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2022-01-25
Shaikh, Fiza Saifan.  2021.  Augmented Reality Search to Improve Searching Using Augmented Reality. 2021 6th International Conference for Convergence in Technology (I2CT). :1—5.
In the current scenario we are facing the issue of real view which is object deal with image or in virtual world for such kind of difficulties the Augmented Reality has came into existence (AR). This paper deal with Augmented Reality Search (ARS). In this Augmented Reality Search (ARS) just user have to make the voice command and the Augmented Reality Search (ARS) will provide you real view of that object. Consider real world scenario where a student searched for NIT Bangalore then it will show the real view of that campus.
2022-01-10
Hu, Guangjun, Li, Haiwei, Li, Kun, Wang, Rui.  2021.  A Network Asset Detection Scheme Based on Website Icon Intelligent Identification. 2021 Asia-Pacific Conference on Communications Technology and Computer Science (ACCTCS). :255–257.
With the rapid development of the Internet and communication technologies, efficient management of cyberspace, safe monitoring and protection of various network assets can effectively improve the overall level of network security protection. Accurate, effective and comprehensive network asset detection is the prerequisite for effective network asset management, and it is also the basis for security monitoring and analysis. This paper proposed an artificial intelligence algorithm based scheme which accurately identify the website icon and help to determine the ownership of network assets. Through experiments based on data set collected from real network, the result demonstrate that the proposed scheme has higher accuracy and lower false alarm rate, and can effectively reduce the training cost.
Shirmarz, Alireza, Ghaffari, Ali, Mohammadi, Ramin, Akleylek, Sedat.  2021.  DDOS Attack Detection Accuracy Improvement in Software Defined Network (SDN) Using Ensemble Classification. 2021 International Conference on Information Security and Cryptology (ISCTURKEY). :111–115.
Nowadays, Denial of Service (DOS) is a significant cyberattack that can happen on the Internet. This attack can be taken place with more than one attacker that in this case called Distributed Denial of Service (DDOS). The attackers endeavour to make the resources (server & bandwidth) unavailable to legitimate traffic by overwhelming resources with malicious traffic. An appropriate security module is needed to discriminate the malicious flows with high accuracy to prevent the failure resulting from a DDOS attack. In this paper, a DDoS attack discriminator will be designed for Software Defined Network (SDN) architecture so that it can be deployed in the POX controller. The simulation results present that the proposed model can achieve an accuracy of about 99.4%which shows an outstanding percentage of improvement compared with Decision Tree (DT), K-Nearest Neighbour (KNN), Support Vector Machine (SVM) approaches.
Zhang, Qixin.  2021.  An Overview and Analysis of Hybrid Encryption: The Combination of Symmetric Encryption and Asymmetric Encryption. 2021 2nd International Conference on Computing and Data Science (CDS). :616–622.
In the current scenario, various forms of information are spread everywhere, especially through the Internet. A lot of valuable information is contained in the dissemination, so security issues have always attracted attention. With the emergence of cryptographic algorithms, information security has been further improved. Generally, cryptography encryption is divided into symmetric encryption and asymmetric encryption. Although symmetric encryption has a very fast computation speed and is beneficial to encrypt a large amount of data, the security is not as high as asymmetric encryption. The same pair of keys used in symmetric algorithms leads to security threats. Thus, if the key can be protected, the security could be improved. Using an asymmetric algorithm to protect the key and encrypting the message with a symmetric algorithm would be a good choice. This paper will review security issues in the information transmission and the method of hybrid encryption algorithms that will be widely used in the future. Also, the various characteristics of algorithms in different systems and some typical cases of hybrid encryption will be reviewed and analyzed to showcase the reinforcement by combining algorithms. Hybrid encryption algorithms will improve the security of the transmission without causing more other problems. Additionally, the way how the encryption algorithms combine to strength the security will be discussed with the aid of an example.
Sallam, Youssef F., Ahmed, Hossam El-din H., Saleeb, Adel, El-Bahnasawy, Nirmeen A., El-Samie, Fathi E. Abd.  2021.  Implementation of Network Attack Detection Using Convolutional Neural Network. 2021 International Conference on Electronic Engineering (ICEEM). :1–6.
