Biblio

At present, machine learning (ML) algorithms are essential components in designing the sophisticated intrusion detection system (IDS). They are building-blocks to enhance cyber threat detection and help in classification at host-level and network-level in a short period. The increasing global connectivity and advancements of network technologies have added unprecedented challenges and opportunities to network security. Malicious attacks impose a huge security threat and warrant scalable solutions to thwart large-scale attacks. These activities encourage researchers to address these imminent threats by analyzing a large volume of the dataset to tackle all possible ranges of attack. In this proposed method, we calculated the fitness value of each feature from the population by using a genetic algorithm (GA) and selected them according to the fitness value. The fitness values are presented in hierarchical order to show the effectiveness of problem decomposition. We implemented Support Vector Machine (SVM) to verify the consistency of the system outcome. The well-known NSL-knowledge discovery in databases (KDD) was used to measure the performance of the system. From the experiments, we achieved a notable classification accuracies using a SVM of the current state of the art intrusion detection.

Use of formal protocol description techniques and code generation can reduce bugs in network packet parsing code. However, such techniques are themselves complex, and don't see wide adoption in the protocol standards development community, where the focus is on consensus building and human-readable specifications. We explore the utility and effectiveness of new techniques for describing protocol data, specifically designed to integrate with the standards development process, and discuss how they can be used to generate code that is safer and more trustworthy, while maintaining correctness and performance.

The risk of detecting incidents in the field of computer technology in Maritime transport is considered. The structure of the computer incident investigation system and its functions are given. The system of conducting investigations of computer incidents on sea transport is considered. A possible algorithm for investigating the incident using the tools of forensic science and an algorithm for transmitting the received data for further processing are presented.

Differential evolution algorithm is an efficient computational method that uses population crossover and variation to achieve high-quality solutions. The algorithm is simple in principle and fast in solving global solutions, so it has been widely used in complex optimization problems. In this paper, we applied the differential evolution algorithm to a time-delay dynamic system for microbial fermentation of 1,3-propanediol and obtained an average error of 22.67% comparing to baseline error of 48.53%.

In the context of the Industry 4.0 initiative, Cyber-Physical Production Systems (CPPS) or Cyber Manufacturing Systems (CMS) can be characterized as advanced networked mechatronic production systems gaining their added value by interaction with different systems using advanced communication technologies. Appropriate wired and wireless communication technologies and standards need to add timing in combination with security concepts to realize the potential improvements in the production process. One of these standards is IO-Link Wireless, which is used for sensor/actuator network operation. In this paper cryptographic performance and energy efficiency of an IO-Link Wireless Device are analyzed. The power consumption and the influence of the cryptographic operations on the trans-mission timing of the IO-Link Wireless protocol are exemplary measured employing a Phytec module based on a CC2650 system-on-chip (SoC) radio transceiver [2]. Confidentiality is considered in combination with the cryptographic performance as well as the energy efficiency. Different cryptographic algorithms are evaluated using the on chip hardware accelerator compared to a cryptographic software implementation.

The Covid-19 Pandemic has caused huge losses worldwide and is still affecting people all around the world. Even after rigorous, incessant and dedicated efforts from people all around the world, it keeps mutating and spreading at an alarming rate. In times such as these, it is extremely important to take proper precautionary measures to stay safe and help to contain the spread of the virus. In this paper, we propose an innovative design of one such commonly used public disinfection method, an Automatic Walkthrough Sanitization Tunnel. It is a walkthrough sanitization tunnel which uses sensors to detect the target and automatically disinfects it followed by irradiation using UV-C rays for extra protection. There is a proposition to add an IoT based Temperature sensor and data relay module used to detect the temperature of any person entering the tunnel and in case of any anomaly, contact nearby covid wards to facilitate rapid treatment.

