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2020-07-03
Yang, Bowen, Liu, Dong.  2019.  Research on Network Traffic Identification based on Machine Learning and Deep Packet Inspection. 2019 IEEE 3rd Information Technology, Networking, Electronic and Automation Control Conference (ITNEC). :1887—1891.
Accurate network traffic identification is an important basis for network traffic monitoring and data analysis, and is the key to improve the quality of user service. In this paper, through the analysis of two network traffic identification methods based on machine learning and deep packet inspection, a network traffic identification method based on machine learning and deep packet inspection is proposed. This method uses deep packet inspection technology to identify most network traffic, reduces the workload that needs to be identified by machine learning method, and deep packet inspection can identify specific application traffic, and improves the accuracy of identification. Machine learning method is used to assist in identifying network traffic with encryption and unknown features, which makes up for the disadvantage of deep packet inspection that can not identify new applications and encrypted traffic. Experiments show that this method can improve the identification rate of network traffic.
2020-06-29
Giri, Nupur, Jaisinghani, Rahul, Kriplani, Rohit, Ramrakhyani, Tarun, Bhatia, Vinay.  2019.  Distributed Denial Of Service(DDoS) Mitigation in Software Defined Network using Blockchain. 2019 Third International conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC). :673–678.
A DDoS attack is a spiteful attempt to disrupt legitimate traffic to a server by overwhelming the target with a flood of requests from geographically dispersed systems. Today attackers prefer DDoS attack methods to disrupt target services as they generate GBs to TBs of random data to flood the target. In existing mitigation strategies, because of lack of resources and not having the flexibility to cope with attacks by themselves, they are not considered to be that effective. So effective DDoS mitigation techniques can be provided using emerging technologies such as blockchain and SDN(Software-Defined Networking). We propose an architecture where a smart contract is deployed in a private blockchain, which facilitates a collaborative DDoS mitigation architecture across multiple network domains. Blockchain application is used as an additional security service. With Blockchain, shared protection is enabled among all hosts. With help of smart contracts, rules are distributed among all hosts. In addition, SDN can effectively enable services and security policies dynamically. This mechanism provides ASes(Autonomous Systems) the possibility to deploy their own DPS(DDoS Prevention Service) and there is no need to transfer control of the network to the third party. This paper focuses on the challenges of protecting a hybridized enterprise from the ravages of rapidly evolving Distributed Denial of Service(DDoS) attack.
2020-06-19
Eziama, Elvin, Ahmed, Saneeha, Ahmed, Sabbir, Awin, Faroq, Tepe, Kemal.  2019.  Detection of Adversary Nodes in Machine-To-Machine Communication Using Machine Learning Based Trust Model. 2019 IEEE International Symposium on Signal Processing and Information Technology (ISSPIT). :1—6.

Security challenges present in Machine-to-Machine Communication (M2M-C) and big data paradigm are fundamentally different from conventional network security challenges. In M2M-C paradigms, “Trust” is a vital constituent of security solutions that address security threats and for such solutions,it is important to quantify and evaluate the amount of trust in the information and its source. In this work, we focus on Machine Learning (ML) Based Trust (MLBT) evaluation model for detecting malicious activities in a vehicular Based M2M-C (VBM2M-C) network. In particular, we present an Entropy Based Feature Engineering (EBFE) coupled Extreme Gradient Boosting (XGBoost) model which is optimized with Binary Particle Swarm optimization technique. Based on three performance metrics, i.e., Accuracy Rate (AR), True Positive Rate (TPR), False Positive Rate (FPR), the effectiveness of the proposed method is evaluated in comparison to the state-of-the-art ensemble models, such as XGBoost and Random Forest. The simulation results demonstrates the superiority of the proposed model with approximately 10% improvement in accuracy, TPR and FPR, with reference to the attacker density of 30% compared with the start-of-the-art algorithms.

2020-06-01
Kapoor, Chavi.  2019.  Routing Table Management using Dynamic Information with Routing Around Connectivity Holes (RACH) for IoT Networks. 2019 International Conference on Automation, Computational and Technology Management (ICACTM). :174—177.