The Internet obviously has a major impact on the global economy and human life every day. This boundless use pushes the attack programmers to attack the data frameworks on the Internet. Web attacks influence the reliability of the Internet and its administrations. These attacks are classified as User-to-Root (U2R), Remote-to-Local (R2L), Denial-of-Service (DoS) and Probing (Probe). Subsequently, making sure about web framework security and protecting data are pivotal. The conventional layers of safeguards like antivirus scanners, firewalls and proxies, which are applied to treat the security weaknesses are insufficient. So, Intrusion Detection Systems (IDSs) are utilized to screen PC and data frameworks for security shortcomings. IDS adds more effectiveness in securing networks against attacks. This paper presents an IDS model based on Deep Learning (DL) with Convolutional Neural Network (CNN) hypothesis. The model has been evaluated on the NSLKDD dataset. It has been trained by Kddtrain+ and tested twice, once using kddtrain+ and the other using kddtest+. The achieved test accuracies are 99.7% and 98.43% with 0.002 and 0.02 wrong alert rates for the two test scenarios, respectively.
Jayanthy, S., Nageswarvijay, S., Kumar, R. K. Rishi, Kanth, R. Krishna.  2021.  Smart Key Using AES Algorithm. 2021 Third International Conference on Inventive Research in Computing Applications (ICIRCA). :467–473.
This paper proposes a real time implementation of a smart key which is a Wi-Fi based device that helps to lock/unlock all kinds of doors. Internet access allows to control doors all over the world by a simple mobile application. The app developed can be used in two modes ADMIN and GUEST mode. The ADMIN mode is protected by pin/password and is encrypted by the Advanced Encryption Standard (AES) algorithm. The password can be stored in the Key store and it can be changed whenever required. The ADMIN mode has the privilege to authenticate the GUEST mode to access all doors. For GUEST mode authentication, guests have to request the admin by using the app. Firebase is used as a server where the device and the mobile app are connected to it. Firebase is fast and accurate and hence can be accessed quickly. The main advantage of this proposed method is that it is fully operated through Internet so it can locked/unlocked wherever from the world. Comparative analysis is taken for three algorithms i.e., AES, DES and 3-DES and AES algorithm has given the best results in terms of execution time and memory usage and is implemented in the hardware lock. The experimental results give the screen shots of the app in guest and admin mode, firebase data and hardware real time implementation of the smart lock on a door.
2021-12-22
Nascita, Alfredo, Montieri, Antonio, Aceto, Giuseppe, Ciuonzo, Domenico, Persico, Valerio, Pescapè, Antonio.  2021.  Unveiling MIMETIC: Interpreting Deep Learning Traffic Classifiers via XAI Techniques. 2021 IEEE International Conference on Cyber Security and Resilience (CSR). :455–460.
The widespread use of powerful mobile devices has deeply affected the mix of traffic traversing both the Internet and enterprise networks (with bring-your-own-device policies). Traffic encryption has become extremely common, and the quick proliferation of mobile apps and their simple distribution and update have created a specifically challenging scenario for traffic classification and its uses, especially network-security related ones. The recent rise of Deep Learning (DL) has responded to this challenge, by providing a solution to the time-consuming and human-limited handcrafted feature design, and better clas-sification performance. The counterpart of the advantages is the lack of interpretability of these black-box approaches, limiting or preventing their adoption in contexts where the reliability of results, or interpretability of polices is necessary. To cope with these limitations, eXplainable Artificial Intelligence (XAI) techniques have seen recent intensive research. Along these lines, our work applies XAI-based techniques (namely, Deep SHAP) to interpret the behavior of a state-of-the-art multimodal DL traffic classifier. As opposed to common results seen in XAI, we aim at a global interpretation, rather than sample-based ones. The results quantify the importance of each modality (payload- or header-based), and of specific subsets of inputs (e.g., TLS SNI and TCP Window Size) in determining the classification outcome, down to per-class (viz. application) level. The analysis is based on a publicly-released recent dataset focused on mobile app traffic.
2021-12-21
Zhai, Tongqing, Li, Yiming, Zhang, Ziqi, Wu, Baoyuan, Jiang, Yong, Xia, Shu-Tao.  2021.  Backdoor Attack Against Speaker Verification. ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). :2560–2564.
