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Gursoy, M. Emre, Rajasekar, Vivekanand, Liu, Ling.  2020.  Utility-Optimized Synthesis of Differentially Private Location Traces. 2020 Second IEEE International Conference on Trust, Privacy and Security in Intelligent Systems and Applications (TPS-ISA). :30—39.
Differentially private location trace synthesis (DPLTS) has recently emerged as a solution to protect mobile users' privacy while enabling the analysis and sharing of their location traces. A key challenge in DPLTS is to best preserve the utility in location trace datasets, which is non-trivial considering the high dimensionality, complexity and heterogeneity of datasets, as well as the diverse types and notions of utility. In this paper, we present OptaTrace: a utility-optimized and targeted approach to DPLTS. Given a real trace dataset D, the differential privacy parameter ε controlling the strength of privacy protection, and the utility/error metric Err of interest; OptaTrace uses Bayesian optimization to optimize DPLTS such that the output error (measured in terms of given metric Err) is minimized while ε-differential privacy is satisfied. In addition, OptaTrace introduces a utility module that contains several built-in error metrics for utility benchmarking and for choosing Err, as well as a front-end web interface for accessible and interactive DPLTS service. Experiments show that OptaTrace's optimized output can yield substantial utility improvement and error reduction compared to previous work.
Gu, Yanyang, Zhang, Ping, Chen, Zhifeng, Cao, Fei.  2020.  UEFI Trusted Computing Vulnerability Analysis Based on State Transition Graph. 2020 IEEE 6th International Conference on Computer and Communications (ICCC). :1043–1052.
In the face of increasingly serious firmware attacks, it is of great significance to analyze the vulnerability security of UEFI. This paper first introduces the commonly used trusted authentication mechanisms of UEFI. Then, aiming at the loopholes in the process of UEFI trust verification in the startup phase, combined with the state transition diagram, PageRank algorithm and Bayesian network theory, the analysis model of UEFI trust verification startup vulnerability is constructed. And according to the example to verify the analysis. Through the verification and analysis of the data obtained, the vulnerable attack paths and key vulnerable nodes are found. Finally, according to the analysis results, security enhancement measures for UEFI are proposed.
Chu, Wen-Yi, Yu, Ting-Guang, Lin, Yu-Kai, Lee, Shao-Chuan, Hsiao, Hsu-Chun.  2020.  On Using Camera-based Visible Light Communication for Security Protocols. 2020 IEEE Security and Privacy Workshops (SPW). :110–117.
In security protocol design, Visible Light Communication (VLC) has often been abstracted as an ideal channel that is resilient to eavesdropping, manipulation, and jamming. Camera Communication (CamCom), a subcategory of VLC, further strengthens the level of security by providing a visually verifiable association between the transmitter and the extracted information. However, the ideal security guarantees of visible light channels may not hold in practice due to limitations and tradeoffs introduced by hardware, software, configuration, environment, etc. This paper presents our experience and lessons learned from implementing CamCom for security protocols. We highlight CamCom's security-enhancing properties and security applications that it enables. Backed by real implementation and experiments, we also systematize the practical considerations of CamCom-based security protocols.
Ahmed, Farooq, Li, Xudong, Niu, Yukun, Zhang, Chi, Wei, Lingbo, Gu, Chengjie.  2020.  UniRoam: An Anonymous and Accountable Authentication Scheme for Cross-Domain Access. 2020 International Conference on Networking and Network Applications (NaNA). :198—205.
In recent years, cross-domain roaming through Wi-Fi is ubiquitous, and the number of roaming users has increased dramatically. It is essential to authenticate users belonging to different institutes to ensure network privacy and security. Existing systems, such as eduroam, have centralized and hierarchical structure on indorse accounts that create privacy and security issues. We have proposed UniRoam, a blockchain-based cross-domain authentication scheme that provides accountability and anonymity without any trusted authority. Unlike traditional centralized approaches, UniRoam provides access authentication for its servers and users to provide anonymity and accountability without any privacy leakage issues efficiently. By using the sovrin identifier as an anonymous identity, we integrate our system with Hyperledger and Intel SGX to authenticate users that preserves both anonymity and trust when the user connects to the network. Therefore, UniRoam is highly “faulted-tolerant” to deal with different attacks and provides an effective solution that can be deployed easily in different environments.
Coulter, Rory, Zhang, Jun, Pan, Lei, Xiang, Yang.  2020.  Unmasking Windows Advanced Persistent Threat Execution. 2020 IEEE 19th International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom). :268—276.