The internet of things (IoT) is the popular wireless network for data collection applications. The IoT networks are deployed in dense or sparse architectures, out of which the dense networks are vastly popular as these are capable of gathering the huge volumes of data. The collected data is analyzed using the historical or continuous analytical systems, which uses the back testing or time-series analytics to observe the desired patterns from the target data. The lost or bad interval data always carries the high probability to misguide the analysis reports. The data is lost due to a variety of reasons, out of which the most popular ones are associated with the node failures and connectivity holes, which occurs due to physical damage, software malfunctioning, blackhole/wormhole attacks, route poisoning, etc. In this paper, the work is carried on the new routing scheme for the IoTs to avoid the connectivity holes, which analyzes the activity of wireless nodes and takes the appropriate actions when required.

2020-05-22
Almashaqbeh, Ghada, Kelley, Kevin, Bishop, Allison, Cappos, Justin.  2019.  CAPnet: A Defense Against Cache Accounting Attacks on Content Distribution Networks. 2019 IEEE Conference on Communications and Network Security (CNS). :250—258.

Peer-assisted content distribution networks (CDNs)have emerged to improve performance and reduce deployment costs of traditional, infrastructure-based content delivery networks. This is done by employing peer-to-peer data transfers to supplement the resources of the network infrastructure. However, these hybrid systems are vulnerable to accounting attacks in which the peers, or caches, collude with clients in order to report that content was transferred when it was not. This is a particular issue in systems that incentivize cache participation, because malicious caches may collect rewards from the content publishers operating the CDN without doing any useful work. In this paper, we introduce CAPnet, the first technique that lets untrusted caches join a peer-assisted CDN while providing a bound on the effectiveness of accounting attacks. At its heart is a lightweight cache accountability puzzle that clients must solve before caches are given credit. This puzzle requires colocating the data a client has requested, so its solution confirms that the content has actually been retrieved. We analyze the security and overhead of our scheme in realistic scenarios. The results show that a modest client machine using a single core can solve puzzles at a rate sufficient to simultaneously watch dozens of 1080p videos. The technique is designed to be even more scalable on the server side. In our experiments, one core of a single low-end machine is able to generate puzzles for 4.26 Tbps of bandwidth - enabling 870,000 clients to concurrently view the same 1080p video. This demonstrates that our scheme can ensure cache accountability without degrading system productivity.

2020-05-11
Memon, Raheel Ahmed, Li, Jianping, Ahmed, Junaid, Khan, Asif, Nazir, M. Irshad, Mangrio, M. Ismail.  2018.  Modeling of Blockchain Based Systems Using Queuing Theory Simulation. 2018 15th International Computer Conference on Wavelet Active Media Technology and Information Processing (ICCWAMTIP). :107–111.
Blockchain is the one of leading technology of this time; it has started to revolutionize several fields like, finance, business, industry, smart home, healthcare, social networks, Internet and the Internet of Things. It has many benefits like, decentralized network, robustness, availability, stability, anonymity, auditability and accountability. The applications of Blockchain are emerging, and it is found that most of the work is focused on its engineering implementation. While the theoretical part is very less considered and explored. In this paper we implemented the simulation of mining process in Blockchain based systems using queuing theory. We took the parameters of one of the mature Cryptocurrency, Bitcoin's real data and simulated using M/M/n/L queuing system in JSIMgraph. We have achieved realistic results; and expect that it will open up new research direction in theoretical research of Blockchain based systems.
Chae, Younghun, Katenka, Natallia, DiPippo, Lisa.  2019.  An Adaptive Threshold Method for Anomaly-based Intrusion Detection Systems. 2019 IEEE 18th International Symposium on Network Computing and Applications (NCA). :1–4.
