Visible to the public Biblio

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2020-08-10
Almajed, Hisham N., Almogren, Ahmad S..  2019.  SE-Enc: A Secure and Efficient Encoding Scheme Using Elliptic Curve Cryptography. IEEE Access. 7:175865–175878.
Many applications use asymmetric cryptography to secure communications between two parties. One of the main issues with asymmetric cryptography is the need for vast amounts of computation and storage. While this may be true, elliptic curve cryptography (ECC) is an approach to asymmetric cryptography used widely in low computation devices due to its effectiveness in generating small keys with a strong encryption mechanism. The ECC decreases power consumption and increases device performance, thereby making it suitable for a wide range of devices, ranging from sensors to the Internet of things (IoT) devices. It is necessary for the ECC to have a strong implementation to ensure secure communications, especially when encoding a message to an elliptic curve. It is equally important for the ECC to secure the mapping of the message to the curve used in the encryption. This work objective is to propose a trusted and proofed scheme that offers authenticated encryption (AE) for both encoding and mapping a message to the curve. In addition, this paper provides analytical results related to the security requirements of the proposed scheme against several encryption techniques. Additionally, a comparison is undertaken between the SE-Enc and other state-of-the-art encryption schemes to evaluate the performance of each scheme.
2020-08-07
Liu, Xiaohu, Li, Laiqiang, Ma, Zhuang, Lin, Xin, Cao, Junyang.  2019.  Design of APT Attack Defense System Based on Dynamic Deception. 2019 IEEE 5th International Conference on Computer and Communications (ICCC). :1655—1659.
Advanced Persistent Threat (APT) attack has the characteristics of complex attack means, long duration and great harmfulness. Based on the idea of dynamic deception, the paper proposed an APT defense system framework, and analyzed the deception defense process. The paper proposed a hybrid encryption communication mechanism based on socket, a dynamic IP address generation method based on SM4, a dynamic timing selection method based on Viterbi algorithm and a dynamic policy allocation mechanism based on DHCPv6. Tests show that the defense system can dynamically change and effectively defense APT attacks.
2020-07-27
McBride, Marci, Mitchell, Robert.  2018.  Enhanced dynamic cyber zone defense. 2018 IEEE 8th Annual Computing and Communication Workshop and Conference (CCWC). :66–71.
Information security is a top priority in government and industry because high consequence cyber incidents continue with regularity. The blue teamers that protect cyber systems cannot stop or even know about all these incidents, so they must take measures to tolerate these incursions in addition to preventing and detecting them. We propose dynamically compartmentalizing subject networks into collaboration zones and limiting the communication between these zones. In this article, we demonstrate this technique's effect on the attacker and the defender for various parameter settings using discrete-time simulation. Based on our results, we conclude that dynamic cyber zone defense is a viable intrusion tolerance technique and should be considered for technology transfer.
2020-07-20
Guelton, Serge, Guinet, Adrien, Brunet, Pierrick, Martinez, Juan Manuel, Dagnat, Fabien, Szlifierski, Nicolas.  2018.  [Research Paper] Combining Obfuscation and Optimizations in the Real World. 2018 IEEE 18th International Working Conference on Source Code Analysis and Manipulation (SCAM). :24–33.
Code obfuscation is the de facto standard to protect intellectual property when delivering code in an unmanaged environment. It relies on additive layers of code tangling techniques, white-box encryption calls and platform-specific or tool-specific countermeasures to make it harder for a reverse engineer to access critical pieces of data or to understand core algorithms. The literature provides plenty of different obfuscation techniques that can be used at compile time to transform data or control flow in order to provide some kind of protection against different reverse engineering scenarii. Scheduling code transformations to optimize a given metric is known as the pass scheduling problem, a problem known to be NP-hard, but solved in a practical way using hard-coded sequences that are generally satisfactory. Adding code obfuscation to the problem introduces two new dimensions. First, as a code obfuscator needs to find a balance between obfuscation and performance, pass scheduling becomes a multi-criteria optimization problem. Second, obfuscation passes transform their inputs in unconventional ways, which means some pass combinations may not be desirable or even valid. This paper highlights several issues met when blindly chaining different kind of obfuscation and optimization passes, emphasizing the need of a formal model to combine them. It proposes a non-intrusive formalism to leverage on sequential pass management techniques. The model is validated on real-world scenarii gathered during the development of an industrial-strength obfuscator on top of the LLVM compiler infrastructure.
Sima, Mihai, Brisson, André.  2017.  Whitenoise encryption implementation with increased robustness to side-channel attacks. 2017 IEEE SmartWorld, Ubiquitous Intelligence Computing, Advanced Trusted Computed, Scalable Computing Communications, Cloud Big Data Computing, Internet of People and Smart City Innovation (SmartWorld/SCALCOM/UIC/ATC/CBDCom/IOP/SCI). :1–4.
Two design techniques improve the robustness of Whitenoise encryption algorithm implementation to side-channel attacks based on dynamic and/or static power consumption. The first technique conceals the power consumption and has linear cost. The second technique randomizes the power consumption and has quadratic cost. These techniques are not mutually exclusive; their synergy provides a good robustness to power analysis attacks. Other circuit-level protection can be applied on top of the proposed techniques, opening the avenue for generating very robust implementations.
Huang, Rui, Wang, Panbao, Zaery, Mohamed, Wei, Wang, Xu, Dianguo.  2019.  A Distributed Fixed-Time Secondary Controller for DC Microgrids. 2019 22nd International Conference on Electrical Machines and Systems (ICEMS). :1–6.

