Biblio

Digital images are becoming the backbone of the social platform. To day of life of the people, the high impact of the images has raised the concern of its authenticity. Image forensics need to be done to assure the authenticity. In this paper, a novel technique is proposed for digital image forensics. The proposed technique is applied for detection of median, averaging and Gaussian filtering in the images. In the proposed method, a first image is normalized using optimal range to obtain a better statistical information. Further, difference arrays are calculated on the normalized array and a proposed thresholding is applied on the normalized arrays. In the last, co-occurrence features are extracted from the thresholding difference arrays. In experimental analysis, significant performance gain is achieved. The detection capability of the proposed method remains upstanding on small size images even with low quality JPEG compression.

Mobile security remains a concern for multiple stakeholders. Safe user behavior is crucial key to avoid and mitigate mobile threats. The research used a survey design to capture key constructs of mobile user threat avoidance behavior. Analysis revealed that there is no significant difference between the two key drivers of secure behavior, threat appraisal and coping appraisal, for Android and iOS users. However, statistically significant differences in avoidance motivation and avoidance behavior of users of the two operating systems were displayed. This indicates that existing threat avoidance models may be insufficient to comprehensively deal with factors that affect mobile user behavior. A newly introduced variable, perceived security, shows a difference in the perceptions of their level of protection among the users of the two operating systems, providing a new direction for research into mobile security.

This paper summarizes the types and contents of enterprise big data information, analyzes the demand and characteristics of enterprise shared data information based on the Internet of things, and analyzes the current situation of enterprise big data fusion at home and abroad. Firstly, using the idea of the Internet of things for reference, the intelligent sensor is used as the key component of data acquisition, and the multi energy data acquisition technology is discussed. Then the data information of entity enterprises is taken as the research object and a low energy consumption transmission method based on data fusion mechanism for industrial ubiquitous Internet of things is proposed. Finally, a network monitoring and data fusion platform for the industrial Internet of things is implemented. The monitoring node networking and platform usability test are also performed. It is proved that the scheme can achieve multi parameter, real-time, high reliable network intelligent management.

With powerful ability to organize, retrieve and share information, cloud computing technology has effectively improved the development of intelligent learning ecological Communities. The study finds development create a security atmosphere with all homomorphic encryption technology, virtualization technology to prevent the leakage and loss of information data. The result provided a helpful guideline to build a security environment for intelligent ecological communities.

The competency to use mobile devices for work-related tasks gives advantages to the company productiveness and expedites business processes. Thus Bring Your Own Device (BYOD) setting emerge to enable work flexibility and technological compatibility. For management, employees’ productivity is important, but they could not jeopardise the security of information and data stored in the corporate network. Securing data and network becomes more complex tasks as it deals with foreign devices, i.e., devices that do not belong to the organisation. With much research focused on pre-implementation and the technical aspects of mobile device usage, post-implementation advancement is receiving less attention. IS audit as one of the post-implementation mechanisms provides performance evaluation of existing IS assets, business operations and process implementation, thus helping management formulating the best strategies in optimising IS practices. This paper discusses the feasibility of IS audit in assessing mobile device security by exploring the risks and vulnerabilities of mobile devices for organisational IS security as well as the perception of Information system management in mobile device security. By analysing related literature, authors pointed out how the references used in the current IS audit research address the mobile device security. This work serves a significant foundation in the future development in mobile device audit.

Wireless Sensor Network consists of hundreds to thousands of tiny sensor nodes deployed in the large field of the target phenomenon. Sensor nodes have advantages for its size, multifunctional, and inexpensive features; unfortunately, the resources are limited in terms of memory, computational, and in energy, especially. Network transmission between nodes and base station (BS) needs to be carefully designed to prolong the network life cycle. As the data transmission is energy consuming compared to data processing, designing sensor nodes into hierarchical network architecture is preferable because it can limit the network transmission. LEACH is one of the hierarchical network protocols known for simple and energy saving protocols. There are lots of modification made since LEACH was introduced for more energy efficient purposed. In this paper, hybridization of LEACH-C and LEACH-R and the modification have been presented for a more energy saving LEACH called LEACH-CR. Experimental result was compared with previous LEACH variant and showed to has advantages over the existing LEACH protocols in terms of energy consumption, dead/alive nodes, and the packet sent to Base Station. The result reflects that the consideration made for residual energy to select the cluster head and proximity transmission lead to a better energy consumption in the network.