Anomaly-based Detection Systems (ADSs) attempt to learn the features of behaviors and events of a system and/or users over a period to build a profile of normal behaviors. There has been a growing interest in ADSs and typically conceived as more powerful systems One of the important factors for ADSs is an ability to distinguish between normal and abnormal behaviors in a given period. However, it is getting complicated due to the dynamic network environment that changes every minute. It is dangerous to distinguish between normal and abnormal behaviors with a fixed threshold in a dynamic environment because it cannot guarantee the threshold is always an indication of normal behaviors. In this paper, we propose an adaptive threshold for a dynamic environment with a trust management scheme for efficiently managing the profiles of normal and abnormal behaviors. Based on the assumption of the statistical analysis-based ADS that normal data instances occur in high probability regions while malicious data instances occur in low probability regions of a stochastic model, we set two adaptive thresholds for normal and abnormal behaviors. The behaviors between the two thresholds are classified as suspicious behaviors, and they are efficiently evaluated with a trust management scheme.
2020-05-08
Chaudhary, Anshika, Mittal, Himangi, Arora, Anuja.  2019.  Anomaly Detection using Graph Neural Networks. 2019 International Conference on Machine Learning, Big Data, Cloud and Parallel Computing (COMITCon). :346—350.
Conventional methods for anomaly detection include techniques based on clustering, proximity or classification. With the rapidly growing social networks, outliers or anomalies find ingenious ways to obscure themselves in the network and making the conventional techniques inefficient. In this paper, we utilize the ability of Deep Learning over topological characteristics of a social network to detect anomalies in email network and twitter network. We present a model, Graph Neural Network, which is applied on social connection graphs to detect anomalies. The combinations of various social network statistical measures are taken into account to study the graph structure and functioning of the anomalous nodes by employing deep neural networks on it. The hidden layer of the neural network plays an important role in finding the impact of statistical measure combination in anomaly detection.
2020-04-20
Takbiri, Nazanin, Shao, Xiaozhe, Gao, Lixin, Pishro-Nik, Hossein.  2019.  Improving Privacy in Graphs Through Node Addition. 2019 57th Annual Allerton Conference on Communication, Control, and Computing (Allerton). :487–494.
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.
2020-04-13
Jeong, Yena, Hwang, DongYeop, Kim, Ki-Hyung.  2019.  Blockchain-Based Management of Video Surveillance Systems. 2019 International Conference on Information Networking (ICOIN). :465–468.
In this paper, we propose a video surveillance system based on blockchain system. The proposed system consists of a blockchain network with trusted internal managers. The metadata of the video is recorded on the distributed ledger of the blockchain, thereby blocking the possibility of forgery of the data. The proposed architecture encrypts and stores the video, creates a license within the blockchain, and exports the video. Since the decryption key for the video is managed by the private DB of the blockchain, it is not leaked by the internal manager unauthorizedly. In addition, the internal administrator can manage and export videos safely by exporting the license generated in the blockchain to the DRM-applied video player.
Grissa, Mohamed, Yavuz, Attila A., Hamdaoui, Bechir.  2019.  TrustSAS: A Trustworthy Spectrum Access System for the 3.5 GHz CBRS Band. IEEE INFOCOM 2019 - IEEE Conference on Computer Communications. :1495–1503.
As part of its ongoing efforts to meet the increased spectrum demand, the Federal Communications Commission (FCC) has recently opened up 150 MHz in the 3.5 GHz band for shared wireless broadband use. Access and operations in this band, aka Citizens Broadband Radio Service (CBRS), will be managed by a dynamic spectrum access system (SAS) to enable seamless spectrum sharing between secondary users (SUs) and incumbent users. Despite its benefits, SAS's design requirements, as set by FCC, present privacy risks to SUs, merely because SUs are required to share sensitive operational information (e.g., location, identity, spectrum usage) with SAS to be able to learn about spectrum availability in their vicinity. In this paper, we propose TrustSAS, a trustworthy framework for SAS that synergizes state-of-the-art cryptographic techniques with blockchain technology in an innovative way to address these privacy issues while complying with FCC's regulatory design requirements. We analyze the security of our framework and evaluate its performance through analysis, simulation and experimentation. We show that TrustSAS can offer high security guarantees with reasonable overhead, making it an ideal solution for addressing SUs' privacy issues in an operational SAS environment.