Speaker verification has been widely and successfully adopted in many mission-critical areas for user identification. The training of speaker verification requires a large amount of data, therefore users usually need to adopt third-party data (e.g., data from the Internet or third-party data company). This raises the question of whether adopting untrusted third-party data can pose a security threat. In this paper, we demonstrate that it is possible to inject the hidden backdoor for infecting speaker verification models by poisoning the training data. Specifically, we design a clustering-based attack scheme where poisoned samples from different clusters will contain different triggers (i.e., pre-defined utterances), based on our understanding of verification tasks. The infected models behave normally on benign samples, while attacker-specified unenrolled triggers will successfully pass the verification even if the attacker has no information about the enrolled speaker. We also demonstrate that existing back-door attacks cannot be directly adopted in attacking speaker verification. Our approach not only provides a new perspective for designing novel attacks, but also serves as a strong baseline for improving the robustness of verification methods. The code for reproducing main results is available at https://github.com/zhaitongqing233/Backdoor-attack-against-speaker-verification.
2021-12-20
Piccolboni, Luca, Guglielmo, Giuseppe Di, Carloni, Luca P., Sethumadhavan, Simha.  2021.  CRYLOGGER: Detecting Crypto Misuses Dynamically. 2021 IEEE Symposium on Security and Privacy (SP). :1972–1989.
Cryptographic (crypto) algorithms are the essential ingredients of all secure systems: crypto hash functions and encryption algorithms, for example, can guarantee properties such as integrity and confidentiality. Developers, however, can misuse the application programming interfaces (API) of such algorithms by using constant keys and weak passwords. This paper presents CRYLOGGER, the first open-source tool to detect crypto misuses dynamically. CRYLOGGER logs the parameters that are passed to the crypto APIs during the execution and checks their legitimacy offline by using a list of crypto rules. We compared CRYLOGGER with CryptoGuard, one of the most effective static tools to detect crypto misuses. We show that our tool complements the results of CryptoGuard, making the case for combining static and dynamic approaches. We analyzed 1780 popular Android apps downloaded from the Google Play Store to show that CRYLOGGER can detect crypto misuses on thousands of apps dynamically and automatically. We reverse-engineered 28 Android apps and confirmed the issues flagged by CRYLOGGER. We also disclosed the most critical vulnerabilities to app developers and collected their feedback.
2021-11-08
Chang, Sang-Yoon, Park, Younghee, Kengalahalli, Nikhil Vijayakumar, Zhou, Xiaobo.  2020.  Query-Crafting DoS Threats Against Internet DNS. 2020 IEEE Conference on Communications and Network Security (CNS). :1–9.
Domain name system (DNS) resolves the IP addresses of domain names and is critical for IP networking. Recent denial-of-service (DoS) attacks on Internet targeted the DNS system (e.g., Dyn), which has the cascading effect of denying the availability of the services and applications relying on the targeted DNS. In view of these attacks, we investigate the DoS on DNS system and introduce the query-crafting threats where the attacker controls the DNS query payload (the domain name) to maximize the threat impact per query (increasing the communications between the DNS servers and the threat time duration), which is orthogonal to other DoS approaches to increase the attack impact such as flooding and DNS amplification. We model the DNS system using a state diagram and comprehensively analyze the threat space, identifying the threat vectors which include not only the random/invalid domains but also those using the domain name structure to combine valid strings and random strings. Query-crafting DoS threats generate new domain-name payloads for each query and force increased complexity in the DNS query resolution. We test the query-crafting DoS threats by taking empirical measurements on the Internet and show that they amplify the DoS impact on the DNS system (recursive resolver) by involving more communications and taking greater time duration. To defend against such DoS or DDoS threats, we identify the relevant detection features specific to query-crafting threats and evaluate the defense using our prototype in CloudLab.
2021-10-12
Chang, Kai Chih, Nokhbeh Zaeem, Razieh, Barber, K. Suzanne.  2020.  Is Your Phone You? How Privacy Policies of Mobile Apps Allow the Use of Your Personally Identifiable Information 2020 Second IEEE International Conference on Trust, Privacy and Security in Intelligent Systems and Applications (TPS-ISA). :256–262.