This paper proposes a distributed fixed-time based secondary controller for the DC microgrids (MGs) to overcome the drawbacks of conventional droop control. The controller, based on a distributed fixed-time control approach, can remove the DC voltage deviation and provide proportional current sharing simultaneously within a fixed-time. Comparing with the conventional centralized secondary controller, the controller, using the dynamic consensus, on each converter communicates only with its neighbors on a communication graph which increases the convergence speed and gets an improved performance. The proposed control strategy is simulated in PLECS to test the controller performance, link-failure resiliency, plug and play capability and the feasibility under different time delays.

2020-07-10
Cai, Zhipeng, Miao, Dongjing, Li, Yingshu.  2019.  Deletion Propagation for Multiple Key Preserving Conjunctive Queries: Approximations and Complexity. 2019 IEEE 35th International Conference on Data Engineering (ICDE). :506—517.
This paper studies the deletion propagation problem in terms of minimizing view side-effect. It is a problem funda-mental to data lineage and quality management which could be a key step in analyzing view propagation and repairing data. The investigated problem is a variant of the standard deletion propagation problem, where given a source database D, a set of key preserving conjunctive queries Q, and the set of views V obtained by the queries in Q, we try to identify a set T of tuples from D whose elimination prevents all the tuples in a given set of deletions on views △V while preserving any other results. The complexity of this problem has been well studied for the case with only a single query. Dichotomies, even trichotomies, for different settings are developed. However, no results on multiple queries are given which is a more realistic case. We study the complexity and approximations of optimizing the side-effect on the views, i.e., find T to minimize the additional damage on V after removing all the tuples of △V. We focus on the class of key-preserving conjunctive queries which is a dichotomy for the single query case. It is surprising to find that except the single query case, this problem is NP-hard to approximate within any constant even for a non-trivial set of multiple project-free conjunctive queries in terms of view side-effect. The proposed algorithm shows that it can be approximated within a bound depending on the number of tuples of both V and △V. We identify a class of polynomial tractable inputs, and provide a dynamic programming algorithm to solve the problem. Besides data lineage, study on this problem could also provide important foundations for the computational issues in data repairing. Furthermore, we introduce some related applications of this problem, especially for query feedback based data cleaning.
2020-07-03
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.