Rivera, Sean, Lagraa, Sofiane, Nita-Rotaru, Cristina, Becker, Sheila, State, Radu.  2019.  ROS-Defender: SDN-Based Security Policy Enforcement for Robotic Applications. 2019 IEEE Security and Privacy Workshops (SPW). :114–119.
In this paper we propose ROS-Defender, a holistic approach to secure robotics systems, which integrates a Security Event Management System (SIEM), an intrusion prevention system (IPS) and a firewall for a robotic system. ROS-Defender combines anomaly detection systems at application (ROS) level and network level, with dynamic policy enforcement points using software defined networking (SDN) to provide protection against a large class of attacks. Although SIEMs, IPS, and firewall have been previously used to secure computer networks, ROSDefender is applying them for the specific use case of robotic systems, where security is in many cases an afterthought.
2020-04-06
Sun, YunZhe, Zhao, QiXi, Zhang, PeiYun.  2019.  Trust Degree Calculation Method Based on Trust Blockchain Node. 2019 IEEE International Conference on Service Operations and Logistics, and Informatics (SOLI). :122–127.
Due to the diversity and mobility of blockchain network nodes and the decentralized nature of blockchain networks, traditional trust value evaluation indicators cannot be directly used. In order to obtain trusted nodes, a trustworthiness calculation method based on trust blockchain nodes is proposed. Different from the traditional P2P network trust value calculation, the trust blockchain not only acquires the working state of the node, but also collects the special behavior information of the node, and calculates the joining time by synthesizing the trust value generated by the node transaction and the trust value generated by the node behavior. After the attenuation factor is comprehensively evaluated, the trusted nodes are selected to effectively ensure the security of the blockchain network environment, while reducing the average transaction delay and increasing the block rate.
Wu, Yichang, Qiao, Yuansong, Ye, Yuhang, Lee, Brian.  2019.  Towards Improved Trust in Threat Intelligence Sharing using Blockchain and Trusted Computing. 2019 Sixth International Conference on Internet of Things: Systems, Management and Security (IOTSMS). :474–481.
Threat intelligence sharing is posited as an important aid to help counter cybersecurity attacks and a number of threat intelligence sharing communities exist. There is a general consensus that many challenges remain to be overcome to achieve fully effective sharing, including concerns about privacy, negative publicity, policy/legal issues and expense of sharing, amongst others. One recent trend undertaken to address this is the use of decentralized blockchain based sharing architectures. However while these platforms can help increase sharing effectiveness they do not fully address all of the above challenges. In particular, issues around trust are not satisfactorily solved by current approaches. In this paper, we describe a novel trust enhancement framework -TITAN- for decentralized sharing based on the use of P2P reputation systems to address open trust issues. Our design uses blockchain and Trusted Execution Environment technologies to ensure security, integrity and privacy in the operation of the threat intelligence sharing reputation system.
Gelil, Walid Abdel, Kunz, Thomas.  2019.  A Hierarchical P2P Overlay for Hierarchical Mobile Ad hoc Networks (MANETs). 2019 IEEE 10th Annual Ubiquitous Computing, Electronics Mobile Communication Conference (UEMCON). :0640–0646.
P2P applications deployment on MANETs is motivated by the popularity of these applications, coupled with the widespread use of mobile devices. P2P applications and MANETs have common features such as decentralization, self organization, and the absence of dedicated servers or infrastructure. The deployment often faces specific performance challenges resulting from topological overlay and underlay mismatch, limited bandwidth constraint and dynamic topology changes. Hierarchical MANETs are a special type of MANETs where some nodes have specific routing roles to allow inter- cluster communications. Such topologies (typical for tactical networks) render a successful P2P deployment more challenging. We developed a novel approach for P2P deployment in such networks by bringing topology-awareness into the overlay, mapping the underlay topology (structure) to the logical overlay and building a hierarchically-structured logical overlay on top of the hierarchical underlay. Simulation results demonstrated a significant performance advantage of our proposed deployment solution vs. a flat logical overlay using different configurations and mobility scenarios.
