Buffer Potency Power Gating Technique. Buffer efficiency is increased and the power-gating is a practical way to mitigate NoC power control schemes properly and effectively without any loss in performance. Thus the prediction of intermittent failure is obtained by utilizing NoC to improve the performance of area and power consumption.

Millimeter wave communication suffers from static blockages such as trees, buildings and so on. Reconfigurable Intelligent Surfaces (RISs) has been adapted to solve this blockage problem and enable the urban environment user to choose mmW enabled small cells as their source transmitter or to choose macro cells in case of non-line of sight exists. RIS is a promising network technology to improve the quality of service parameters such as spectral efficiency and energy efficiency by artificially reconfiguring the propagation environment of electromagnetic waves. An indoor user can be connected with mmW band if the line of sight (LOS) link exists. Otherwise, the system utilizes RIS transmission model to have reliable and low-latency communication. It reflects the capability of RISs to enable enhanced communications in challenging environments. An optimization problem is formulated to maximize the sum data rate of an indoor user by phase shift optimization at the RIS. The outage probability of the proposed scheme is analyzed under Rician fading channel. The proposed RIS enabled method targets to enhance the overall performance in terms of average spectral efficiency and achievable data rate in the presence of blockages and system imperfections. The data rate is increased by three fold times than that of the transmission without RIS. The utility of this framework is discussed for both indoor and outdoor environments.