In recent years, cyber security has become a challenging task to protect the networks and computing systems from various types of digital attacks. Therefore, to preserve these systems, various innovative methods have been reported and implemented in practice. However, still more research work needs to be carried out to have malware free computing system. In this paper, an attempt has been made to develop simple but reliable ML based malware detection systems which can be implemented in practice. Keeping this in view, the present paper has proposed and compared the performance of three ML based malware detection systems applicable for computer systems. The proposed methods include k-NN, RF and LR for detection purpose and the features extracted comprise of Byte and ASM. The performance obtained from the simulation study of the proposed schemes has been evaluated in terms of ROC, Log loss plot, accuracy, precision, recall, specificity, sensitivity and F1-score. The analysis of the various results clearly demonstrates that the RF based malware detection scheme outperforms the model based on k-NN and LR The efficiency of detection of proposed ML models is either same or comparable to deep learning-based methods.

There has been a significant rise in ransomware attacks over the last few years. Cyber attackers have made use of tried and true ransomware viruses to target the government, health care, and educational institutions. Ransomware variants can be purchased on the dark web by amateurs giving them the same attack tools used by professional cyber attackers without experience or skill. Traditional antivirus and antimalware products have improved, but they alone fall short when it comes to catching and stopping ransomware attacks. Employee training has become one of the most important aspects of being prepared for attempted cyberattacks. However, training alone only goes so far; human error is still the main entry point for malware and ransomware infections. In this paper, we propose a multi-layered defense approach to safeguard against ransomware. We have come to the startling realization that it is not a matter of “if” your organization will be hit with ransomware, but “when” your organization will be hit with ransomware. If an organization is not adequately prepared for an attack or how to respond to an attack, the effects can be costly and devastating. Our approach proposes having innovative antimalware software on the local machines, properly configured firewalls, active DNS/Web filtering, email security, backups, and staff training. With the implementation of this layered defense, the attempt can be caught and stopped at multiple points in the event of an attempted ransomware attack. If the attack were successful, the layered defense provides the option for recovery of affected data without paying a ransom.

Recently, with the increasing number of large-scale remote sensing images, the demand for large-scale remote sensing image object classification is growing and attracting the interest of many researchers. Hashing, because of its low memory requirements and high time efficiency, has widely solve the problem of large-scale remote sensing image. Supervised hashing methods mainly leverage the label information of remote sensing image to learn hash function, however, the similarity of the original feature space cannot be well preserved, which can not meet the accurate requirements for object classification of remote sensing image. To solve the mentioned problem, we propose a novel method named Optimized Projection Supervised Discrete Hashing(OPSDH), which jointly learns a discrete binary codes generation and optimized projection constraint model. It uses an effective optimized projection method to further constraint the supervised hash learning and generated hash codes preserve the similarity based on the data label while retaining the similarity of the original feature space. The experimental results show that OPSDH reaches improved performance compared with the existing hash learning methods and demonstrate that the proposed method is more efficient for operational applications.

Cloud computing enables users and organizations to conveniently store and share data in large volumes and to enjoy on-demand services. Security and the protection of big data sharing from various attacks is the most challenging issue. Proxy re-encryption (PRE) is an effective method to improve the security of data sharing in the cloud environment. However, in PRE schemes, offloading big data for re-encryption will impose a heavy computational burden on the cloud proxy server, resulting in an increased computation delay and response time for the users. In this paper, we propose a novel parallel PRE workload distribution scheme to dynamically route the big data re-encryption process into the fog of the network. Moreover, this paper proposes a dynamic load balancing technique to avoid an excessive workload for the fog nodes. It also uses lightweight asymmetric cryptography to provide end-to-end security for the big data sharing between users. Within the proposed scheme, the offloading overhead on the centralized cloud server is effectively mitigated. Meanwhile, the processing delay incurred by the big data re-encryption process is efficiently improved.