Huang, Wei-Chiao, Yeh, Lo-Yao, Huang, Jiun-Long.  2019.  A Monitorable Peer-to-Peer File Sharing Mechanism. 2019 20th Asia-Pacific Network Operations and Management Symposium (APNOMS). :1–4.
With the rise of blockchain technology, peer-to-peer network system has once again caught people's attention. Peer-to-peer (P2P) is currently being implemented on various kind of decentralized systems such as InterPlanetary File System (IPFS). However, P2P file sharing network systems is not without its flaws. Data stored in the other nodes cannot be deleted by the owner and can only be deleted by other nodes themselves. Ensuring that personal data can be completely removed is an important issue to comply with the European Union's General Data Protection Regulation (GDPR) criteria. To improve P2Ps privacy and security, we propose a monitorable peer-to-peer file sharing mechanism that synchronizes with other nodes to perform file deletion and to generate the File Authentication Code (FAC) of each IPFS nodes in order to make sure the system synchronized correctly. The proposed mechanism can integrate with a consortium Blockchain to comply with GDPR.
Frahat, Rzan Tarig, Monowar, Muhammed Mostafa, Buhari, Seyed M.  2019.  Secure and Scalable Trust Management Model for IoT P2P Network. 2019 2nd International Conference on Computer Applications Information Security (ICCAIS). :1–6.
IoT trust management is a security solution that assures the trust between different IoT entities before establishing any relationship with other anonymous devices. Recent researches presented in the literature tend to use a Blockchain-based trust management model for IoT besides the fog node approach in order to address the constraints of IoT resources. Actually, Blockchain has solved many drawbacks of centralized models. However, it is still not preferable for dealing with massive data produced by IoT because of its drawbacks such as delay, network overhead, and scalability issues. Therefore, in this paper we define some factors that should be considered when designing scalable models, and we propose a fully distributed trust management model for IoT that provide a large-scale trust model and address the limitations of Blockchain. We design our model based on a new approach called Holochain considering some security issues, such as detecting misbehaviors, data integrity and availability.
Khan, Riaz Ullah, Kumar, Rajesh, Alazab, Mamoun, Zhang, Xiaosong.  2019.  A Hybrid Technique To Detect Botnets, Based on P2P Traffic Similarity. 2019 Cybersecurity and Cyberforensics Conference (CCC). :136–142.
The botnet has been one of the most common threats to the network security since it exploits multiple malicious codes like worm, Trojans, Rootkit, etc. These botnets are used to perform the attacks, send phishing links, and/or provide malicious services. It is difficult to detect Peer-to-peer (P2P) botnets as compare to IRC (Internet Relay Chat), HTTP (HyperText Transfer Protocol) and other types of botnets because of having typical features of the centralization and distribution. To solve these problems, we propose an effective two-stage traffic classification method to detect P2P botnet traffic based on both non-P2P traffic filtering mechanism and machine learning techniques on conversation features. At the first stage, we filter non-P2P packages to reduce the amount of network traffic through well-known ports, DNS query, and flow counting. At the second stage, we extract conversation features based on data flow features and flow similarity. We detected P2P botnets successfully, by using Machine Learning Classifiers. Experimental evaluations show that our two-stage detection method has a higher accuracy than traditional P2P botnet detection methods.
Li, Jiabin, Xue, Zhi.  2019.  Distributed Threat Intelligence Sharing System: A New Sight of P2P Botnet Detection. 2019 2nd International Conference on Computer Applications Information Security (ICCAIS). :1–6.
Botnet has been evolving over time since its birth. Nowadays, P2P (Peer-to-Peer) botnet has become a main threat to cyberspace security, owing to its strong concealment and easy expansibility. In order to effectively detect P2P botnet, researchers often focus on the analysis of network traffic. For the sake of enriching P2P botnet detection methods, the author puts forward a new sight of applying distributed threat intelligence sharing system to P2P botnet detection. This system aims to fight against distributed botnet by using distributed methods itself, and then to detect botnet in real time. To fulfill the goal of botnet detection, there are 3 important parts: the threat intelligence sharing and evaluating system, the BAV quantitative TI model, and the AHP and HMM based analysis algorithm. Theoretically, this method should work on different types of distributed cyber threat besides P2P botnet.