People continue to store their sensitive information in their smart-phone applications. Users seldom read an app's privacy policy to see how their information is being collected, used, and shared. In this paper, using a reference list of over 600 Personally Identifiable Information (PII) attributes, we investigate the privacy policies of 100 popular health and fitness mobile applications in both Android and iOS app markets to find the set of personal information these apps collect, use and share. The reference list of PII was independently built from a longitudinal study at The University of Texas investigating thousands of identity theft and fraud cases where PII attributes and associated value and risks were empirically quantified. This research leverages the reference PII list to identify and analyze the value of personal information collected by the mobile apps and the risk of disclosing this information. We found that the set of PII collected by these mobile apps covers 35% of the entire reference set of PII and, due to dependencies between PII attributes, these mobile apps have a likelihood of indirectly impacting 70% of the reference PII if breached. For a specific app, we discovered the monetary loss could reach \$1M if the set of sensitive data it collects is breached. We finally utilize Bayesian inference to measure risks of a set of PII gathered by apps: the probability that fraudsters can discover, impersonate and cause harm to the user by misusing only the PII the mobile apps collected.
2021-10-04
Alsoghyer, Samah, Almomani, Iman.  2020.  On the Effectiveness of Application Permissions for Android Ransomware Detection. 2020 6th Conference on Data Science and Machine Learning Applications (CDMA). :94–99.
Ransomware attack is posting a serious threat against Android devices and stored data that could be locked or/and encrypted by such attack. Existing solutions attempt to detect and prevent such attack by studying different features and applying various analysis mechanisms including static, dynamic or both. In this paper, recent ransomware detection solutions were investigated and compared. Moreover, a deep analysis of android permissions was conducted to identify significant android permissions that can discriminate ransomware with high accuracy before harming users' devices. Consequently, based on the outcome of this analysis, a permissions-based ransomware detection system is proposed. Different classifiers were tested to build the prediction model of this detection system. After the evaluation of the ransomware detection service, the results revealed high detection rate that reached 96.9%. Additionally, the newly permission-based android dataset constructed in this research will be made available to researchers and developers for future work.
2021-09-30
Mahmoud, Loreen, Praveen, Raja.  2020.  Network Security Evaluation Using Deep Neural Network. 2020 15th International Conference for Internet Technology and Secured Transactions (ICITST). :1–4.
One of the most significant systems in computer network security assurance is the assessment of computer network security. With the goal of finding an effective method for performing the process of security evaluation in a computer network, this paper uses a deep neural network to be responsible for the task of security evaluating. The DNN will be built with python on Spyder IDE, it will be trained and tested by 17 network security indicators then the output that we get represents one of the security levels that have been already defined. The maj or purpose is to enhance the ability to determine the security level of a computer network accurately based on its selected security indicators. The method that we intend to use in this paper in order to evaluate network security is simple, reduces the human factors interferences, and can obtain the correct results of the evaluation rapidly. We will analyze the results to decide if this method will enhance the process of evaluating the security of the network in terms of accuracy.
KOSE, Busra OZDENIZCI, BUK, Onur, MANTAR, Haci Ali, COSKUN, Vedat.  2020.  TrustedID: An Identity Management System Based on OpenID Connect Protocol. 2020 4th International Symposium on Multidisciplinary Studies and Innovative Technologies (ISMSIT). :1–6.
Today, authentication and non-repudiation of actions are essential requirements for almost all mobile services. In this respect, various common identity systems (such as Facebook Login, Google Sign-In, Apple ID and many other) based on OpenID Connect protocol have been introduced that support easier password management for users, and reduce potential risks by securing the service provider and the user. With the widespread use of the Internet, smartphones can offer many services with rich content. The use of common identity systems on mobile devices with a high security level is becoming a more important requirement. At this point, MNOs (Mobile Network Operators) have a significant potential and capability for providing common identity services. The existing solutions based on Mobile Connect standard provide generally low level of assurance. Accordingly, there is an urgent need for a common identity system that provide higher level of assurance and security for service providers. This study presents a multi-factor authentication mechanism called TrustedID system that is based on Mobile Connect and OpenID Connect standards, and ensures higher level of assurance. The proposed system aims to use three identity factors of the user in order to access sensitive mobile services on the smartphone. The proposed authentication system will support improvement of new value-added services and also support the development of mobile ecosystem.
2021-09-21
Li, Mingxuan, Lv, Shichao, Shi, Zhiqiang.  2020.  Malware Detection for Industrial Internet Based on GAN. 2020 IEEE International Conference on Information Technology,Big Data and Artificial Intelligence (ICIBA). 1:475–481.