In line with the rapid development of the Internet of Things (IoT) network, the challenges faced are ensuring the network performance is capable to support the communication of these IoT devices. As a result, the routing protocols can provide fast route discovery and network maintenance by considering the IoT network's resource constraints. This paper's main contributions are to identify compatible IoT routing protocol using qualitative method and factor that affect network performance. Routing Protocol for Low Power and Lossy Networks (RPL) is a proactive distance- vector routing protocol designed as a proposed standard to meet the requirements of the Low Power and Lossy Networks (LLN). In this project, four influential factors on the performance of RPL in Contiki OS are examined using the Cooja simulator and then RPL performance is assessed in terms of Packet Delivery Ratio (PDR), Energy consumption and Overhead control message for the network. The project provides an insight into the implications of traffic patterns, transmission ranges, network size and node mobility for different scenarios. The results of the simulation show that the PDR and overhead ratio increases proportional to transmission distances increases but decreases while radio interference is increased. From the mobility aspect, PDR decreases by an average of 19.5% when the mobility nodes expand. On the other hand, energy consumption increases by an average of 63.7% and control message size increased up to 213% when the network consists of 40 percent of mobility nodes.

Aiming at the problem of low detection accuracy in traditional power system network intrusion detection methods, in order to improve the performance of power system network intrusion detection, a power system network intrusion detection method based on random forest algorithm is proposed. Firstly, the power system network intrusion sub sample is selected to construct the random forest decision tree. The random forest model is optimized by using the edge function. The accuracy of the vector is judged by the minimum state vector of the power system network, and the measurement residual of the power system network attack is calculated. Finally, the power system network intrusion data set is clustered by Gaussian mixture clustering Through the design of power system network intrusion detection process, the power system network intrusion detection is realized. The experimental results show that the power system network intrusion detection method based on random forest algorithm has high network intrusion detection performance.

With rapid advancement of Smart Grid as well as Internet of Things (IoT), current power distribution communication network faces the challenges of satisfying the emerging data transmission requirements of ubiquitous secure coverage for distributed power services. This paper focuses on secure ubiquitous wireless communication solution for power distribution Internet of Things (PDİoT) in Smart Grid. Detailed secure ubiquitous wireless communication networking topology is presented, and integrated encryption and communication device is developed. The proposed solution supports several State Secret cryptographic algorithm including SM1/SM2/SM3/SM4 as well as forward and reverse isolation functions, thus achieving secure wireless communication for PDİoT services.

In this paper, we mainly introduce the security protection technology of smart grid based on edge computing and propose an edge computing security protection architecture based on multi-service flexible mechanism. Aiming at the real time requirements of heterogeneous energy terminal access and power edge computing business in multiple interactive environment, a real-time and strong compatibility terminal security access mechanism integrating physical characteristics and lightweight cryptographic mechanism is proposed. According to different power terminal security data requirements, the edge computing data transmission, processing security and privacy protection technology are proposed. In addition, in the power system of distribution, microgrid and advanced metering system, the application of edge computing has been well reflected. Combined with encryption technology, access authentication, the security defense of edge data, edge equipment and edge application is carried out in many aspects, which strengthens the security and reliability of business penetration and information sharing at the edge of power grid, and realizes the end-to-end and end-to-system security prevention and control of power grid edge computing.