2020-06-22
Das, Subhajit, Mondal, Satyendra Nath, Sanyal, Manas.  2019.  A Novel Approach of Image Encryption Using Chaos and Dynamic DNA Sequence. 2019 Amity International Conference on Artificial Intelligence (AICAI). :876–880.
In this paper, an image encryption scheme based on dynamic DNA sequence and two dimension logistic map is proposed. Firstly two different pseudo random sequences are generated using two dimension Sine-Henon alteration map. These sequences are used for altering the positions of each pixel of plain image row wise and column wise respectively. Secondly each pixels of distorted image and values of random sequences are converted into a DNA sequence dynamically using one dimension logistic map. Reversible DNA operations are applied between DNA converted pixel and random values. At last after decoding the results of DNA operations cipher image is obtained. Different theoretical analyses and experimental results proved the effectiveness of this algorithm. Large key space proved that it is possible to protect different types of attacks using our proposed encryption scheme.
Bhavani, Y., Puppala, Sai Srikar, Krishna, B.Jaya, Madarapu, Srija.  2019.  Modified AES using Dynamic S-Box and DNA Cryptography. 2019 Third International conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC). :164–168.
Today the frequency of technological transformations is very high. In order to cope up with these, there is a demand for fast processing and secured algorithms should be proposed for data exchange. In this paper, Advanced Encryption Standard (AES) is modified using DNA cryptography for fast processing and dynamic S-boxes are introduced to develop an attack resistant algorithm. This is strengthened by combining symmetric and asymmetric algorithms. Diffie-Hellman key exchange is used for AES key generation and also for secret number generation used for creation of dynamic S-boxes. The proposed algorithm is fast in computation and can resist cryptographic attacks like linear and differential cryptanalysis attacks.
Gao, Ruichao, Ma, Xuebin.  2019.  Dynamic Data Publishing with Differential Privacy via Reinforcement Learning. 2019 IEEE 43rd Annual Computer Software and Applications Conference (COMPSAC). 1:746–752.
Differential privacy, which is due to its rigorous mathematical proof and strong privacy guarantee, has become a standard for the release of statistics with privacy protection. Recently, a lot of dynamic data publishing algorithms based on differential privacy have been proposed, but most of the algorithms use a native method to allocate the privacy budget. That is, the limited privacy budget is allocated to each time point uniformly, which may result in the privacy budget being unreasonably utilized and reducing the utility of data. In order to make full use of the limited privacy budget in the dynamic data publishing and improve the utility of data publishing, we propose a dynamic data publishing algorithm based on reinforcement learning in this paper. The algorithm consists of two parts: privacy budget allocation and data release. In the privacy budget allocation phase, we combine the idea of reinforcement learning and the changing characteristics of dynamic data, and establish a reinforcement learning model for the allocation of privacy budget. Finally, the algorithm finds a reasonable privacy budget allocation scheme to publish dynamic data. In the data release phase, we also propose a new dynamic data publishing strategy to publish data after the privacy budget is exhausted. Extensive experiments on real datasets demonstrate that our algorithm can allocate the privacy budget reasonably and improve the utility of dynamic data publishing.
2020-06-12
Zhang, Suman, Qin, Cai, Wang, Chaowei, Wang, Weidong, Zhang, Yinghai.  2018.  Slot Assignment Algorithm Based on Hash Function for Multi-target RFID System. 2018 IEEE/CIC International Conference on Communications in China (ICCC). :583—587.

Multi-tag identification technique has been applied widely in the RFID system to increase flexibility of the system. However, it also brings serious tags collision issues, which demands the efficient anti-collision schemes. In this paper, we propose a Multi-target tags assignment slots algorithm based on Hash function (MTSH) for efficient multi-tag identification. The proposed algorithm can estimate the number of tags and dynamically adjust the frame length. Specifically, according to the number of tags, the proposed algorithm is composed of two cases. when the number of tags is small, a hash function is constructed to map the tags into corresponding slots. When the number of tags is large, the tags are grouped and randomly mapped into slots. During the tag identification, tags will be paired with a certain matching rate and then some tags will exit to improve the efficiency of the system. The simulation results indicate that the proposed algorithm outperforms the traditional anti-collision algorithms in terms of the system throughput, stability and identification efficiency.