Berenjian, Samaneh, Hajizadeh, Saeed, Atani, Reza Ebrahimi.  2019.  An Incentive Security Model to Provide Fairness for Peer-to-Peer Networks. 2019 IEEE Conference on Application, Information and Network Security (AINS). :71–76.
Peer-to-Peer networks are designed to rely on the resources of their own users. Therefore, resource management plays an important role in P2P protocols. Early P2P networks did not use proper mechanisms to manage fairness. However, after seeing difficulties and rise of freeloaders in networks like Gnutella, the importance of providing fairness for users have become apparent. In this paper, we propose an incentive-based security model which leads to a network infrastructure that lightens the work of Seeders and makes Leechers to contribute more. This method is able to prevent betrayals in Leecher-to-Leecher transactions and helps Seeders to be treated more fairly. This is what other incentive methods such as Bittorrent are incapable of doing. Additionally, by getting help from cryptography and combining it with our method, it is also possible to achieve secure channels, immune to spying, next to a fair network. This is the first protocol designed for P2P networks which has separated Leechers and Seeders without the need to a central server. The simulation results clearly show how our proposed approach can overcome free-riding issue. In addition, our findings revealed that our approach is able to provide an appropriate level of fairness for the users and can decrease the download time.
2020-03-30
Khan, Abdul Ghaffar, Zahid, Amjad Hussain, Hussain, Muzammil, Riaz, Usama.  2019.  Security Of Cryptocurrency Using Hardware Wallet And QR Code. 2019 International Conference on Innovative Computing (ICIC). :1–10.
Today, the privacy and the security of any organization are the key requirement, the digital online transaction of money or coins also needed a certain level of security not only during the broadcasting of the transaction but before the sending of the transaction. In this research paper we proposed and implemented a cryptocurrency (Bitcoin) wallet for the android operating system, by using the QR code-based android application and a secure private key storage (Cold Wallet). Two android applications have been implemented one of them is called cold wallet and the other one is hot wallet. Cold wallet (offline) is to store and generate the private key addresses for secure transaction confirmation and the hot wallet is used to send bitcoin to the network. Hot wallet application gives facility to the user view history of performed transactions, to send and compose a new bitcoin transaction, receive bitcoin, sign it and send it to the network. By using the process of cross QR code scanning of the hot and cold wallet to the identification, validation and authentication of the user made it secure.
2020-03-18
Lin, Yongze, Zhang, Xinyuan, Xia, Liting, Ren, Yue, Li, Weimin.  2019.  A Hybrid Algorithm for Influence Maximization of Social Networks. 2019 IEEE Intl Conf on Dependable, Autonomic and Secure Computing, Intl Conf on Pervasive Intelligence and Computing, Intl Conf on Cloud and Big Data Computing, Intl Conf on Cyber Science and Technology Congress (DASC/PiCom/CBDCom/CyberSciTech). :427–431.
Influence Maximization is an important research content in the dissemination process of information and behavior in social networks. Because Hill Climbing and Greedy Algorithm have good dissemination effect on this topic, researchers have used it to solve this NP problem for a long time. These algorithms only consider the number of active nodes in each round, ignoring the characteristic that the influence will be accumulated, so its effect is still far from the optimal solution. Also, the time complexity of these algorithms is considerable. Aiming at the problem of Influence Maximization, this paper improves the traditional Hill Climbing and Greedy Algorithm. We propose a Hybrid Distribution Value Accumulation Algorithm for Influence Maximization, which has better activation effect than Hill Climbing and Greedy Algorithm. In the first stage of the algorithm, the region is numerically accumulating rapidly and is easy to activate through value-greed. Experiments are conducted on two data sets: the voting situation on Wikipedia and the transmission situation of Gnutella node-to-node file sharing network. Experimental results verify the efficiency of our methods.