The advanced persistent threat (APT) landscape has been studied without quantifiable data, for which indicators of compromise (IoC) may be uniformly analyzed, replicated, or used to support security mechanisms. This work culminates extensive academic and industry APT analysis, not as an incremental step in existing approaches to APT detection, but as a new benchmark of APT related opportunity. We collect 15,259 APT IoC hashes, retrieving subsequent sandbox execution logs across 41 different file types. This work forms an initial focus on Windows-based threat detection. We present a novel Windows APT executable (APT-EXE) dataset, made available to the research community. Manual and statistical analysis of the APT-EXE dataset is conducted, along with supporting feature analysis. We draw upon repeat and common APT paths access, file types, and operations within the APT-EXE dataset to generalize APT execution footprints. A baseline case analysis successfully identifies a majority of 117 of 152 live APT samples from campaigns across 2018 and 2019.

Piplai, A., Ranade, P., Kotal, A., Mittal, S., Narayanan, S. N., Joshi, A..  2020.  Using Knowledge Graphs and Reinforcement Learning for Malware Analysis. 2020 IEEE International Conference on Big Data (Big Data). :2626—2633.

Machine learning algorithms used to detect attacks are limited by the fact that they cannot incorporate the back-ground knowledge that an analyst has. This limits their suitability in detecting new attacks. Reinforcement learning is different from traditional machine learning algorithms used in the cybersecurity domain. Compared to traditional ML algorithms, reinforcement learning does not need a mapping of the input-output space or a specific user-defined metric to compare data points. This is important for the cybersecurity domain, especially for malware detection and mitigation, as not all problems have a single, known, correct answer. Often, security researchers have to resort to guided trial and error to understand the presence of a malware and mitigate it.In this paper, we incorporate prior knowledge, represented as Cybersecurity Knowledge Graphs (CKGs), to guide the exploration of an RL algorithm to detect malware. CKGs capture semantic relationships between cyber-entities, including that mined from open source. Instead of trying out random guesses and observing the change in the environment, we aim to take the help of verified knowledge about cyber-attack to guide our reinforcement learning algorithm to effectively identify ways to detect the presence of malicious filenames so that they can be deleted to mitigate a cyber-attack. We show that such a guided system outperforms a base RL system in detecting malware.

Agirre, I., Onaindia, P., Poggi, T., Yarza, I., Cazorla, F. J., Kosmidis, L., Grüttner, K., Abuteir, M., Loewe, J., Orbegozo, J. M. et al..  2020.  UP2DATE: Safe and secure over-the-air software updates on high-performance mixed-criticality systems. 2020 23rd Euromicro Conference on Digital System Design (DSD). :344–351.
Following the same trend of consumer electronics, safety-critical industries are starting to adopt Over-The-Air Software Updates (OTASU) on their embedded systems. The motivation behind this trend is twofold. On the one hand, OTASU offer several benefits to the product makers and users by improving or adding new functionality and services to the product without a complete redesign. On the other hand, the increasing connectivity trend makes OTASU a crucial cyber-security demand to download latest security patches. However, the application of OTASU in the safety-critical domain is not free of challenges, specially when considering the dramatic increase of software complexity and the resulting high computing performance demands. This is the mission of UP2DATE, a recently launched project funded within the European H2020 programme focused on new software update architectures for heterogeneous high-performance mixed-criticality systems. This paper gives an overview of UP2DATE and its foundations, which seeks to improve existing OTASU solutions by considering safety, security and availability from the ground up in an architecture that builds around composability and modularity.
Tyagi, H., Vardy, A..  2015.  Universal Hashing for Information-Theoretic Security. Proceedings of the IEEE. 103:1781–1795.
The information-theoretic approach to security entails harnessing the correlated randomness available in nature to establish security. It uses tools from information theory and coding and yields provable security, even against an adversary with unbounded computational power. However, the feasibility of this approach in practice depends on the development of efficiently implementable schemes. In this paper, we review a special class of practical schemes for information-theoretic security that are based on 2-universal hash families. Specific cases of secret key agreement and wiretap coding are considered, and general themes are identified. The scheme presented for wiretap coding is modular and can be implemented easily by including an extra preprocessing layer over the existing transmission codes.
Ekşim, A., Demirci, T..  2020.  Ultimate Secrecy in Cooperative and Multi-hop Wireless Communications. 2020 XXXIIIrd General Assembly and Scientific Symposium of the International Union of Radio Science. :1–4.
In this work, communication secrecy in cooperative and multi-hop wireless communications for various radio frequencies are examined. Attenuation lines and ranges of both detection and ultimate secrecy regions were calculated for cooperative communication channel and multi-hop channel with various number of hops. From results, frequency ranges with the highest potential to apply bandwidth saving method known as frequency reuse were determined and compared to point-to-point channel. Frequencies with the highest attenuation were derived and their ranges of both detection and ultimate secrecy are calculated. Point-to-point, cooperative and multi-hop channels were compared in terms of ultimate secrecy ranges. Multi-hop channel measurements were made with different number of hops and the relation between the number of hops and communication security is examined. Ultimate secrecy ranges were calculated up to 1 Terahertz and found to be less than 13 meters between 550-565 GHz frequency range. Therefore, for short-range wireless communication systems such as indoor and in-device communication systems (board-to-board or chip-to-chip communications), it is shown that various bands in the Terahertz band can be used to reuse the same frequency in different locations to obtain high security and high bandwidth.
Ben-Yaakov, Y., Meyer, J., Wang, X., An, B..  2020.  User detection of threats with different security measures. 2020 IEEE International Conference on Human-Machine Systems (ICHMS). :1—6.

