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Li, Guanyu, Zhang, Menghao, Liu, Chang, Kong, Xiao, Chen, Ang, Gu, Guofei, Duan, Haixin.  2019.  NETHCF: Enabling Line-rate and Adaptive Spoofed IP Traffic Filtering. 2019 IEEE 27th International Conference on Network Protocols (ICNP). :1–12.
In this paper, we design NETHCF, a line-rate in-network system for filtering spoofed traffic. NETHCF leverages the opportunity provided by programmable switches to design a novel defense against spoofed IP traffic, and it is highly efficient and adaptive. One key challenge stems from the restrictions of the computational model and memory resources of programmable switches. We address this by decomposing the HCF system into two complementary components-one component for the data plane and another for the control plane. We also aggregate the IP-to-Hop-Count (IP2HC) mapping table for efficient memory usage, and design adaptive mechanisms to handle end-to-end routing changes, IP popularity changes, and network activity dynamics. We have built a prototype on a hardware Tofino switch, and our evaluation demonstrates that NETHCF can achieve line-rate and adaptive traffic filtering with low overheads.
Tun, May Thet, Nyaung, Dim En, Phyu, Myat Pwint.  2019.  Performance Evaluation of Intrusion Detection Streaming Transactions Using Apache Kafka and Spark Streaming. 2019 International Conference on Advanced Information Technologies (ICAIT). :25–30.
In the information era, the size of network traffic is complex because of massive Internet-based services and rapid amounts of data. The more network traffic has enhanced, the more cyberattacks have dramatically increased. Therefore, cybersecurity intrusion detection has been a challenge in the current research area in recent years. The Intrusion detection system requires high-level protection and detects modern and complex attacks with more accuracy. Nowadays, big data analytics is the main key to solve marketing, security and privacy in an extremely competitive financial market and government. If a huge amount of stream data flows within a short period time, it is difficult to analyze real-time decision making. Performance analysis is extremely important for administrators and developers to avoid bottlenecks. The paper aims to reduce time-consuming by using Apache Kafka and Spark Streaming. Experiments on the UNSWNB-15 dataset indicate that the integration of Apache Kafka and Spark Streaming can perform better in terms of processing time and fault-tolerance on the huge amount of data. According to the results, the fault tolerance can be provided by the multiple brokers of Kafka and parallel recovery of Spark Streaming. And then, the multiple partitions of Apache Kafka increase the processing time in the integration of Apache Kafka and Spark Streaming.
Wang, Jinmiao, Lang, Bo.  2016.  An efficient KP-ABE scheme for content protection in Information-Centric Networking. 2016 IEEE Symposium on Computers and Communication (ISCC). :830—837.
Media streaming has largely dominated the Internet traffic and the trend will keep increasing in the next years. To efficiently distribute the media content, Information-Centric Networking (ICN) has attracted many researchers. Since end users usually obtain content from indeterminate caches in ICN, the publisher cannot reinforce data security and access control depending on the caches. Hence, the ability of self-contained protection is important for the cached contents. Attribute-based encryption (ABE) is considered the preferred solution to achieve this goal. However, the existing ABE schemes usually have problems regarding efficiency. The exponentiation in key generation and pairing operation in decryption respectively increases linearly with the number of attributes involved, which make it costly. In this paper, we propose an efficient key-policy ABE with fast key generation and decryption (FKP-ABE). In the key generation, we get rid of exponentiation and only require multiplications/divisions for each attribute in the access policy. And in the decryption, we reduce the pairing operations to a constant number, no matter how many attributes are used. The efficiency analysis indicates that our scheme has better performance than the existing KP-ABE schemes. Finally, we present an implementation framework that incorporates the proposed FKP-ABE with the ICN architecture.
Ayub, Md. Ahsan, Smith, Steven, Siraj, Ambareen.  2019.  A Protocol Independent Approach in Network Covert Channel Detection. 2019 IEEE International Conference on Computational Science and Engineering (CSE) and IEEE International Conference on Embedded and Ubiquitous Computing (EUC). :165—170.