This thesis focuses on the detection of malware in industrial Internet. The basic flow of the detection of malware contains feature extraction and sample identification. API graph can effectively represent the behavior information of malware. However, due to the high algorithm complexity of solving the problem of subgraph isomorphism, the efficiency of analysis based on graph structure feature is low. Due to the different scales of API graph of different malicious codes, the API graph needs to be normalized. Considering the difficulties of sample collection and manual marking, it is necessary to expand the number of malware samples in industrial Internet. This paper proposes a method that combines PageRank with TF-IDF to process the API graph. Besides, this paper proposes a method to construct the adversarial samples of malwares based on GAN.
2021-09-07
Zebari, Rizgar R., Zeebaree, Subhi R. M., Sallow, Amira Bibo, Shukur, Hanan M., Ahmad, Omar M., Jacksi, Karwan.  2020.  Distributed Denial of Service Attack Mitigation Using High Availability Proxy and Network Load Balancing. 2020 International Conference on Advanced Science and Engineering (ICOASE). :174–179.
Nowadays, cybersecurity threat is a big challenge to all organizations that present their services over the Internet. Distributed Denial of Service (DDoS) attack is the most effective and used attack and seriously affects the quality of service of each E-organization. Hence, mitigation this type of attack is considered a persistent need. In this paper, we used Network Load Balancing (NLB) and High Availability Proxy (HAProxy) as mitigation techniques. The NLB is used in the Windows platform and HAProxy in the Linux platform. Moreover, Internet Information Service (IIS) 10.0 is implemented on Windows server 2016 and Apache 2 on Linux Ubuntu 16.04 as web servers. We evaluated each load balancer efficiency in mitigating synchronize (SYN) DDoS attack on each platform separately. The evaluation process is accomplished in a real network and average response time and average CPU are utilized as metrics. The results illustrated that the NLB in the Windows platform achieved better performance in mitigation SYN DDOS compared to HAProxy in the Linux platform. Whereas, the average response time of the Window webservers is reduced with NLB. However, the impact of the SYN DDoS on the average CPU usage of the IIS 10.0 webservers was more than those of the Apache 2 webservers.
Sudugala, A.U, Chanuka, W.H, Eshan, A.M.N, Bandara, U.C.S, Abeywardena, K.Y.  2020.  WANHEDA: A Machine Learning Based DDoS Detection System. 2020 2nd International Conference on Advancements in Computing (ICAC). 1:380–385.
In today's world computer communication is used almost everywhere and majority of them are connected to the world's largest network, the Internet. There is danger in using internet due to numerous cyber-attacks which are designed to attack Confidentiality, Integrity and Availability of systems connected to the internet. One of the most prominent threats to computer networking is Distributed Denial of Service (DDoS) Attack. They are designed to attack availability of the systems. Many users and ISPs are targeted and affected regularly by these attacks. Even though new protection technologies are continuously proposed, this immense threat continues to grow rapidly. Most of the DDoS attacks are undetectable because they act as legitimate traffic. This situation can be partially overcome by using Intrusion Detection Systems (IDSs). There are advanced attacks where there is no proper documented way to detect. In this paper authors present a Machine Learning (ML) based DDoS detection mechanism with improved accuracy and low false positive rates. The proposed approach gives inductions based on signatures previously extracted from samples of network traffic. Authors perform the experiments using four distinct benchmark datasets, four machine learning algorithms to address four of the most harmful DDoS attack vectors. Authors achieved maximum accuracy and compared the results with other applicable machine learning algorithms.
2021-08-17
Song, Guanglei, He, Lin, Wang, Zhiliang, Yang, Jiahai, Jin, Tao, Liu, Jieling, Li, Guo.  2020.  Towards the Construction of Global IPv6 Hitlist and Efficient Probing of IPv6 Address Space. 2020 IEEE/ACM 28th International Symposium on Quality of Service (IWQoS). :1–10.
Fast IPv4 scanning has made sufficient progress in network measurement and security research. However, it is infeasible to perform brute-force scanning of the IPv6 address space. We can find active IPv6 addresses through scanning candidate addresses generated by the state-of-the-art algorithms, whose probing efficiency of active IPv6 addresses, however, is still very low. In this paper, we aim to improve the probing efficiency of IPv6 addresses in two ways. Firstly, we perform a longitudinal active measurement study over four months, building a high-quality dataset called hitlist with more than 1.3 billion IPv6 addresses distributed in 45.2k BGP prefixes. Different from previous work, we probe the announced BGP prefixes using a pattern-based algorithm, which makes our dataset overcome the problems of uneven address distribution and low active rate. Secondly, we propose an efficient address generation algorithm DET, which builds a density space tree to learn high-density address regions of the seed addresses in linear time and improves the probing efficiency of active addresses. On the public hitlist and our hitlist, we compare our algorithm DET against state-of-the-art algorithms and find that DET increases the de-aliased active address ratio by 10%, and active address (including aliased addresses) ratio by 14%, by scanning 50 million addresses.