Cyber attacks and the associated costs made cybersecurity a vital part of any system. User behavior and decisions are still a major part in the coping with these risks. We developed a model of optimal investment and human decisions with security measures, given that the effectiveness of each measure depends partly on the performance of the others. In an online experiment, participants classified events as malicious or non-malicious, based on the value of an observed variable. Prior to making the decisions, they had invested in three security measures - a firewall, an IDS or insurance. In three experimental conditions, maximal investment in only one of the measures was optimal, while in a fourth condition, participants should not have invested in any of the measures. A previous paper presents the analysis of the investment decisions. This paper reports users' classifications of events when interacting with these systems. The use of security mechanisms helped participants gain higher scores. Participants benefited in particular from purchasing IDS and/or Cyber Insurance. Participants also showed higher sensitivity and compliance with the alerting system when they could benefit from investing in the IDS. Participants, however, did not adjust their behavior optimally to the security settings they had chosen. The results demonstrate the complex nature of risk-related behaviors and the need to consider human abilities and biases when designing cyber security systems.

Naik, N., Jenkins, P..  2020.  uPort Open-Source Identity Management System: An Assessment of Self-Sovereign Identity and User-Centric Data Platform Built on Blockchain. 2020 IEEE International Symposium on Systems Engineering (ISSE). :1—7.

Managing identity across an ever-growing digital services landscape has become one of the most challenging tasks for security experts. Over the years, several Identity Management (IDM) systems were introduced and adopted to tackle with the growing demand of an identity. In this series, a recently emerging IDM system is Self-Sovereign Identity (SSI) which offers greater control and access to users regarding their identity. This distinctive feature of the SSI IDM system represents a major development towards the availability of sovereign identity to users. uPort is an emerging open-source identity management system providing sovereign identity to users, organisations, and other entities. As an emerging identity management system, it requires meticulous analysis of its architecture, working, operational services, efficiency, advantages and limitations. Therefore, this paper contributes towards achieving all of these objectives. Firstly, it presents the architecture and working of the uPort identity management system. Secondly, it develops a Decentralized Application (DApp) to demonstrate and evaluate its operational services and efficiency. Finally, based on the developed DApp and experimental analysis, it presents the advantages and limitations of the uPort identity management system.

Malek, Z. S., Trivedi, B., Shah, A..  2020.  User behavior Pattern -Signature based Intrusion Detection. 2020 Fourth World Conference on Smart Trends in Systems, Security and Sustainability (WorldS4). :549—552.

Technology advancement also increases the risk of a computer's security. As we can have various mechanisms to ensure safety but still there have flaws. The main concerned area is user authentication. For authentication, various biometric applications are used but once authentication is done in the begging there was no guarantee that the computer system is used by the authentic user or not. The intrusion detection system (IDS) is a particular procedure that is used to identify intruders by analyzing user behavior in the system after the user logged in. Host-based IDS monitors user behavior in the computer and identify user suspicious behavior as an intrusion or normal behavior. This paper discusses how an expert system detects intrusions using a set of rules as a pattern recognized engine. We propose a PIDE (Pattern Based Intrusion Detection) model, which is verified previously implemented SBID (Statistical Based Intrusion Detection) model. Experiment results indicate that integration of SBID and PBID approach provides an extensive system to detect intrusion.