2020-05-15
Fan, Renshi, Du, Gaoming, Xu, Pengfei, Li, Zhenmin, Song, Yukun, Zhang, Duoli.  2019.  An Adaptive Routing Scheme Based on Q-learning and Real-time Traffic Monitoring for Network-on-Chip. 2019 IEEE 13th International Conference on Anti-counterfeiting, Security, and Identification (ASID). :244—248.
In the Network on Chip (NoC), performance optimization has always been a research focus. Compared with the static routing scheme, dynamical routing schemes can better reduce the data of packet transmission latency under network congestion. In this paper, we propose a dynamical Q-learning routing approach with real-time monitoring of NoC. Firstly, we design a real-time monitoring scheme and the corresponding circuits to record the status of traffic congestion for NoC. Secondly, we propose a novel method of Q-learning. This method finds an optimal path based on the lowest traffic congestion. Finally, we dynamically redistribute network tasks to increase the packet transmission speed and balance the traffic load. Compared with the C-XY routing and DyXY routing, our method achieved improvement in terms of 25.6%-49.5% and 22.9%-43.8%.
2020-04-10
Huang, Yongjie, Qin, Jinghui, Wen, Wushao.  2019.  Phishing URL Detection Via Capsule-Based Neural Network. 2019 IEEE 13th International Conference on Anti-counterfeiting, Security, and Identification (ASID). :22—26.

As a cyber attack which leverages social engineering and other sophisticated techniques to steal sensitive information from users, phishing attack has been a critical threat to cyber security for a long time. Although researchers have proposed lots of countermeasures, phishing criminals figure out circumventions eventually since such countermeasures require substantial manual feature engineering and can not detect newly emerging phishing attacks well enough, which makes developing an efficient and effective phishing detection method an urgent need. In this work, we propose a novel phishing website detection approach by detecting the Uniform Resource Locator (URL) of a website, which is proved to be an effective and efficient detection approach. To be specific, our novel capsule-based neural network mainly includes several parallel branches wherein one convolutional layer extracts shallow features from URLs and the subsequent two capsule layers generate accurate feature representations of URLs from the shallow features and discriminate the legitimacy of URLs. The final output of our approach is obtained by averaging the outputs of all branches. Extensive experiments on a validated dataset collected from the Internet demonstrate that our approach can achieve competitive performance against other state-of-the-art detection methods while maintaining a tolerable time overhead.

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.
2020-03-09
Cao, Yuan, Zhao, Yongli, Li, Jun, Lin, Rui, Zhang, Jie, Chen, Jiajia.  2019.  Reinforcement Learning Based Multi-Tenant Secret-Key Assignment for Quantum Key Distribution Networks. 2019 Optical Fiber Communications Conference and Exhibition (OFC). :1–3.
We propose a reinforcement learning based online multi-tenant secret-key assignment algorithm for quantum key distribution networks, capable of reducing tenant-request blocking probability more than half compared to the benchmark heuristics.
2020-02-17
Broomandi, Fateme, Ghasemi, Abdorasoul.  2019.  An Improved Cooperative Cell Outage Detection in Self-Healing Het Nets Using Optimal Cooperative Range. 2019 27th Iranian Conference on Electrical Engineering (ICEE). :1956–1960.
Heterogeneous Networks (Het Nets) are introduced to fulfill the increasing demands of wireless communications. To be manageable, it is expected that these networks are self-organized and in particular, self-healing to detect and relief faults autonomously. In the Cooperative Cell Outage Detection (COD), the Macro-Base Station (MBS) and a group of Femto-Base Stations (FBSs) in a specific range are cooperatively communicating to find out if each FBS is working properly or not. In this paper, we discuss the impacts of the cooperation range on the detection delay and accuracy and then conclude that there is an optimal amount for cooperation range which maximizes detection accuracy. We then derive the optimal cooperative range that improves the detection accuracy by using network parameters such as FBS's transmission power, noise power, shadowing fading factor, and path-loss exponent and investigate the impacts of these parameters on the optimal cooperative range. The simulation results show the optimal cooperative range that we proposed maximizes the detection accuracy.
Tunde-Onadele, Olufogorehan, He, Jingzhu, Dai, Ting, Gu, Xiaohui.  2019.  A Study on Container Vulnerability Exploit Detection. 2019 IEEE International Conference on Cloud Engineering (IC2E). :121–127.
Containers have become increasingly popular for deploying applications in cloud computing infrastructures. However, recent studies have shown that containers are prone to various security attacks. In this paper, we conduct a study on the effectiveness of various vulnerability detection schemes for containers. Specifically, we implement and evaluate a set of static and dynamic vulnerability attack detection schemes using 28 real world vulnerability exploits that widely exist in docker images. Our results show that the static vulnerability scanning scheme only detects 3 out of 28 tested vulnerabilities and dynamic anomaly detection schemes detect 22 vulnerability exploits. Combining static and dynamic schemes can further improve the detection rate to 86% (i.e., 24 out of 28 exploits). We also observe that the dynamic anomaly detection scheme can achieve more than 20 seconds lead time (i.e., a time window before attacks succeed) for a group of commonly seen attacks in containers that try to gain a shell and execute arbitrary code.
2020-01-28
Patel, Yogesh, Ouazzane, Karim, Vassilev, Vassil T., Faruqi, Ibrahim, Walker, George L..  2019.  Keystroke Dynamics Using Auto Encoders. 2019 International Conference on Cyber Security and Protection of Digital Services (Cyber Security). :1–8.