Network covert channels are used in various cyberattacks, including disclosure of sensitive information and enabling stealth tunnels for botnet commands. With time and technology, covert channels are becoming more prevalent, complex, and difficult to detect. The current methods for detection are protocol and pattern specific. This requires the investment of significant time and resources into application of various techniques to catch the different types of covert channels. This paper reviews several patterns of network storage covert channels, describes generation of network traffic dataset with covert channels, and proposes a generic, protocol-independent approach for the detection of network storage covert channels using a supervised machine learning technique. The implementation of the proposed generic detection model can lead to a reduction of necessary techniques to prevent covert channel communication in network traffic. The datasets we have generated for experimentation represent storage covert channels in the IP, TCP, and DNS protocols and are available upon request for future research in this area.

Muñoz, Jordi Zayuelas i, Suárez-Varela, José, Barlet-Ros, Pere.  2019.  Detecting cryptocurrency miners with NetFlow/IPFIX network measurements. 2019 IEEE International Symposium on Measurements Networking (M N). :1—6.

In the last few years, cryptocurrency mining has become more and more important on the Internet activity and nowadays is even having a noticeable impact on the global economy. This has motivated the emergence of a new malicious activity called cryptojacking, which consists of compromising other machines connected to the Internet and leverage their resources to mine cryptocurrencies. In this context, it is of particular interest for network administrators to detect possible cryptocurrency miners using network resources without permission. Currently, it is possible to detect them using IP address lists from known mining pools, processing information from DNS traffic, or directly performing Deep Packet Inspection (DPI) over all the traffic. However, all these methods are still ineffective to detect miners using unknown mining servers or result too expensive to be deployed in real-world networks with large traffic volume. In this paper, we present a machine learning-based method able to detect cryptocurrency miners using NetFlow/IPFIX network measurements. Our method does not require to inspect the packets' payload; as a result, it achieves cost-efficient miner detection with similar accuracy than DPI-based techniques.

Attarian, Reyhane, Hashemi, Sattar.  2019.  Investigating the Streaming Algorithms Usage in Website Fingerprinting Attack Against Tor Privacy Enhancing Technology. 2019 16th International ISC (Iranian Society of Cryptology) Conference on Information Security and Cryptology (ISCISC). :33–38.
Website fingerprinting attack is a kind of traffic analysis attack that aims to identify the URL of visited websites using the Tor browser. Previous website fingerprinting attacks were based on batch learning methods which assumed that the traffic traces of each website are independent and generated from the stationary probability distribution. But, in realistic scenarios, the websites' concepts can change over time (dynamic websites) that is known as concept drift. To deal with data whose distribution change over time, the classifier model must update its model permanently and be adaptive to concept drift. Streaming algorithms are dynamic models that have these features and lead us to make a comparison of various representative data stream classification algorithms for website fingerprinting. Given to our experiments and results, by considering streaming algorithms along with statistical flow-based network traffic features, the accuracy grows significantly.
Jia, Guanbo, Miller, Paul, Hong, Xin, Kalutarage, Harsha, Ban, Tao.  2019.  Anomaly Detection in Network Traffic Using Dynamic Graph Mining with a Sparse Autoencoder. 2019 18th IEEE International Conference On Trust, Security And Privacy In Computing And Communications/13th IEEE International Conference On Big Data Science And Engineering (TrustCom/BigDataSE). :458—465.