Dylan Wang, Melody Moh, Teng-Sheng Moh.  2020.  Using Deep Learning to Solve Google reCAPTCHA v2’s Image Challenges.

The most popular CAPTCHA service in use today is Google reCAPTCHA v2, whose main offering is an image-based CAPTCHA challenge. This paper looks into the security measures used in reCAPTCHA v2's image challenges and proposes a deep learning-based solution that can be used to automatically solve them. The proposed method is tested with both a custom object- detection deep learning model as well as Google's own Cloud Vision API, in conjunction with human mimicking mouse movements to bypass the challenges. The paper also suggests some potential defense measures to increase overall security and other additional attack directions for reCAPTCHA v2.

Al-Emadi, S., Al-Mohannadi, A., Al-Senaid, F..  2020.  Using Deep Learning Techniques for Network Intrusion Detection. 2020 IEEE International Conference on Informatics, IoT, and Enabling Technologies (ICIoT). :171—176.
In recent years, there has been a significant increase in network intrusion attacks which raises a great concern from the privacy and security aspects. Due to the advancement of the technology, cyber-security attacks are becoming very complex such that the current detection systems are not sufficient enough to address this issue. Therefore, an implementation of an intelligent and effective network intrusion detection system would be crucial to solve this problem. In this paper, we use deep learning techniques, namely, Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN) to design an intelligent detection system which is able to detect different network intrusions. Additionally, we evaluate the performance of the proposed solution using different evaluation matrices and we present a comparison between the results of our proposed solution to find the best model for the network intrusion detection system.
Drakopoulos, G., Giotopoulos, K., Giannoukou, I., Sioutas, S..  2020.  Unsupervised Discovery Of Semantically Aware Communities With Tensor Kruskal Decomposition: A Case Study In Twitter. 2020 15th International Workshop on Semantic and Social Media Adaptation and Personalization (SMA. :1–8.
Substantial empirical evidence, including the success of synthetic graph generation models as well as of analytical methodologies, suggests that large, real graphs have a recursive community structure. The latter results, in part at least, in other important properties of these graphs such as low diameter, high clustering coefficient values, heavy degree distribution tail, and clustered graph spectrum. Notice that this structure need not be official or moderated like Facebook groups, but it can also take an ad hoc and unofficial form depending on the functionality of the social network under study as for instance the follow relationship on Twitter or the connections between news aggregators on Reddit. Community discovery is paramount in numerous applications such as political campaigns, digital marketing, crowdfunding, and fact checking. Here a tensor representation for Twitter subgraphs is proposed which takes into consideration both the followfollower relationships but also the coherency in hashtags. Community structure discovery then reduces to the computation of Tucker tensor decomposition, a higher order counterpart of the well-known unsupervised learning method of singular value decomposition (SVD). Tucker decomposition clearly outperforms the SVD in terms of finding a more compact community size distribution in experiments done in Julia on a Twitter subgraph. This can be attributed to the facts that the proposed methodology combines both structural and functional Twitter elements and that hashtags carry an increased semantic weight in comparison to ordinary tweets.
Uzhga-Rebrov, O., Kuleshova, G..  2020.  Using Singular Value Decomposition to Reduce Dimensionality of Initial Data Set. 2020 61st International Scientific Conference on Information Technology and Management Science of Riga Technical University (ITMS). :1–4.
The purpose of any data analysis is to extract essential information implicitly present in the data. To do this, it often seems necessary to transform the initial data into a form that allows one to identify and interpret the essential features of their structure. One of the most important tasks of data analysis is to reduce the dimension of the original data. The paper considers an approach to solving this problem based on singular value decomposition (SVD).
Srivastava, V., Pathak, R. K., Kumar, A., Prakash, S..  2020.  Using a Blend of Brassard and Benett 84 Elliptic Curve Digital Signature for Secure Cloud Data Communication. 2020 International Conference on Electronics and Sustainable Communication Systems (ICESC). :738–743.