2020-03-16
White, Ruffin, Caiazza, Gianluca, Jiang, Chenxu, Ou, Xinyue, Yang, Zhiyue, Cortesi, Agostino, Christensen, Henrik.  2019.  Network Reconnaissance and Vulnerability Excavation of Secure DDS Systems. 2019 IEEE European Symposium on Security and Privacy Workshops (EuroS PW). :57–66.

Data Distribution Service (DDS) is a realtime peer-to-peer protocol that serves as a scalable middleware between distributed networked systems found in many Industrial IoT domains such as automotive, medical, energy, and defense. Since the initial ratification of the standard, specifications have introduced a Security Model and Service Plugin Interface (SPI) architecture, facilitating authenticated encryption and data centric access control while preserving interoperable data exchange. However, as Secure DDS v1.1, the default plugin specifications presently exchanges digitally signed capability lists of both participants in the clear during the crypto handshake for permission attestation; thus breaching confidentiality of the context of the connection. In this work, we present an attacker model that makes use of network reconnaissance afforded by this leaked context in conjunction with formal verification and model checking to arbitrarily reason about the underlying topology and reachability of information flow, enabling targeted attacks such as selective denial of service, adversarial partitioning of the data bus, or vulnerability excavation of vendor implementations.

Hasavari, Shirin, Song, Yeong Tae.  2019.  A Secure and Scalable Data Source for Emergency Medical Care using Blockchain Technology. 2019 IEEE 17th International Conference on Software Engineering Research, Management and Applications (SERA). :71–75.
Emergency medical services universally get regarded as the essential part of the health care delivery system [1]. A relationship exists between the emergency patient death rate and factors such as the failure to access a patient's critical data and the time it takes to arrive at hospitals. Nearly thirty million Americans do not live within an hour of trauma care, so this poor access to trauma centers links to higher pre-hospital death rates in more than half of the United States [2]. So, we need to address the problem. In a patient care-cycle, loads of medical data items are born in different healthcare settings using a disparate system of records during patient visits. The ability for medical care providers to access a patient's complete picture of emergency-relevant medical data is critical and can significantly reduce the annual mortality rate. Today, the problem exists with a continuous recording system of the patient data between healthcare providers. In this paper, we've introduced a combination of secure file transfer methods/tools and blockchain technology as a solution to record patient Emergency relevant medical data as patient walk through from one clinic/medical facility to another, creating a continuous footprint of patient as a secure and scalable data source. So, ambulance crews can access and use it to provide high quality pre-hospital care. All concerns of medical record sharing and accessing like authentication, privacy, security, scalability and audibility, confidentiality has been considered in this approach.
Kholidy, Hisham A..  2019.  Towards A Scalable Symmetric Key Cryptographic Scheme: Performance Evaluation and Security Analysis. 2019 2nd International Conference on Computer Applications Information Security (ICCAIS). :1–6.
In most applications, security attributes are pretty difficult to meet but it becomes even a bigger challenge when talking about Grid Computing. To secure data passes in Grid Systems, we need a professional scheme that does not affect the overall performance of the grid system. Therefore, we previously developed a new security scheme “ULTRA GRIDSEC” that is used to accelerate the performance of the symmetric key encryption algorithms for both stream and block cipher encryption algorithms. The scheme is used to accelerate the security of data pass between elements of our newly developed pure peer-to-peer desktop grid framework, “HIMAN”. It also enhances the security of the encrypted data resulted from the scheme and prevents the problem of weak keys of the encryption algorithms. This paper covers the analysis and evaluation of this scheme showing the different factors affecting the scheme performance, and covers the efficiency of the scheme from the security prospective. The experimental results are highlighted for two types of encryption algorithms, TDES as an example for the block cipher algorithms, and RC4 as an example for the stream cipher algorithms. The scheme speeds up the former algorithm by 202.12% and the latter one by 439.7%. These accelerations are also based on the running machine's capabilities.