2021-08-11
Garcia-Luna-Aceves, J.J., Ali Albalawi, Abdulazaz.  2020.  Connection-Free Reliable and Efficient Transport Services in the IP Internet. 2020 16th International Conference on Network and Service Management (CNSM). :1—7.
The Internet Transport Protocol (ITP) is introduced to support reliable end-to-end transport services in the IP Internet without the need for end-to-end connections, changes to the Internet routing infrastructure, or modifications to name-resolution services. Results from simulation experiments show that ITP outperforms the Transmission Control Protocol (TCP) and the Named Data Networking (NDN) architecture, which requires replacing the Internet Protocol (IP). In addition, ITP allows transparent content caching while enforcing privacy.
Huang, Cheng-Wei, Wu, Tien-Yi, Tai, Yuan, Shao, Ching-Hsuan, Chen, Lo-An, Tsai, Meng-Hsun.  2020.  Machine learning-based IP Camera identification system. 2020 International Computer Symposium (ICS). :426—430.
With the development of technology, application of the Internet in daily life is increasing, making our connection with the Internet closer. However, with the improvement of convenience, information security has become more and more important. How to ensure information security in a convenient living environment is a question worth discussing. For instance, the widespread deployment of IP-cameras has made great progress in terms of convenience. On the contrary, it increases the risk of privacy exposure. Poorly designed surveillance devices may be implanted with suspicious software, which might be a thorny issue to human life. To effectively identify vulnerable devices, we design an SDN-based identification system that uses machine learning technology to identify brands and probable model types by identifying packet features. The identifying results make it possible for further vulnerability analysis.
Brooks, Richard, Wang, Kuang-Ching, Oakley, Jon, Tusing, Nathan.  2020.  Global Internet Traffic Routing and Privacy. 2020 International Scientific and Technical Conference Modern Computer Network Technologies (MoNeTeC). :1—7.
Current Internet Protocol routing provides minimal privacy, which enables multiple exploits. The main issue is that the source and destination addresses of all packets appear in plain text. This enables numerous attacks, including surveillance, man-in-the-middle (MITM), and denial of service (DoS). The talk explains how these attacks work in the current network. Endpoints often believe that use of Network Address Translation (NAT), and Dynamic Host Configuration Protocol (DHCP) can minimize the loss of privacy.We will explain how the regularity of human behavior can be used to overcome these countermeasures. Once packets leave the local autonomous system (AS), they are routed through the network by the Border Gateway Protocol (BGP). The talk will discuss the unreliability of BGP and current attacks on the routing protocol. This will include an introduction to BGP injects and the PEERING testbed for BGP experimentation. One experiment we have performed uses statistical methods (CUSUM and F-test) to detect BGP injection events. We describe work we performed that applies BGP injects to Internet Protocol (IP) address randomization to replace fixed IP addresses in headers with randomized addresses. We explain the similarities and differences of this approach with virtual private networks (VPNs). Analysis of this work shows that BGP reliance on autonomous system (AS) numbers removes privacy from the concept, even though it would disable the current generation of MITM and DoS attacks. We end by presenting a compromise approach that creates software-defined data exchanges (SDX), which mix traffic randomization with VPN concepts. We contrast this approach with the Tor overlay network and provide some performance data.
2021-08-02
Castilho, Sergio D., Godoy, Eduardo P., Salmen, Fadir.  2020.  Implementing Security and Trust in IoT/M2M using Middleware. 2020 International Conference on Information Networking (ICOIN). :726—731.