Scalability is one of the effective features of the Software Defined Network (SDN) that allows several devices to communicate with each other. In SDN scalable networks, the number of hosts keeps increasing as per networks need. This increment makes network administrators take a straightforward action to ensure these hosts' authenticity in the network. To address this issue, we proposed a technique to authenticate SDN hosts before permitting them to establish communication with the SDN controller. In this technique, we used the Kerberos authentication protocol to ensure the authenticity of the hosts. Kerberos verifies the hosts' credentials using a centralized server contains all hosts IDs and passwords. This technique eases the secure communication between the hosts and controller and allows the hosts to safely get network rules and policies. The proposed technique ensures the immunity of the network against network attacks.

In state estimation of steady-state power networks, a cyber attack that cannot be detected from the residual (i.e., the estimation error) is called a false data injection attack. In this paper, to enforce security of power networks, we propose a method of detecting a false data injection attack. In the proposed method, a false data injection attack is detected by randomly choosing sensors used in state estimation. The effectiveness of the proposed method is presented by two numerical examples including the IEEE 14-bus system.

Recently, several Internet of Things Tools have been created, contributing to growing network loads. To refrain from IoT should use the idea of cognitive radio networks because of the lack of bandwidth. This article presents much of the research discusses the distribution of channels and preparation of packets when combining cognitive radio networks with IoT technology and we are further discussing the spectrum-based Features and heterogeneity in cognitive IoT Security. Surveying the research performed in this field reveals that the work performed is still developing. A variety of inventions and experiments are part of its initial phases.

Recently, information leakage accidents have been continuously occurring due to cyberattacks, and internal information leakage has also been occurring additionally. In this situation, many hacking accidents and DDoS attacks related to IoT are reported, and cyber threat detection field is expanding. Therefore, in this study, the trend related to the commercialization and generalization of IoT technology and the degree of standardization of IoT have been analyzed. Based on the reality of IoT analyzed through this process, research and analysis on what points are required in IoT security control was conducted, and then IoT security control strategy was presented. In this strategy, the IoT environment was divided into IoT device, IoT network/communication, and IoT service/platform in line with the basic strategic framework of 'Pre-response-accident response-post-response', and the strategic direction of security control was established suitable for each of them.

The popularity of Unmanned Aerial Vehicles (UAV) or more commonly known as Drones is increasing recently. UAVs have tremendous potential in various industries, e.g., military, agriculture, transportation, movie, supply chain, and surveillance. UAVs are also popular among hobbyists for photography, racing, etc. Despite the possibilities, many UAV related security incidents are reported nowadays. UAVs can be targeted by malicious parties and if compromised, life-threatening activities can be performed using them. As a result, governments around the world have started to regulate the use of UAVs. We believe that UAVs need an intelligent and automated defense mechanism to ensure the safety of humans, properties, and the UAVs themselves. A major component where we can incorporate the defense mechanism is the operating system. In this paper, we investigate the security of existing operating systems used in consumer and commercial UAVs. We then survey various security issues of UAV operating systems and possible solutions. Finally, we discuss several research challenges for developing a secure operating system for UAVs.

In electronic warfare, communication may not counter reconnaissance and jamming without the help of network-station selection of frequency hopping. The competition in the field of electromagnetic spectrum is becoming more and more fierce with the increasingly complex electromagnetic environment of modern battlefield. The research on detection, identification, parameter estimation and network station selection of frequency hopping communication network has aroused the interest of scholars both at home and abroad, which has been summarized in this paper. Firstly, the working mode and characteristics of two kinds of FH communication networking modes synchronous orthogonal network and asynchronous non orthogonal network are introduced. Then, through the analysis of FH signals time hopping, frequency hopping, bandwidth, frequency, direction of arrival, bad time-frequency analysis, clustering analysis and machine learning method, the feature-based method is adopted Parameter selection technology is used to sort FH network stations. Finally, the key and difficult points of current research on FH communication network separation technology and the research status of blind source separation technology are introduced in details in this paper.