Network based attacks on ecommerce websites can have serious economic consequences. Hence, anomaly detection in dynamic network traffic has become an increasingly important research topic in recent years. This paper proposes a novel dynamic Graph and sparse Autoencoder based Anomaly Detection algorithm named GAAD. In GAAD, the network traffic over contiguous time intervals is first modelled as a series of dynamic bipartite graph increments. One mode projection is performed on each bipartite graph increment and the adjacency matrix derived. Columns of the resultant adjacency matrix are then used to train a sparse autoencoder to reconstruct it. The sum of squared errors between the reconstructed approximation and original adjacency matrix is then calculated. An online learning algorithm is then used to estimate a Gaussian distribution that models the error distribution. Outlier error values are deemed to represent anomalous traffic flows corresponding to possible attacks. In the experiment, a network emulator was used to generate representative ecommerce traffic flows over a time period of 225 minutes with five attacks injected, including SYN scans, host emulation and DDoS attacks. ROC curves were generated to investigate the influence of the autoencoder hyper-parameters. It was found that increasing the number of hidden nodes and their activation level, and increasing sparseness resulted in improved performance. Analysis showed that the sparse autoencoder was unable to encode the highly structured adjacency matrix structures associated with attacks, hence they were detected as anomalies. In contrast, SVD and variants, such as the compact matrix decomposition, were found to accurately encode the attack matrices, hence they went undetected.

El-Din Abd El-Raouf, Karim Alaa, Bahaa-Eldin, Ayman M., Sobh, Mohamed A..  2019.  Multipath Traffic Engineering for Software Defined Networking. 2019 14th International Conference on Computer Engineering and Systems (ICCES). :132—136.

ASA systems (firewall, IDS, IPS) are probable to become communication bottlenecks in networks with growing network bandwidths. To alleviate this issue, we suggest to use Application-aware mechanism based on Deep Packet Inspection (DPI) to bypass chosen traffic around firewalls. The services of Internet video sharing gained importance and expanded their share of the multimedia market. The Internet video should meet strict service quality (QoS) criteria to make the broadcasting of broadcast television a viable and comparable level of quality. However, since the Internet video relies on packet communication, it is subject to delays, transmission failures, loss of data and bandwidth restrictions that may have a catastrophic effect on the quality of multimedia.

Shaout, Adnan, Crispin, Brennan.  2019.  Markov Augmented Neural Networks for Streaming Video Classification. 2019 International Arab Conference on Information Technology (ACIT). :1—7.

With the growing number of streaming services, internet providers are increasingly needing to be able to identify the types of data and content providers that are being used on their networks. Traditional methods, such as IP and port scanning, are not always available for clients using VPNs or with providers using varying IP addresses. As such, in this paper we explore a potential method using neural networks and Markov Decision Process in order to augment deep packet inspection techniques in identifying the source and class of video streaming services.

Yamauchi, Hiroaki, Nakao, Akihiro, Oguchi, Masato, Yamamoto, Shu, Yamaguchi, Saneyasu.  2019.  A Study on Service Identification Based on Server Name Indication Analysis. 2019 Seventh International Symposium on Computing and Networking Workshops (CANDARW). :470—474.

Identifying services constituting traffic from given IP network flows is essential to various applications, such as the management of quality of service (QoS) and the prevention of security issues. Typical methods for achieving this objective include identifications based on IP addresses and port numbers. However, such methods are not sufficiently accurate and require improvement. Deep Packet Inspection (DPI) is one of the most promising methods for improving the accuracy of identification. In addition, many current IP flows are encrypted using Transport Layer Security (TLS). Hence, it is necessary for identification methods to analyze flows encrypted by TLS. For that reason, a service identification method based on DPI and n-gram that focuses only on the non-encrypted parts in the TLS session establishment was proposed. However, there is room for improvement in identification accuracy because this method analyzes all the non-encrypted parts including Random Values without protocol analyses. In this paper, we propose a method for identifying the service from given IP flows based on analysis of Server Name Indication (SNI). The proposed method clusters flow according to the value of SNI and identify services from the occurrences of all clusters. Our evaluations, which involve identifications of services on Google and Yahoo sites, demonstrate that the proposed method can identify services more accurately than the existing method.

Ceška, Milan, Havlena, Vojtech, Holík, Lukáš, Korenek, Jan, Lengál, Ondrej, Matoušek, Denis, Matoušek, Jirí, Semric, Jakub, Vojnar, Tomáš.  2019.  Deep Packet Inspection in FPGAs via Approximate Nondeterministic Automata. 2019 IEEE 27th Annual International Symposium on Field-Programmable Custom Computing Machines (FCCM). :109—117.