A kitchen underpinned by the Internet of Things (IoT) requires the management of complex procedural processes. This is due to the fact that when supporting an end-user in the preparation of even only one dish, various devices may need to coordinate with each other. Additionally, it is challenging— yet desirable—to enable an end-user to program their kitchen devices according to their preferred behaviour and to allow them to visualise and track their cooking workflows. In this paper, we compared two semantic representations, namely, Behaviour Trees and the DX-MAN model. We analysed these representations based on their suitability for a range of end-users (i.e., novice to experienced). The methodology required the analysis of smart kitchen user requirements, from which we inferred that the main architectural requirements for IoT cooking workflows are variability and compositionality. Guided by the user requirements, we examined various scenarios and analysed workflow complexity and feasibility for each representation. On the one hand, we found that execution complexity tends to be higher on Behaviour Trees. However, due to their fallback node, they provide more transparency on how to recover from unprecedented circumstances. On the other hand, parameter complexity tends to be somewhat higher for the DX-MAN model. Nevertheless, the DX-MAN model can be favourable due to its compositionality aspect and the ease of visualisation it can offer.

Internet of Things (IoT) devices are ubiquitous, with web cameras, smart refrigerators, and digital assistants appearing in homes, offices, and public spaces. However, these devices are lacking in security measures due to their low time to market and insufficient funding for security research and development. In order to improve the security of IoTs, we have defined novel security metrics based on generic IoT characteristics. Furthermore, we have developed automation for experimentation with IoT devices that results to repeatable and reproducible calculations of security metrics within a realistic IoT testbed. Our results demonstrate that repeatable IoT security measurements are feasible with automation. They prove quantitatively intuitive hypotheses. For example, an large number of inbound / outbound network connections contributes to higher probability of compromise or measuring password strength leads to a robust estimation of IoT security.

IoT devices have evolved with the goal of becoming more connected. However, for security it is necessary to reduce the attack surface by allowing only necessary devices to be connected. In addition, as the number of IoT devices increases, DDoS attacks targeting IoT devices also increase. In this paper, we propose a method to apply the zero trust concept of SDP as a way to enhance security and prevent DDoS attacks in the IoT device network to which the OCF platform, one of the IoT standard platforms, is applied. The protocol proposed in this paper needs to perform additional functions in IoT devices, and the processing overhead due to the functions is 62.6ms on average. Therefore, by applying the method proposed in this paper, although there is a small amount of processing overhead, DDoS attacks targeting the IoT network can be defended and the security of the IoT network can be improved.

Digitization has pioneered to drive exceptional changes across all industries in the advancement of analytics, automation, and Artificial Intelligence (AI) and Machine Learning (ML). However, new business requirements associated with the efficiency benefits of digitalization are forcing increased connectivity between IT and OT networks, thereby increasing the attack surface and hence the cyber risk. Cyber threats are on the rise and securing industrial networks are challenging with the shortage of human resource in OT field, with more inclination to IT/OT convergence and the attackers deploy various hi-tech methods to intrude the control systems nowadays. We have developed an innovative real-time ICS cyber test kit to obtain the OT industrial network traffic data with various industrial attack vectors. In this paper, we have introduced the industrial datasets generated from ICS test kit, which incorporate the cyber-physical system of industrial operations. These datasets with a normal baseline along with different industrial hacking scenarios are analyzed for research purposes. Metadata is obtained from Deep packet inspection (DPI) of flow properties of network packets. DPI analysis provides more visibility into the contents of OT traffic based on communication protocols. The advancement in technology has led to the utilization of machine learning/artificial intelligence capability in IDS ICS SCADA. The industrial datasets are pre-processed, profiled and the abnormality is analyzed with DPI. The processed metadata is normalized for the easiness of algorithm analysis and modelled with machine learning-based latest deep learning ensemble LSTM algorithms for anomaly detection. The deep learning approach has been used nowadays for enhanced OT IDS performances.

In the field of image steganography, edge detection based implantation methods play vital rules in providing stronger security of hided data. In this arena, researcher applies a suitable edge detection method to detect edge pixels in an image. Those detected pixels then conceive secret message bits. A very recent trend is to employ multiple edge detection methods to increase edge pixels in an image and thus to enhance the embedding capacity. The uses of multiple edge detectors additionally boost up the data security. Like as the demand for embedding capacity, many applications need to have the modified image, i.e., stego image, with good quality. Indeed, when the message payload is low, it will not be a better idea to finds more local pixels for embedding that small payload. Rather, the image quality will look better, visually and statistically, if we could choose a part but sufficient pixels to implant bits. In this article, we propose an algorithm that uses multiple edge detection algorithms to find edge pixels separately and then selects pixels which are common to all edges. This way, the proposed method decreases the number of embeddable pixels and thus, increases the image quality. The experimental results provide promising output.