The exchange of data has expanded utilizing the web nowadays, but it is not dependable because, during communication on the cloud, any malicious client can alter or steal the information or misuse it. To provide security to the data during transmission is becoming hot research and quite challenging topic. In this work, our proposed algorithm enhances the security of the keys by increasing its complexity, so that it can't be guessed, breached or stolen by the third party and hence by this, the data will be concealed while sending between the users. The proposed algorithm also provides more security and authentication to the users during cloud communication, as compared to the previously existing algorithm.

Lei, M., Jin, M., Huang, T., Guo, Z., Wang, Q., Wu, Z., Chen, Z., Chen, X., Zhang, J..  2020.  Ultra-wideband Fingerprinting Positioning Based on Convolutional Neural Network. 2020 International Conference on Computer, Information and Telecommunication Systems (CITS). :1—5.

The Global Positioning System (GPS) can determine the position of any person or object on earth based on satellite signals. But when inside the building, the GPS cannot receive signals, the indoor positioning system will determine the precise position. How to achieve more precise positioning is the difficulty of an indoor positioning system now. In this paper, we proposed an ultra-wideband fingerprinting positioning method based on a convolutional neural network (CNN), and we collect the dataset in a room to test the model, then compare our method with the existing method. In the experiment, our method can reach an accuracy of 98.36%. Compared with other fingerprint positioning methods our method has a great improvement in robustness. That results show that our method has good practicality while achieves higher accuracy.

Xie, J., Zhang, M., Ma, Y..  2019.  Using Format Migration and Preservation Metadata to Support Digital Preservation of Scientific Data. 2019 IEEE 10th International Conference on Software Engineering and Service Science (ICSESS). :1—6.

With the development of e-Science and data intensive scientific discovery, it needs to ensure scientific data available for the long-term, with the goal that the valuable scientific data should be discovered and re-used for downstream investigations, either alone, or in combination with newly generated data. As such, the preservation of scientific data enables that not only might experiment be reproducible and verifiable, but also new questions can be raised by other scientists to promote research and innovation. In this paper, we focus on the two main problems of digital preservation that are format migration and preservation metadata. Format migration includes both format verification and object transformation. The system architecture of format migration and preservation metadata is presented, mapping rules of object transformation are analyzed, data fixity and integrity and authenticity, digital signature and so on are discussed and an example is shown in detail.

Wang, W., Xuan, S., Yang, W., Chen, Y..  2019.  User Credibility Assessment Based on Trust Propagation in Microblog. 2019 Computing, Communications and IoT Applications (ComComAp). :270—275.

Nowadays, Microblog has become an important online social networking platform, and a large number of users share information through Microblog. Many malicious users have released various false news driven by various interests, which seriously affects the availability of Microblog platform. Therefore, the evaluation of Microblog user credibility has become an important research issue. This paper proposes a microblog user credibility evaluation algorithm based on trust propagation. In view of the high consumption and low precision caused by malicious users' attacking algorithms and manual selection of seed sets by establishing false social relationships, this paper proposes two optimization strategies: pruning algorithm based on social activity and similarity and based on The seed node selection algorithm of clustering. The pruning algorithm can trim off the attack edges established by malicious users and normal users. The seed node selection algorithm can efficiently select the highly available seed node set, and finally use the user social relationship graph to perform the two-way propagation trust scoring, so that the low trusted user has a lower trusted score and thus identifies the malicious user. The related experiments verify the effectiveness of the trustworthiness-based user credibility evaluation algorithm in the evaluation of Microblog user credibility.