In the modern day and age, credential based authentication systems no longer provide the level of security that many organisations and their services require. The level of trust in passwords has plummeted in recent years, with waves of cyber attacks predicated on compromised and stolen credentials. This method of authentication is also heavily reliant on the individual user's choice of password. There is the potential to build levels of security on top of credential based authentication systems, using a risk based approach, which preserves the seamless authentication experience for the end user. One method of adding this security to a risk based authentication framework, is keystroke dynamics. Monitoring the behaviour of the users and how they type, produces a type of digital signature which is unique to that individual. Learning this behaviour allows dynamic flags to be applied to anomalous typing patterns that are produced by attackers using stolen credentials, as a potential risk of fraud. Methods from statistics and machine learning have been explored to try and implement such solutions. This paper will look at an Autoencoder model for learning the keystroke dynamics of specific users. The results from this paper show an improvement over the traditional tried and tested statistical approaches with an Equal Error Rate of 6.51%, with the additional benefits of relatively low training times and less reliance on feature engineering.

2020-01-20
Noura, Hassan, Couturier, Raphael, Pham, Congduc, Chehab, Ali.  2019.  Lightweight Stream Cipher Scheme for Resource-Constrained IoT Devices. 2019 International Conference on Wireless and Mobile Computing, Networking and Communications (WiMob). :1–8.

The Internet of Things (IoT) systems are vulnerable to many security threats that may have drastic impacts. Existing cryptographic solutions do not cater for the limitations of resource-constrained IoT devices, nor for real-time requirements of some IoT applications. Therefore, it is essential to design new efficient cipher schemes with low overhead in terms of delay and resource requirements. In this paper, we propose a lightweight stream cipher scheme, which is based, on one hand, on the dynamic key-dependent approach to achieve a high security level, and on the other hand, the scheme involves few simple operations to minimize the overhead. In our approach, cryptographic primitives change in a dynamic lightweight manner for each input block. Security and performance study as well as experimentation are performed to validate that the proposed cipher achieves a high level of efficiency and robustness, making it suitable for resource-constrained IoT devices.

Noura, Hassan, Chehab, Ali, Couturier, Raphael.  2019.  Lightweight Dynamic Key-Dependent and Flexible Cipher Scheme for IoT Devices. 2019 IEEE Wireless Communications and Networking Conference (WCNC). :1–8.

Security attacks against Internet of Things (IoT) are on the rise and they lead to drastic consequences. Data confidentiality is typically based on a strong symmetric-key algorithm to guard against confidentiality attacks. However, there is a need to design an efficient lightweight cipher scheme for a number of applications for IoT systems. Recently, a set of lightweight cryptographic algorithms have been presented and they are based on the dynamic key approach, requiring a small number of rounds to minimize the computation and resource overhead, without degrading the security level. This paper follows this logic and provides a new flexible lightweight cipher, with or without chaining operation mode, with a simple round function and a dynamic key for each input message. Consequently, the proposed cipher scheme can be utilized for real-time applications and/or devices with limited resources such as Multimedia Internet of Things (MIoT) systems. The importance of the proposed solution is that it produces dynamic cryptographic primitives and it performs the mixing of selected blocks in a dynamic pseudo-random manner. Accordingly, different plaintext messages are encrypted differently, and the avalanche effect is also preserved. Finally, security and performance analysis are presented to validate the efficiency and robustness of the proposed cipher variants.

