Biblio

New Taipei City is a metropolitan area in Taiwan, with a prosperous economy and a large population. Many parades and public assemblies are held within the jurisdiction and they might have unintended consequences such as traffic, overcrowding, or even serious risks and mishaps. Many cities around the world face similar problems and threats, such as domestic violence in Turkey and the Boston Marathon bombing. Combating new types of public safety threats has become a primary task. Setting up a complete surveillance system at an event site would give a command center access to the site and allow them to respond to an incident in real time. The system's digital records would help track a crime scene, as well as providing insight into how criminals commit crimes. The New Taipei City Police Department has established an "All-Round 3D Technology Security Circle" equipped with various cameras that transmit images back to a command center based on an Internet of Things connection. Innovative technology combined with smart equipment gives three-dimensional coverage of an area to ensure public safety.

In the era of Industry 4.0 (IR 4.0), information leakage has become a critical issue for information security. The basic approach to addressing information leakage threats is to implement an information security policy (ISP) that defines the standards, boundaries, and responsibilities of users of information and technology of an organization. ISPs are one of the most commonly used methods for controlling internal user security behaviours, which include, but not limited to, computer usage ethics; organizational system usage policies; Internet and email usage policies; and the use of social media. Human error is the main security threat to information security, resulting from negligence, ignorance, and failure to adhere to organizational information security policies. Information security incidents are a problem related to human behaviour because technology is designed and operated by humans, presenting the opportunities and spaces for human error. In addition to the factor of human error as the main source of information leakage, this study aims to systematically analyse the fundamental issues of information security policy compliance. An analysis of these papers identifies and categories critical factor that effect an employee's attitude toward compliance with ISP. The human, process, technology element and information governance should be thought as a significant scope for more efficiency of information security policy compliance and in any further extensive studies to improve on information security policy compliance. Therefore, to ensure these are properly understood, further study is needed to identity the information governance that needs to be included in organizations and current best practices for developing an information security policy compliance within organizations.

In this work, we use a subjective approach to compute cyber resilience metrics for industrial control systems. We utilize the extended form of the R4 resilience framework and span the metrics over physical, technical, and organizational domains of resilience. We develop a qualitative cyber resilience assessment tool using the framework and a subjective questionnaire method. We make sure the questionnaires are realistic, balanced, and pertinent to ICS by involving subject matter experts into the process and following security guidelines and standards practices. We provide detail mathematical explanation of the resilience computation procedure. We discuss several usages of the qualitative tool by generating simulation results. We provide a system architecture of the simulation engine and the validation of the tool. We think the qualitative simulation tool would give useful insights for industrial control systems' overall resilience assessment and security analysis.

Industrial control systems (ICS) have been widely adopted in mission-critical infrastructures. However, the increasing prevalence of cyberattacks targeting them has been a critical security concern. On the other hand, the high real-time and availability requirements of ICS limits the applicability of certain available security solutions due to the performance overhead they introduce and the system unavailability they cause. Moreover, scientific metrics (mathematical models) are not available to evaluate the efficiency and resilience of security solutions in the ICS context. Hence, in this paper, we propose ICS-SEA to address the ICS design constraints of Security, Efficiency, and Availability (SEA). Our ICS-SEA formally models the real-time constraints and physical-state resiliency quantitatively based on a typical ICS. We then design two real-world ICS testbeds and evaluate the efficiency and resilience of a few selected security solutions using our defined models. The results show that our ICS-SEA is effective to evaluate security solutions against the SEA conflicting design constraints in ICS.

The supply chains for advanced automobiles will continue to become increasingly complex. Furthermore, automotive OEMs will experience decreased control over the components and software implemented into their vehicles. These issues create risks to advanced vehicle technologies that must be addressed by a comprehensive and coordinated approach to end-to-end cybersecurity across the automotive supply chain.

Although Vehicle Named Data Network (VNDN) possess the communication benefits of Named Data Network and Vehicle Opportunity Network, it also introduces some new privacy problems, including the identity security of Data Requesters and Data Providers. Data providers in VNDN need to sign data packets directly, which will leak the identity information of the providers, while the vicinity malicious nodes can access the sensitive information of Data Requesters by analyzing the relationship between Data Requesters and the data names in Interest Packages that are sent directly in plaintext. In order to solve the above privacy problems, this paper presents an identity privacy protection strategy for Data Requesters and Data Providers in VNDN. A ring signature scheme is used to hide the correlation between the signature and the data provider and the anonymous proxy idea is used to protect the real identity of the data requester in the proposed strategy. Security Analysis and experiments in the ONE-based VNDN platform indicate that the proposed strategy is effective and practical.