Deep packet inspection via regular expression (RE) matching is a crucial task of network intrusion detection systems (IDSes), which secure Internet connection against attacks and suspicious network traffic. Monitoring high-speed computer networks (100 Gbps and faster) in a single-box solution demands that the RE matching, traditionally based on finite automata (FAs), is accelerated in hardware. In this paper, we describe a novel FPGA architecture for RE matching that is able to process network traffic beyond 100 Gbps. The key idea is to reduce the required FPGA resources by leveraging approximate nondeterministic FAs (NFAs). The NFAs are compiled into a multi-stage architecture starting with the least precise stage with a high throughput and ending with the most precise stage with a low throughput. To obtain the reduced NFAs, we propose new approximate reduction techniques that take into account the profile of the network traffic. Our experiments showed that using our approach, we were able to perform matching of large sets of REs from SNORT, a popular IDS, on unprecedented network speeds.

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.

Ahuja, Nisha, Singal, Gaurav.  2019.  DDOS Attack Detection Prevention in SDN using OpenFlow Statistics. 2019 IEEE 9th International Conference on Advanced Computing (IACC). :147–152.
Software defined Network is a network defined by software, which is one of the important feature which makes the legacy old networks to be flexible for dynamic configuration and so can cater to today's dynamic application requirement. It is a programmable network but it is prone to different type of attacks due to its centralized architecture. The author provided a solution to detect and prevent Distributed Denial of service attack in the paper. Mininet [5] which is a popular emulator for Software defined Network is used. We followed the approach in which collection of the traffic statistics from the various switches is done. After collection we calculated the packet rate and bandwidth which shoots up to high values when attack take place. The abrupt increase detects the attack which is then prevented by changing the forwarding logic of the host nodes to drop the packets instead of forwarding. After this, no more packets will be forwarded and then we also delete the forwarding rule in the flow table. Hence, we are finding out the change in packet rate and bandwidth to detect the attack and to prevent the attack we modify the forwarding logic of the switch flow table to drop the packets coming from malicious host instead of forwarding it.
Kaljic, Enio, Maric, Almir, Njemcevic, Pamela.  2019.  DoS attack mitigation in SDN networks using a deeply programmable packet-switching node based on a hybrid FPGA/CPU data plane architecture. 2019 XXVII International Conference on Information, Communication and Automation Technologies (ICAT). :1–6.
The application of the concept of software-defined networks (SDN) has, on the one hand, led to the simplification and reduction of switches price, and on the other hand, has created a significant number of problems related to the security of the SDN network. In several studies was noted that these problems are related to the lack of flexibility and programmability of the data plane, which is likely first to suffer potential denial-of-service (DoS) attacks. One possible way to overcome this problem is to increase the flexibility of the data plane by increasing the depth of programmability of the packet-switching nodes below the level of flow table management. Therefore, this paper investigates the opportunity of using the architecture of deeply programmable packet-switching nodes (DPPSN) in the implementation of a firewall. Then, an architectural model of the firewall based on a hybrid FPGA/CPU data plane architecture has been proposed and implemented. Realized firewall supports three models of DoS attacks mitigation: DoS traffic filtering on the output interface, DoS traffic filtering on the input interface, and DoS attack redirection to the honeypot. Experimental evaluation of the implemented firewall has shown that DoS traffic filtering at the input interface is the best strategy for DoS attack mitigation, which justified the application of the concept of deep network programmability.
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.