An electronic medical record (EMR) is the digital medical data of a patient, and they are healthcare system's most valuable asset. In this paper, we introduce a decentralized network using blockchain technology and smart contracts as a solution to manage and secure medical records storing, and transactions between medical healthcare providers. Ethereum blockchain is employed to build the blockchain. Solidity object-oriented language was utilized to implement smart contracts to digitally facilitate and verify transactions across the network (creating records, access requests, permitting access, revoking access, rejecting access). This will mitigate prevailing issues of current systems and enhance their performance, since current EMRs are stored on a centralized database, which cannot guarantee data integrity and security, consequently making them susceptible to malicious attacks. Our proposed system approach is of vital importance considering that healthcare providers depend on various tests in making a decision about a patient's diagnosis, and the respective plan of treatment they will go through. These tests are not shared with other providers, while data is scattered on various systems, as a consequence of these ensuing scenarios, patients suffer of the resulting care provided. Moreover, blockchain can meliorate the motley serious challenges caused by future use of IoT devices that provide real-time data from patients. Therefore, integrating the two technologies will produce decentralized IoT based healthcare systems.

Controller area network is the serial communication protocol, which broadcasts the message on the CAN bus. The transmitted message is read by all the nodes which shares the CAN bus. The message can be eavesdropped and can be re-used by some other node by changing the information or send it by duplicate times. The message reused after some delay is replay attack. In this paper, the CAN network with three CAN nodes is implemented using the universal verification components and the replay attack is demonstrated by creating the faulty node. Two types of replay attack are implemented in this paper, one is to replay the entire message and the other one is to replay only the part of the frame. The faulty node uses the first replay attack method where it behaves like the other node in the network by duplicating the identifier. CAN frame except the identifier is reused in the second method which is hard to detect the attack as the faulty node uses its own identifier and duplicates only the data in the CAN frame.

The development of technologies makes it possible to increase the power of information processing systems, but the modernization of processors brings not only an increase in performance but also an increase in the number of errors and vulnerabilities that can allow an attacker to attack the system and gain access to confidential information. White-Box cryptography allows (due to its structure) not only monitoring possible changes but also protects the processed data even with full access of the attacker to the environment. Elliptic Curve Cryptography (ECC) due to its properties, is becoming stronger and stronger in our lives, as it allows you to get strong encryption at a lower cost of processing your own algorithm. This allows you to reduce the load on the system and increase its performance.

Enhancing Software Supply Chain Security. (a) The security of soft- ware used by the Federal Government is vital to the Federal Government’s ability to perform its critical functions. The development of commercial software often lacks transparency, sufficient focus on the ability of the software to resist attack, and adequate controls to prevent tampering by malicious actors. There is a pressing need to implement more rigorous and predictable mechanisms for ensuring that products function securely, and as intended. The security and integrity of ‘‘critical software’’—software that performs functions critical to trust (such as affording or requiring elevated system privileges or direct access to networking and computing resources)—is a particular concern. Accordingly, the Federal Government must take action to rapidly improve the security and integrity of the software supply chain, with a priority on addressing critical software.

In this work, the algorithm of increasing the information security of a communication system with Orthogonal Frequency Division Multiplexing (OFDM) was achieved by using a discrete-nonlinear Duffing system with dynamic chaos. The main idea of increasing information security is based on scrambling input information on three levels. The first one is mixing up data order, the second is scrambling data values and the final is mixing symbols at the Quadrature Amplitude Modulation (QAM) plot constellation. Each level's activities were made with the use of pseudorandom numbers set, generated by the discrete-nonlinear Duffing system with dynamic chaos.

Code-reuse attacks pose a threat to embedded devices since they are able to defeat common security defenses such as non-executable stacks. To succeed in his code-reuse attack, the attacker has to gain knowledge of some or all of the instructions of the target firmware/software. In case of a bare-metal firmware that is protected from being dumped out of a device, it is hard to know the running instructions of the target firmware. This consequently makes code-reuse attacks more difficult to achieve. This paper shows how an attacker can gain knowledge of some of these instructions by sniffing the unencrypted incremental updates. These updates exist to reduce the radio reception power for resource-constrained devices. Based on the literature, these updates are checked against authentication and integrity, but they are sometimes sent unencrypted. Therefore, it will be demonstrated how a Return-Oriented Programming (ROP) attack can be accomplished using only the passively sniffed incremental updates. The generated updates of the R3diff and Delta Generator (DG) differencing algorithms will be under assessment. The evaluation reveals that both of them can be exploited by the attacker. It also shows that the DG generated updates leak more information than the R3diff generated updates. To defend against this attack, different countermeasures that consider different power consumption scenarios are proposed, but yet to be evaluated.