Vehicle ad-hoc network (VANET) is the main driving force to alleviate traffic congestion and accelerate the construction of intelligent transportation. However, the rapid growth of the number of vehicles makes the construction of the safety system of the vehicle network facing multiple tests. This paper proposes an identity-based aggregate signature scheme to protect the privacy of vehicle identity, receive messages in time and authenticate quickly in VANET. The scheme uses aggregate signature algorithm to aggregate the signatures of multiple users into one signature, and joins the idea of batch authentication to complete the authentication of multiple vehicular units, thereby improving the verification efficiency. In addition, the pseudoidentity of vehicles is used to achieve the purpose of vehicle anonymity and privacy protection. Finally, the secure storage of message signatures is effectively realized by using reliable cloud storage technology. Compared with similar schemes, this paper improves authentication efficiency while ensuring security, and has lower storage overhead.

The main objective of this paper is to present a more secured and computationally efficient procedure of encrypting and decrypting images using the enigma algorithm in comparison to the existing methods. Available literature on image encryptions and descriptions are not highly secured in every case.To achieve more secured image processing for highly advanced technologies, a proposed algorithm can be the process used in enigma machine for image encryption and decryption. Enigma machine is piece of spook hardware that was used frequently during the World War II by the Germans. This paper describes the detailed algorithm along with proper demonstration of several essential components present in an enigma machine that is required for image security. Each pixel in a colorful picture can be represented by RGB (Red, Green, Blue) value. The range of RGB values is 0 to 255 that states the red, green and blue intensity of a particular picture.These RGB values are accessed one by one and changed into another by various steps and hence it is not possible to track the original RGB value. In order to retrieve the original image, the receiver needs to know the setting of the enigma. To compare the decrypted image with the original one,these two images are subtracted and their results are also discussed in this paper.

Information security is winding up noticeably more vital in information stockpiling and transmission. Images are generally utilised for various purposes. As a result, the protection of image from the unauthorised client is critical. Established encryption techniques are not ready to give a secure framework. To defeat this, image encryption is finished through DNA encoding which is additionally included with confused 1D and 2D logistic maps. The key communication is done through the quantum channel using the BB84 protocol. To recover the encrypted image DNA decoding is performed. Since DNA encryption is invertible, decoding can be effectively done through DNA subtraction. It decreases the complexity and furthermore gives more strength when contrasted with traditional encryption plans. The enhanced strength of the framework is measured utilising measurements like NPCR, UACI, Correlation and Entropy.

The purpose of this paper is to improve the safety of chaotic image encryption algorithm. Firstly, to achieve this goal, it put forward two improved chaotic system logistic and henon, which covered an promoted henon chaotic system with better probability density, and an 2-dimension logistic chaotic system with high Lyapunov exponents. Secondly, the chaotic key stream was generated by the new 2D logistic chaotic system and optimized henon mapping, which mixed in dynamic proportions. The conducted sequence has better randomness and higher safety for image cryptosystem. Thirdly, we proposed algorithm takes advantage of the compounded chaotic system Simulation experiment results and security analysis showed that the proposed scheme was more effective and secure. It can resist various typical attacks, has high security, satisfies the requirements of image encryption theoretical.

The core operation of all cryptosystems based on Elliptic Curve Cryptography is Elliptic Curve Point Multiplication. Depending on implementation it can be vulnerable to different Side Channel Analysis attacks exploiting information leakage, such as power consumption or execution time. Multiple countermeasures against these attacks have been developed over time, each having different impact on parameters of the cryptosystem. This paper summarizes popular countermeasures for simple and differential power analysis attacks on Elliptic Curve cryptosystems. Presented secure algorithms were implemented in Verilog hardware description language and synthesized to logic gates for power trace generation.

The growing use of MANETs and its vulnerability to attacks due to its fundamental characteristics make secure routing one of the most considerable challenges. In this paper, a new security scheme for mobile ad hoc networks (MANETs) is presented. The proposed scheme used Trivium lightweight stream cipher algorithm in combination with HMAC to secure the routing control packets. This paper compares the performance of the AODV after implementing the security scheme in terms of throughput, delay sum (end-to-end), jitter sum (end-to-end) and packet loss ratio. The proposed scheme shows better performance than original AODV under blackhole attack.