Daneshgadeh, Salva, Ahmed, Tarem, Kemmerich, Thomas, Baykal, Nazife.  2019.  Detection of DDoS Attacks and Flash Events Using Shannon Entropy, KOAD and Mahalanobis Distance. 2019 22nd Conference on Innovation in Clouds, Internet and Networks and Workshops (ICIN). :222–229.
The growing number of internet based services and applications along with increasing adoption rate of connected wired and wireless devices presents opportunities as well as technical challenges and threads. Distributed Denial of Service (DDoS) attacks have huge devastating effects on internet enabled services. It can be implemented diversely with a variety of tools and codes. Therefore, it is almost impossible to define a single solution to prevent DDoS attacks. The available solutions try to protect internet services from DDoS attacks, but there is no accepted best-practice yet to this security breach. On the other hand, distinguishing DDoS attacks from analogous Flash Events (FEs) wherein huge number of legitimate users try to access a specific internet based services and applications is a tough challenge. Both DDoS attacks and FEs result in unavailability of service, but they should be treated with different countermeasures. Therefore, it is worthwhile to investigate novel methods which can detect well disguising DDoS attacks from similar FE traffic. This paper will contribute to this topic by proposing a hybrid DDoS and FE detection scheme; taking 3 isolated approaches including Kernel Online Anomaly Detection (KOAD), Shannon Entropy and Mahalanobis Distance. In this study, Shannon entropy is utilized with an online machine learning technique to detect abnormal traffic including DDoS attacks and FE traffic. Subsequently, the Mahalanobis distance metric is employed to differentiate DDoS and FE traffic. the purposed method is validated using simulated DDoS attacks, real normal and FE traffic. The results revealed that the Mahalanobis distance metric works well in combination with machine learning approach to detect and discriminate DDoS and FE traffic in terms of false alarms and detection rate.
Nenova, Maria, Atanasov, Denis, Kassev, Kiril, Nenov, Andon.  2019.  Intrusion Detection System Model Implementation against DDOS attacks. 2019 IEEE International Conference on Microwaves, Antennas, Communications and Electronic Systems (COMCAS). :1–4.
In the paper is presented implementation of a system for detecting intrusion actions. An implementation of intrusion detection systems (IDS), their architectures, and intrusion detection methods are investigated. Analyzed are methods for SNORT (IDS) bandwidth traffic analysis in intrusion detection and prevention systems. The main requirements for Installation and configuration of the system are also discussed. Then the configuration of the firewall policy and specifics there, are also presented. It is also described the database structure, the operating modes, and analysis of the rules. Two of the most commonly implemented attacks and model for defense against them is proposed.
Sun, Wenwen, Li, Yi, Guan, Shaopeng.  2019.  An Improved Method of DDoS Attack Detection for Controller of SDN. 2019 IEEE 2nd International Conference on Computer and Communication Engineering Technology (CCET). :249–253.
For controllers of Software Defined Network (SDN), Distributed Denial of Service (DDoS) attacks are still the simplest and most effective way to attack. Aiming at this problem, a real-time DDoS detection attack method for SDN controller is proposed. The method first uses the entropy to detect whether the flow is abnormal. After the abnormal warning is issued, the flow entry of the OpenFlow switch is obtained, and the DDoS attack feature in the SDN environment is analyzed to extract important features related to the attack. The BiLSTM-RNN neural network algorithm is used to train the data set, and the BiLSTM model is generated to classify the real-time traffic to realize the DDoS attack detection. Experiments show that, compared with other methods, this method can efficiently implement DDoS attack traffic detection and reduce controller overhead in SDN environment.
Lai, Chengzhe, Du, Yangyang, Men, Jiawei, Zheng, Dong.  2019.  A Trust-based Real-time Map Updating Scheme. 2019 IEEE/CIC International Conference on Communications in China (ICCC). :334—339.