The widespread utilization of smart grids is due to their flexibility to support the two-way flow of electricity and data. The critical nature of smart grids evokes traditional network attacks. Due to the advantages of blockchains in terms of ensuring trustworthiness and security, a significant body of literature has been recently developed to secure smart grid operations. We categorize the blockchain applications in smart grid into three categories: energy trading, infrastructure management, and smart-grid operations management. This paper provides an extensive survey of these works and the different ways to utilize blockchains in smart grid in general. We propose an abstract system to overcome a critical cyberattack; namely, the fake data injection, as previous works did not consider such an attack.

Video Conferencing has emerged as a new paradigm of communication in the age of COVID-19 pandemic. This technology is allowing us to have real-time interaction during the social distancing era. Even before the current crisis, it was increasingly commonplace for organizations to adopt a video conferencing tool. As people adopt video conferencing tools and access data with potentially less secure equipment and connections, meetings are becoming a target to cyber attackers. Enforcing appropriate security and privacy settings prevents attackers from exploiting the system. To design the video conferencing system's security and privacy model, an exhaustive threat model must be adopted. Threat modeling is a process of optimizing security by identifying objectives, vulnerabilities, and defining the plan to mitigate or prevent potential threats to the system. In this paper, we use the widely accepted STRIDE threat modeling technique to identify all possible risks to video conferencing tools and suggest mitigation strategies for creating a safe and secure system.

Transportation of people and goods is important and crucial in the context of smart cities. The trend in regard of people's mobility is moving from privately owned vehicles towards shared mobility. This trend is even stronger in urban areas, where space for parking is limited, and the mobility is supported by the public transport system, which lowers the need for private vehicles. Several challenges and barriers of currently available solutions retard a massive growth of this mobility option, such as the trust problem, data monopolism, or intermediary costs. Decentralizing mobility management is a promising approach to solve the current problems of the mobility market, allowing to move towards a more usable internet of mobility and smart transportation. Leveraging blockchain technology allows to cut intermediary costs, by utilizing smart contracts. Important in this ecosystem is the proof of identity of participants in the blockchain network. To proof the possession of the claimed identity, the private key corresponding to the wallet address is utilized, and therefore essential to protect. In this paper, a blockchain-based shared mobility platform is proposed and a proof-of-concept is shown. First, current problems and state-of-the-art systems are analyzed. Then, a decentralized concept is built based on ERC-721 tokens, implemented in a smart contract, and augmented with a Hardware Security Module (HSM) to protect the confidential key material. Finally, the system is evaluated and compared against state-of-the-art solutions.

With the development of deep learning technology, a large number of new technologies for video anomaly detection have emerged. This paper proposes a video anomaly detection algorithm based on the future frame prediction using Generative Adversarial Network (GAN) and attention mechanism. For the generation model, a U-Net model, is modified and added with an attention module. For the discrimination model, a Markov GAN discrimination model with self-attention mechanism is proposed, which can affect the generator and improve the generation quality of the future video frame. Experiments show that the new video anomaly detection algorithm improves the detection performance, and the attention module plays an important role in the overall detection performance. It is found that the more the attention modules are appliedthe deeper the application level is, the better the detection effect is, which also verifies the rationality of the model structure used in this project.

With the rapid development of the Internet, cyberspace security has become a potentially huge problem. At the same time, the disclosure of cyberspace vulnerabilities is getting faster and faster. Traditional protection methods based on known features cannot effectively defend against new network attacks. Network attack is no more a single vulnerability exploit, but an APT attack based on multiple complicated methods. Cyberspace attacks have become ``rationalized'' on the surface. Currently, there are a lot of researches about visualization of attack paths, but there is no an overall plan to reproduce the attack path. Most researches focus on the detection and characterization individual based on single behavior cyberspace attacks, which loose it's abilities to help security personnel understand the complete attack behavior of attackers. The key factors of this paper is to collect the attackers' aggressive behavior by reverse retrospective method based on the actual shooting range environment. By finding attack nodes and dividing offensive behavior into time series, we can characterize the attacker's behavior path vividly and comprehensively.