Network intrusion detection is an important component of network security. Currently, the popular detection technology used the traditional machine learning algorithms to train the intrusion samples, so as to obtain the intrusion detection model. However, these algorithms have the disadvantage of low detection rate. Deep learning is more advanced technology that automatically extracts features from samples. In view of the fact that the accuracy of intrusion detection is not high in traditional machine learning technology, this paper proposes a network intrusion detection model based on convolutional neural network algorithm. The model can automatically extract the effective features of intrusion samples, so that the intrusion samples can be accurately classified. Experimental results on KDD99 datasets show that the proposed model can greatly improve the accuracy of intrusion detection.

Chinese Remainder Theorem (CRT) is one of the spatial domain methods that is more implemented in the data hiding method watermarking. CRT is used to improve security and imperceptibility in the watermarking method. CRT is rarely studied in studies that discuss steganographic images. Steganography research focuses more on increasing imperceptibility, embedded payload, and message security, so methods like LSB are still popular to be developed to date. CRT and LSB have some similarities such as default payload capacity and both are methods in the spatial domain which can produce good imperceptibility quality of stego image. But CRT is very superior in terms of security, so CRT is also widely used in cryptographic algorithms. Some ways to increase imperceptibility in image steganography are edge detection and spread spectrum embedding. This research proposes a combination of edge detection techniques and spread-spectrum embedding based on the CRT method to produce imperceptibility and safe image steganography method. Based on the test results it is proven that the combination of the proposed methods can increase imperceptibility of CRT-based steganography based on SSIM metric.

In this paper, we focus on versatile and scalable key management for Advanced Metering Infrastructure (AMI) in Smart Grid (SG). We show that a recently proposed key graph based scheme for AMI systems (VerSAMI) suffers from efficiency flaws in its broadcast key management protocol. Then, we propose a new key management scheme (iVerSAMI) by modifying VerSAMI's key graph structure and proposing a new broadcast key update process. We analyze security and performance of the proposed broadcast key management in details to show that iVerSAMI is secure and efficient in terms of storage and communication overheads.

To preserve the privacy of social networks, most existing methods are applied to satisfy different anonymity models, but there are some serious problems such as huge large information losses and great structural modifications of original social network. Therefore, an improved privacy protection method called k-subgraph is proposed, which is based on k-degree anonymous graph derived from k-anonymity to keep the network structure stable. The method firstly divides network nodes into several clusters by label propagation algorithm, and then reconstructs the sub-graph by means of moving edges to achieve k-degree anonymity. Experimental results show that our k-subgraph method can not only effectively improve the defense capability against malicious attacks based on node degrees, but also maintain stability of network structure. In addition, the cost of information losses due to anonymity is minimized ideally.

In the era of the ever-growing number of smart devices, fraudulent practices through Phishing Websites have become an increasingly severe threat to modern computers and internet security. These websites are designed to steal the personal information from the user and spread over the internet without the knowledge of the user using the system. These websites give a false impression of genuinity to the user by mirroring the real trusted web pages which then leads to the loss of important credentials of the user. So, Detection of such fraudulent websites is an essence and the need of the hour. In this paper, various classifiers have been considered and were found that ensemble classifiers predict to utmost efficiency. The idea behind was whether a combined classifier model performs better than a single classifier model leading to a better efficiency and accuracy. In this paper, for experimentation, three Meta Classifiers, namely, AdaBoostM1, Stacking, and Bagging have been taken into consideration for performance comparison. It is found that Meta Classifier built by combining of simple classifier(s) outperform the simple classifier's performance.

Deep learning has undergone tremendous advancements in computer vision studies. The training of deep learning neural networks depends on a considerable amount of ground truth datasets. However, labeling ground truth data is a labor-intensive task, particularly for large-volume video analytics applications such as video surveillance and vehicles detection for autonomous driving. This paper presents a rapid and accurate method for associative searching in big image data obtained from security monitoring systems. We developed a semi-automatic moving object annotation method for improving deep learning models. The proposed method comprises three stages, namely automatic foreground object extraction, object annotation in subsequent video frames, and dataset construction using human-in-the-loop quick selection. Furthermore, the proposed method expedites dataset collection and ground truth annotation processes. In contrast to data augmentation and data generative models, the proposed method produces a large amount of real data, which may facilitate training results and avoid adverse effects engendered by artifactual data. We applied the constructed annotation dataset to train a deep learning you-only-look-once (YOLO) model to perform vehicle detection on street intersection surveillance videos. Experimental results demonstrated that the accurate detection performance was improved from a mean average precision (mAP) of 83.99 to 88.03.