The real-time map updating enables vehicles to obtain accurate and timely traffic information. Especially for driverless cars, real-time map updating can provide high-precision map service to assist the navigation, which requires vehicles to actively upload the latest road conditions. However, due to the untrusted network environment, it is difficult for the real-time map updating server to evaluate the authenticity of the road information from the vehicles. In order to prevent malicious vehicles from deliberately spreading false information and protect the privacy of vehicles from tracking attacks, this paper proposes a trust-based real-time map updating scheme. In this scheme, the public key is used as the identifier of the vehicle for anonymous communication with conditional anonymity. In addition, the blockchain is applied to provide the existence proof for the public key certificate of the vehicle. At the same time, to avoid the spread of false messages, a trust evaluation algorithm is designed. The fog node can validate the received massages from vehicles using Bayesian Inference Model. Based on the verification results, the road condition information is sent to the real-time map updating server so that the server can update the map in time and prevent the secondary traffic accident. In order to calculate the trust value offset for the vehicle, the fog node generates a rating for each message source vehicle, and finally adds the relevant data to the blockchain. According to the result of security analysis, this scheme can guarantee the anonymity and prevent the Sybil attack. Simulation results show that the proposed scheme is effective and accurate in terms of real-time map updating and trust values calculating.

Haefner, Kyle, Ray, Indrakshi.  2019.  ComplexIoT: Behavior-Based Trust For IoT Networks. 2019 First IEEE International Conference on Trust, Privacy and Security in Intelligent Systems and Applications (TPS-ISA). :56—65.

This work takes a novel approach to classifying the behavior of devices by exploiting the single-purpose nature of IoT devices and analyzing the complexity and variance of their network traffic. We develop a formalized measurement of complexity for IoT devices, and use this measurement to precisely tune an anomaly detection algorithm for each device. We postulate that IoT devices with low complexity lead to a high confidence in their behavioral model and have a correspondingly more precise decision boundary on their predicted behavior. Conversely, complex general purpose devices have lower confidence and a more generalized decision boundary. We show that there is a positive correlation to our complexity measure and the number of outliers found by an anomaly detection algorithm. By tuning this decision boundary based on device complexity we are able to build a behavioral framework for each device that reduces false positive outliers. Finally, we propose an architecture that can use this tuned behavioral model to rank each flow on the network and calculate a trust score ranking of all traffic to and from a device which allows the network to autonomously make access control decisions on a per-flow basis.

Chowdhury, Abdullahi, Karmakar, Gour, Kamruzzaman, Joarder.  2019.  Trusted Autonomous Vehicle: Measuring Trust using On-Board Unit Data. 2019 18th IEEE International Conference On Trust, Security And Privacy In Computing And Communications/13th IEEE International Conference On Big Data Science And Engineering (TrustCom/BigDataSE). :787—792.

Vehicular Ad-hoc Networks (VANETs) play an essential role in ensuring safe, reliable and faster transportation with the help of an Intelligent Transportation system. The trustworthiness of vehicles in VANETs is extremely important to ensure the authenticity of messages and traffic information transmitted in extremely dynamic topographical conditions where vehicles move at high speed. False or misleading information may cause substantial traffic congestions, road accidents and may even cost lives. Many approaches exist in literature to measure the trustworthiness of GPS data and messages of an Autonomous Vehicle (AV). To the best of our knowledge, they have not considered the trustworthiness of other On-Board Unit (OBU) components of an AV, along with GPS data and transmitted messages, though they have a substantial relevance in overall vehicle trust measurement. In this paper, we introduce a novel model to measure the overall trustworthiness of an AV considering four different OBU components additionally. The performance of the proposed method is evaluated with a traffic simulation model developed by Simulation of Urban Mobility (SUMO) using realistic traffic data and considering different levels of uncertainty.

Garrido, Pablo, Sanchez, Isabel, Ferlin, Simone, Aguero, Ramon, Alay, Ozgu.  2019.  Poster: rQUIC - integrating FEC with QUIC for robust wireless communications. 2019 IFIP Networking Conference (IFIP Networking). :1—2.

Quick UDP Internet Connections (QUIC) is an experimental transport protocol designed to primarily reduce connection establishment and transport latency, as well as to improve security standards with default end-to-end encryption in HTTPbased applications. QUIC is a multiplexed and secure transport protocol fostered by Google and its design emerged from the urgent need of innovation in the transport layer, mainly due to difficulties extending TCP and deploying new protocols. While still under standardisation, a non-negligble fraction of the Internet's traffic, more than 7% of a European Tier1-ISP, is already running over QUIC and it constitutes more than 30% of Google's egress traffic [1].