Machine to Machine (M2M) a sub area of Internet of Things (IoT) will link billions of devices or things distributed around the world using the Internet. These devices when connected exchange information obtained from the environment such as temperature or humidity from industrial or residential control process. Information Security (IS) and Trust are one of the fundamental points for users and the industry to accept the use of these devices with Confidentiality, Integrity, Availability and Authenticity. The key reason is that most of these devices use wireless media especially in residential and smart city environments. The overall goal of this work is to implement a Middleware Security to improve Safety and Security between the control network devices used in IoT/M2M and the Internet for residential or industrial environments. This implementation has been tested with different protocols as CoAP and MQTT, a microcomputer with free Real-Time Operating System (RTOS) implemented in a Raspberry Pi Gateway Access Point (RGAP), Network Address Translator (NAT), IPTable firewall and encryption is part of this implementation for secure data transmission
2021-07-08
Cesconetto, Jonas, Silva, Luís A., Valderi Leithardt, R. Q., Cáceres, María N., Silva, Luís A., Garcia, Nuno M..  2020.  PRIPRO:Solution for user profile control and management based on data privacy. 2020 15th Iberian Conference on Information Systems and Technologies (CISTI). :1—6.
Intelligent environments work collaboratively, bringing more comfort to human beings. The intelligence of these environments comes from technological advances in sensors and communication. IoT is the model developed that allows a wide and intelligent communication between devices. Hardware reduction of IoT devices results in vulnerabilities. Thus, there are numerous concerns regarding the security of user information, since mobile devices are easily trackable over the Internet. Care must be taken regarding the information in user profiles. Mobile devices are protected by a permission-based mechanism, which limits third-party applications from accessing sensitive device resources. In this context, this work aims to present a proposal for materialization of application for the evolution of user profiles in intelligent environments. Having as parameters the parameters presented in the proposed taxonomy. The proposed solution is the development of two applications, one for Android devices, responsible for allowing or blocking some features of the device. And another in Cloud, responsible for imposing the parameters and privacy criteria, formalizing the profile control module (PRIPRO - PRIvacy PROfiles).
2021-06-30
Lim, Wei Yang Bryan, Xiong, Zehui, Niyato, Dusit, Huang, Jianqiang, Hua, Xian-Sheng, Miao, Chunyan.  2020.  Incentive Mechanism Design for Federated Learning in the Internet of Vehicles. 2020 IEEE 92nd Vehicular Technology Conference (VTC2020-Fall). :1—5.
In the Internet of Vehicles (IoV) paradigm, a model owner is able to leverage on the enhanced capabilities of Intelligent Connected Vehicles (ICV) to develop promising Artificial Intelligence (AI) based applications, e.g., for traffic efficiency. However, in some cases, a model owner may have insufficient data samples to build an effective AI model. To this end, we propose a Federated Learning (FL) based privacy preserving approach to facilitate collaborative FL among multiple model owners in the IoV. Our system model enables collaborative model training without compromising data privacy given that only the model parameters instead of the raw data are exchanged within the federation. However, there are two main challenges of incentive mismatches between workers and model owners, as well as among model owners. For the former, we leverage on the self-revealing mechanism in contract theory under information asymmetry. For the latter, we use the coalitional game theory approach that rewards model owners based on their marginal contributions. The numerical results validate the performance efficiency of our proposed hierarchical incentive mechanism design.
Chen, Jichang, Lu, Zhixiang, Zhu, Xueping.  2020.  A Lightweight Dual Authentication Protocol for the Internet of Vehicles. 2020 IEEE 3rd International Conference on Information Systems and Computer Aided Education (ICISCAE). :17—22.
With the development of 5G communication technology, the status of the Internet of Vehicles in people's lives is greatly improved in the general trend of intelligent transportation. The combination of vehicles and Radio Frequency Identification (RFID) makes the application prospects of vehicle networking gradually expand. However, the wireless network of the Internet of Vehicles is open and mobile, so it can be easily stolen or tampered with by attackers. Moreover, it will cause serious traffic security problems and even threat people's lives. In this paper, we propose a lightweight authentication protocol for the Internet of Vehicles based on a mobile RFID system and give corresponding security requirements for modeling potential attacks. The protocol is based on the three-party mutual authentication, and uses bit-operated left-cycle shift operations and hetero-oriented operations to generate encrypted data. The simultaneous inclusion of triparty shared key information and random numbers makes the protocol resistant to counterfeit attacks, violent attacks, replay attacks and desynchronization attacks. Finally, a simulation analysis of the security protocol using the ProVerif tool shows that the protocol secures is not accessible to attackers during the data transfer, and achieve the three-party authentication between sensor nodes (SN), vehicle nodes (Veh) and backend servers.