The understanding of measured jitter is improved in three ways. First, it is shown that the measured jitter is not only governed by written-in jitter and the reader resolution along the cross-track direction but by remanence noise in the vicinity of transitions and the down-track reader resolution as well. Second, a novel data analysis scheme is introduced that allows for an unambiguous separation of these two contributions. Third, based on data analyses involving the first two learnings and micro-magnetic simulations, we identify and explain the root causes for variations of jitter with write current (WC) (write field), WC overshoot amplitude (write-field rise time), and linear disk velocity measured for heat-assisted magnetic recording.

The rapid growth of computer systems which generate graph data necessitates employing privacy-preserving mechanisms to protect users' identity. Since structure-based de-anonymization attacks can reveal users' identity's even when the graph is simply anonymized by employing naïve ID removal, recently, k- anonymity is proposed to secure users' privacy against the structure-based attack. Most of the work ensured graph privacy using fake edges, however, in some applications, edge addition or deletion might cause a significant change to the key property of the graph. Motivated by this fact, in this paper, we introduce a novel method which ensures privacy by adding fake nodes to the graph. First, we present a novel model which provides k- anonymity against one of the strongest attacks: seed-based attack. In this attack, the adversary knows the partial mapping between the main graph and the graph which is generated using the privacy-preserving mechanisms. We show that even if the adversary knows the mapping of all of the nodes except one, the last node can still have k- anonymity privacy. Then, we turn our attention to the privacy of the graphs generated by inter-domain routing against degree attacks in which the degree sequence of the graph is known to the adversary. To ensure the privacy of networks against this attack, we propose a novel method which tries to add fake nodes in a way that the degree of all nodes have the same expected value.

Traditional network routing protocol exhibits high statics and singleness, which provide significant advantages for the attacker. There are two kinds of attacks on the network: active attacks and passive attacks. Existing solutions for those attacks are based on replication or detection, which can deal with active attacks; but are helpless to passive attacks. In this paper, we adopt the theory of network coding to fragment the data in the Software-Defined Networks and propose a network coding-based resilient multipath routing scheme. First, we present a new metric named expected eavesdropping ratio to measure the resilience in the presence of passive attacks. Then, we formulate the network coding-based resilient multipath routing problem as an integer-programming optimization problem by using expected eavesdropping ratio. Since the problem is NP-hard, we design a Simulated Annealing-based algorithm to efficiently solve the problem. The simulation results demonstrate that the proposed algorithms improve the defense performance against passive attacks by about 20% when compared with baseline algorithms.

The present paper describes some of the results obtained in the Faculty of Computer Systems and Technology at Technical University of Sofia in the implementation of project related to the application of intelligent methods for increasing the security in computer networks. Also is made a survey about existing hybrid methods, which are using several artificial intelligent methods for cyber defense. The paper introduces a model for intrusion detection systems where multi agent systems are the bases and artificial intelligence are applicable by the means simple real-time models constructed in laboratory environment.

A method to increase the resiliency of in-cone logic locking against the SAT attack is described in this paper. Current logic locking techniques provide protection through the addition of circuitry outside of the original logic cone. While the additional circuitry provides provable security against the SAT attack, other attacks, such as the removal attack, limit the efficacy of such techniques. Traditional in-cone logic locking is not prone to removal attacks, but is less secure against the SAT attack. The focus of this paper is, therefore, the analysis of in-cone logic locking to increase the security against the SAT attack, which provides a comparison between in-cone techniques and newly developed methodologies. A novel algorithm is developed that utilizes maximum fanout free cones (MFFC). The application of the algorithm limits the fanout of incorrect key information. The MFFC based algorithm resulted in an average increase of 61.8% in the minimum number of iterations required to complete the SAT attack across 1,000 different variable orderings of the circuit netlist while restricted to a 5% overhead in area.

The goal of this document is to provide knowledge of Security for Industrial Control Systems (ICS,) such as supervisory control and data acquisition (SCADA) which is implemented in power transmission network, power stations, power distribution grids and other big infrastructures that affect large number of persons and security of nations. A distinction between IT and ICS security is given to make a difference between the two disciplines. In order to avoid intrusion and destruction of industrials plants, some recommendations are given to preserve their security.

IIoT devices are sourced in many different countries and contain many components including hardware, software, and firmware. Each of these devices and components have a supply chain that can be compromised at many points including by the manufacturer, the software libraries, the shippers, the distributors and more.