Boubakri, Wided, Abdallah, Walid, Boudriga, Noureddine.  2019.  Game-Based Attack Defense Model to Provide Security for Relay Selection in 5G Mobile Networks. 2019 IEEE Intl Conf on Parallel Distributed Processing with Applications, Big Data Cloud Computing, Sustainable Computing Communications, Social Computing Networking (ISPA/BDCloud/SocialCom/SustainCom). :160–167.
5G mobile networks promise universal communication environment and aims at providing higher bandwidth, increased communication and networking capabilities, and extensive signal coverage by using multiple communication technologies including Device-to-Device (D-to-D). This paradigm, will allow scalable and ubiquitous connectivity for large-scale mobile networks where a huge number of heterogeneous devices with limited resources will cooperate to enhance communication efficiency in terms of link reliability, spectral efficiency, system capacity, and transmission range. However, owing to its decentralized nature, cooperative D-to-D communication could be vulnerable to attacks initiated on relay nodes. Consequently, a source node has the interest to select the more protected relay to ensure the security of its traffic. Nevertheless, an improvement in the protection level has a counterpart cost that must be sustained by the device. To address this trade-off as well as the interaction between the attacker and the source device, we propose a dynamic game theoretic based approach to model and analyze this problem as a cost model. The utility function of the proposed non-cooperative game is based on the concepts of return on protection and return on attack which illustrate the gain of selecting a relay for transmitting a data packet by a source node and the reward of the attacker to perform an attack to compromise the transmitted data. Moreover, we discuss and analyze Nash equilibrium convergence of this attack-defense model and we propose an heuristic algorithm that can determine the equilibrium state in a limited number of running stages. Finally, we perform simulation work to show the effectiveness of the game model in assessing the behavior of the source node and the attacker and its ability to reach equilibrium within a finite number of steps.
Talusan, Jose Paolo, Tiausas, Francis, Yasumoto, Keiichi, Wilbur, Michael, Pettet, Geoffrey, Dubey, Abhishek, Bhattacharjee, Shameek.  2019.  Smart Transportation Delay and Resiliency Testbed Based on Information Flow of Things Middleware. 2019 IEEE International Conference on Smart Computing (SMARTCOMP). :13–18.
Edge and Fog computing paradigms are used to process big data generated by the increasing number of IoT devices. These paradigms have enabled cities to become smarter in various aspects via real-time data-driven applications. While these have addressed some flaws of cloud computing some challenges remain particularly in terms of privacy and security. We create a testbed based on a distributed processing platform called the Information flow of Things (IFoT) middleware. We briefly describe a decentralized traffic speed query and routing service implemented on this framework testbed. We configure the testbed to test countermeasure systems that aim to address the security challenges faced by prior paradigms. Using this testbed, we investigate a novel decentralized anomaly detection approach for time-sensitive distributed smart transportation systems.
Wang, He, Wu, Bin.  2019.  SDN-based hybrid honeypot for attack capture. 2019 IEEE 3rd Information Technology, Networking, Electronic and Automation Control Conference (ITNEC). :1602–1606.
Honeypots have become an important tool for capturing attacks. Hybrid honeypots, including the front end and the back end, are widely used in research because of the scalability of the front end and the high interactivity of the back end. However, traditional hybrid honeypots have some problems that the flow control is difficult and topology simulation is not realistic. This paper proposes a new architecture based on SDN applied to the hybrid honeypot system for network topology simulation and attack traffic migration. Our system uses the good expansibility and controllability of the SDN controller to simulate a large and realistic network to attract attackers and redirect high-level attacks to a high-interaction honeypot for attack capture and further analysis. It improves the deficiencies in the network spoofing technology and flow control technology in the traditional honeynet. Finally, we set up the experimental environment on the mininet and verified the mechanism. The test results show that the system is more intelligent and the traffic migration is more stealthy.