Information and Communications Technology (ICT) supply chain risk management (SCRM) is the process of identifying and mitigating risks in the manufacture and distribution of ICT products and services. While the Information Technology (IT) sector and the Communications sector face different supply chain risks, their mitigation strategies are similar. Both sectors emphasize having an end-to-end Cyber-SCRM program, continuously evaluating risks to vendor networks, and maintaining geographically-diverse and occasionally-redundant supply chains in the event of a manufacturer compromise.

The fusion of infrared and visible images using traditional multi-scale decomposition methods often leads to the loss of detailed information or the blurring of image edges, which is because the contour information and the detailed information within the contour cannot be retained simultaneously in the fusion process. To obtain high-quality fused images, a hybrid multi-scale decomposition fusion method using co-occurrence and Gaussian filters is proposed in this research. At first, by making full use of the smoothing effect of the Gaussian filter and edge protection characteristic of the co-occurrence filter, source images are decomposed into multiple hierarchical structures with different characteristics. Then, characteristics of sub-images at each level are analyzed, and the corresponding fusion rules are designed for images at different levels. At last, the final fused image obtained by combining fused sub-images of each level has rich scene information and clear infrared targets. Compared with several traditional multi-scale fusion algorithms, the proposed method has great advantages in some objective evaluation indexes.

In the crowdsourced testing system, due to the openness of crowdsourced testing platform and other factors, the security of crowdsourced testing intellectual property cannot be effectively protected. We proposed an attribute-based double encryption scheme, combined with the blockchain technology, to achieve the data access control method of the code to be tested. It can meet the privacy protection and traceability of specific intellectual property in the crowdsourced testing environment. Through the experimental verification, the access control method is feasible, and the performance test is good, which can meet the normal business requirements.

Internet of Things (IoT) is flourishing in several application areas, such as smart cities, smart factories, smart homes, smart healthcare, etc. With the adoption of IoT in critical scenarios, it is crucial to investigate its security aspects. All the layers of IoT are vulnerable to severely disruptive attacks. However, the attacks in IoT Network layer have a high impact on communication between the connected objects. Routing in most of the IoT networks is carried out by IPv6 Routing Protocol for Low-Power and Lossy Networks (RPL). RPL-based IoT offers limited protection against routing attacks. A trust-based approach for routing security is suitable to be integrated with IoT systems due to the resource-constrained nature of devices. This research proposes a trust-based secure routing protocol to provide security against packet dropping attacks in RPL-based IoT networks. IoT networks are dynamic and consist of both static and mobile nodes. Hence the chosen trust metrics in the proposed method also include the mobility-based metrics for trust evaluation. The proposed solution is integrated into RPL as a modified objective function, and the results are compared with the default RPL objective function, MRHOF. The analysis and evaluation of the proposed protocol indicate its efficacy and adaptability in a mobile IoT environment.

Intrusion Detection System (IDS) is one of the most important security tool for many security issues that are prevailing in today's cyber world. Intrusion Detection System is designed to scan the system applications and network traffic to detect suspicious activities and issue an alert if it is discovered. So many techniques are available in machine learning for intrusion detection. The main objective of this project is to apply machine learning algorithms to the data set and to compare and evaluate their performances. The proposed application has used the SVM (Support Vector Machine) and ANN (Artificial Neural Networks) Algorithms to detect the intrusion rates. Each algorithm is used to detect whether the requested data is authorized or contains any anomalies. While IDS scans the requested data if it finds any malicious information it drops that request. These algorithms have used Correlation-Based and Chi-Squared Based feature selection algorithms to reduce the dataset by eliminating the useless data. The preprocessed dataset is trained and tested with the models to obtain the prominent results, which leads to increasing the prediction accuracy. The NSL KDD dataset has been used for the experimentation. Finally, an accuracy of about 48% has been achieved by the SVM algorithm and 97% has been achieved by ANN algorithm. Henceforth, ANN model is working better than the SVM on this dataset.