Anyfantis, D. I., Sarigiannidou, E., Rapenne, L., Stamatelatos, A., Ntemogiannis, D., Kapaklis, V., Poulopoulos, P..  2019.  Unexpected Development of Perpendicular Magnetic Anisotropy in Ni/NiO Multilayers After Mild Thermal Annealing. IEEE Magnetics Letters. 10:1–5.
We report on the significant enhancement of perpendicular magnetic anisotropy of Ni/NiO multilayers after mild annealing up to 90 min at 250 °C. Transmission electron microscopy shows that after annealing, a partial crystallization of the initially amorphous NiO layers occurs. This turns out to be the source of the anisotropy enhancement. Magnetic measurements reveal that even multilayers with Ni layers as thick as 7 nm, which in the as-deposited state showed inplane anisotropy with square hysteresis loops, show reduced in-plane remanence after thermal treatment. Hysteresis loops recorded with the field in the normal-to-film-plane direction provide evidence for perpendicular magnetic anisotropy with up and down magnetic domains at remanence. A plot of effective uniaxial magnetic anisotropy constant times individual Ni layer thickness as a function of individual Ni layer thickness shows a large change in the slope of the data attributed to a drastic change of volume anisotropy. Surface anisotropy showed a small decrease because of some layer roughening introduced by annealing.
Haddad, G. El, Aïmeur, E., Hage, H..  2018.  Understanding Trust, Privacy and Financial Fears in Online Payment. 2018 17th IEEE International Conference On Trust, Security And Privacy In Computing And Communications/ 12th IEEE International Conference On Big Data Science And Engineering (TrustCom/BigDataSE). :28–36.
In online payment, customers must transmit their personal and financial information through the website to conclude their purchase and pay the services or items selected. They may face possible fears from online transactions raised by their risk perception about financial or privacy loss. They may have concerns over the payment decision with the possible negative behaviors such as shopping cart abandonment. Therefore, customers have three major players that need to be addressed in online payment: the online seller, the payment page, and their own perception. However, few studies have explored these three players in an online purchasing environment. In this paper, we focus on the customer concerns and examine the antecedents of trust, payment security perception as well as their joint effect on two fundamentally important customers' aspects privacy concerns and financial fear perception. A total of 392 individuals participated in an online survey. The results highlight the importance, of the seller website's components (such as ease of use, security signs, and quality information) and their impact on the perceived payment security as well as their impact on customer's trust and financial fear perception. The objective of our study is to design a research model that explains the factors contributing to an online payment decision.
Alruwaythi, M., Kambampaty, K., Nygard, K..  2018.  User Behavior Trust Modeling in Cloud Security. 2018 International Conference on Computational Science and Computational Intelligence (CSCI). :1336–1339.
Evaluating user behavior in cloud computing infrastructure is important for both Cloud Users and Cloud Service Providers. The service providers must ensure the safety of users who access the cloud. User behavior can be modeled and employed to help assess trust and play a role in ensuring authenticity and safety of the user. In this paper, we propose a User Behavior Trust Model based on Fuzzy Logic (UBTMFL). In this model, we develop user history patterns and compare them current user behavior. The outcome of the comparison is sent to a trust computation center to calculate a user trust value. This model considers three types of trust: direct, history and comprehensive. Simulation results are included.
Paul, S., Padhy, N. P., Mishra, S. K., Srivastava, A. K..  2019.  UUCA: Utility-User Cooperative Algorithm for Flexible Load Scheduling in Distribution System. 2019 8th International Conference on Power Systems (ICPS). :1—6.
Demand response analysis in smart grid deployment substantiated itself as an important research area in recent few years. Two-way communication between utility and users makes peak load reduction feasible by delaying the operation of deferrable appliances. Flexible appliance rescheduling is preferred to the users compared to traditional load curtailment. Again, if users' preferences are accounted into appliance transferring process, then customers concede a little discomfort to help the utility in peak reduction. This paper presents a novel Utility-User Cooperative Algorithm (UUCA) to lower total electricity cost and gross peak demand while preserving users' privacy and preferences. Main driving force in UUCA to motivate the consumers is a new cost function for their flexible appliances. As a result, utility will experience low peak and due to electricity cost decrement, users will get reduced bill. However, to maintain privacy, the behaviors of one customer have not be revealed either to other customers or to the central utility. To justify the effectiveness, UUCA is executed separately on residential, commercial and industrial customers of a distribution grid. Harmony search optimization technique has proved itself superior compared to other heuristic search techniques to prove efficacy of UUCA.
Ekşim, A., Demirci, T..  2019.  Ultimate Secrecy in Wireless Communications. 2019 11th International Conference on Electrical and Electronics Engineering (ELECO). :682–686.
In this work, communication secrecy in the physical layer for various radio frequencies is examined. Frequencies with the highest level of secrecy in 1-1000 GHz range and their level of communication secrecy are derived. The concept of ultimate secrecy in wireless communications is proposed. Attenuation lines and ranges of both detection and ultimate secrecy are calculated for transmitter powers from 1 W to 1000 W. From results, frequencies with the highest potential to apply bandwidth saving method known as frequency reuse are devised. Commonly used secrecy benchmarks for the given conditions are calculated. Frequencies with the highest attenuation are devised and their ranges of both detection and ultimate secrecy are calculated.