Noma, Adamu Muhammad, Muhammad, Abdullah.  2019.  Stochastic Heuristic Approach to Addition Chain Problem in PKC for Efficiency and Security Effectiveness. 2019 International Conference on Information Networking (ICOIN). :55–59.

This paper shows that stochastic heuristic approach for implicitly solving addition chain problem (ACP) in public-key cryptosystem (PKC) enhances the efficiency of the PKC and improves the security by blinding the multiplications/squaring operations involved against side-channel attack (SCA). We show that while the current practical heuristic approaches being deterministic expose the fixed pattern of the operations, using stochastic method blinds the pattern by being unpredictable and generating diffident pattern of operation for the same exponent at a different time. Thus, if the addition chain (AC) is generated implicitly every time the exponentiation operation is being made, needless for such approaches as padding by insertion of dummy operations and the operation is still totally secured against the SCA. Furthermore, we also show that the stochastic approaches, when carefully designed, further reduces the length of the operation than state-of-the-art practical methods for improving the efficiency. We demonstrated our investigation by implementing RSA cryptosystem using the stochastic approach and the results benchmarked with the existing current methods.

2019-12-18
M, Suchitra, S M, Renuka, Sreerekha, Lingaraj K..  2018.  DDoS Prevention Using D-PID. 2018 Second International Conference on Intelligent Computing and Control Systems (ICICCS). :453-457.

In recent years, the attacks on systems have increased and among such attack is Distributed Denial of Service (DDoS) attack. The path identifiers (PIDs) used for inter-domain routing are static, which makes it easier the attack easier. To address this vulnerability, this paper addresses the usage of Dynamic Path Identifiers (D-PIDs) for routing. The PID of inter-domain path connector is kept oblivious and changes dynamically, thus making it difficult to attack the system. The prototype designed with major components like client, server and router analyses the outcome of D-PID usage instead of PIDs. The results show that, DDoS attacks can be effectively prevented if Dynamic Path Identifiers (D-PIDs) are used instead of Static Path Identifiers (PIDs).

2019-12-09
Yang, Chao, Chen, Xinghe, Song, Tingting, Jiang, Bin, Liu, Qin.  2018.  A Hybrid Recommendation Algorithm Based on Heuristic Similarity and Trust Measure. 2018 17th IEEE International Conference On Trust, Security And Privacy In Computing And Communications/ 12th IEEE International Conference On Big Data Science And Engineering (TrustCom/BigDataSE). :1413–1418.
In this paper, we propose a hybrid collaborative filtering recommendation algorithm based on heuristic similarity and trust measure, in order to alleviate the problem of data sparsity, cold start and trust measure. Firstly, a new similarity measure is implemented by weighted fusion of multiple similarity influence factors obtained from the rating matrix, so that the similarity measure becomes more accurate. Then, a user trust relationship computing model is implemented by constructing the user's trust network based on the trust propagation theory. On this basis, a SIMT collaborative filtering algorithm is designed which integrates trust and similarity instead of the similarity in traditional collaborative filtering algorithm. Further, an improved K nearest neighbor recommendation based on clustering algorithm is implemented for generation of a better recommendation list. Finally, a comparative experiment on FilmTrust dataset shows that the proposed algorithm has improved the quality and accuracy of recommendation, thus overcome the problem of data sparsity, cold start and trust measure to a certain extent.
2019-08-05
Xia, S., Li, N., Xiaofeng, T., Fang, C..  2018.  Multiple Attributes Based Spoofing Detection Using an Improved Clustering Algorithm in Mobile Edge Network. 2018 1st IEEE International Conference on Hot Information-Centric Networking (HotICN). :242–243.

Information centric network (ICN) based Mobile Edge Computing (MEC) network has drawn growing attentions in recent years. The distributed network architecture brings new security problems, especially the identity security problem. Because of the cloud platform deployed on the edge of the MEC network, multiple channel attributes can be easily obtained and processed. Thus this paper proposes a multiple channel attributes based spoofing detection mechanism. To further reduce the complexity, we also propose an improved clustering algorithm. The simulation results indicate that the proposed spoofing detection method can provide near-optimal performance with extremely low complexity.