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Talluri, Sacheendra, Iosup, Alexandru.  2019.  Efficient Estimation of Read Density When Caching for Big Data Processing. IEEE INFOCOM 2019 - IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS). :502–507.

Big data processing systems are becoming increasingly more present in cloud workloads. Consequently, they are starting to incorporate more sophisticated mechanisms from traditional database and distributed systems. We focus in this work on the use of caching policies, which for big data raise important new challenges. Not only they must respond to new variants of the trade-off between hit rate, response time, and the space consumed by the cache, but they must do so at possibly higher volume and velocity than web and database workloads. Previous caching policies have not been tested experimentally with big data workloads. We address these challenges in this work. We propose the Read Density family of policies, which is a principled approach to quantify the utility of cached objects through a family of utility functions that depend on the frequency of reads of an object. We further design the Approximate Histogram, which is a policy-based technique based on an array of counters. This technique promises to achieve runtime-space efficient computation of the metric required by the cache policy. We evaluate through trace-based simulation the caching policies from the Read Density family, and compare them with over ten state-of-the-art alternatives. We use two workload traces representative for big data processing, collected from commercial Spark and MapReduce deployments. While we achieve comparable performance to the state-of-art with less parameters, meaningful performance improvement for big data workloads remain elusive.

Yin, Mingyong, Wang, Qixu, Cao, Mingsheng.  2019.  An Attack Vector Evaluation Method for Smart City Security Protection. 2019 International Conference on Wireless and Mobile Computing, Networking and Communications (WiMob). :1–7.
In the network security risk assessment on critical information infrastructure of smart city, to describe attack vectors for predicting possible initial access is a challenging task. In this paper, an attack vector evaluation model based on weakness, path and action is proposed, and the formal representation and quantitative evaluation method are given. This method can support the assessment of attack vectors based on known and unknown weakness through combination of depend conditions. In addition, defense factors are also introduced, an attack vector evaluation model of integrated defense is proposed, and an application example of the model is given. The research work in this paper can provide a reference for the vulnerability assessment of attack vector.
Bansal, Bhawana, Sharma, Monika.  2019.  Client-Side Verification Framework for Offline Architecture of IoT. 2019 3rd International conference on Electronics, Communication and Aerospace Technology (ICECA). :1044–1050.
Internet of things is a network formed between two or more devices through internet which helps in sharing data and resources. IoT is present everywhere and lot of applications in our day-to-day life such as smart homes, smart grid system which helps in reducing energy consumption, smart garbage collection to make cities clean, smart cities etc. It has some limitations too such as concerns of security of the network and the cost of installations of the devices. There have been many researches proposed various method in improving the IoT systems. In this paper, we have discussed about the scope and limitations of IoT in various fields and we have also proposed a technique to secure offline architecture of IoT.
Selvi J., Anitha Gnana, kalavathy G., Maria.  2019.  Probing Image and Video Steganography Based On Discrete Wavelet and Discrete Cosine Transform. 2019 Fifth International Conference on Science Technology Engineering and Mathematics (ICONSTEM). 1:21–24.

Now-a-days, video steganography has developed for a secured communication among various users. The two important factor of steganography method are embedding potency and embedding payload. Here, a Multiple Object Tracking (MOT) algorithmic programs used to detect motion object, also shows foreground mask. Discrete wavelet Transform (DWT) and Discrete Cosine Transform (DCT) are used for message embedding and extraction stage. In existing system Least significant bit method was proposed. This technique of hiding data may lose some data after some file transformation. The suggested Multiple object tracking algorithm increases embedding and extraction speed, also protects secret message against various attackers.

Gao, Jian, Bai, Huifeng, Wang, Dongshan, Wang, Licheng, Huo, Chao, Hou, Yingying.  2019.  Rapid Security Situation Prediction of Smart Grid Based on Markov Chain. 2019 IEEE 3rd Information Technology, Networking, Electronic and Automation Control Conference (ITNEC). :2386–2389.

Based on Markov chain analysis method, the situation prediction of smart grid security and stability can be judged in this paper. First component state transition probability matrix and component state prediction were defined. A fast derivation method of Markov state transition probability matrix using in system state prediction was proposed. The Matlab program using this method was compiled to analyze and obtain the future state probability distribution of grid system. As a comparison the system state distribution was simulated based on sequential Monte Carlo method, which was in good agreement with the state transition matrix, and the validity of the method was verified. Furthermore, the situation prediction of the six-node example was analyzed, which provided an effective prediction and analysis tool for the security situation.

Talukder, Md Arabin Islam, Shahriar, Hossain, Qian, Kai, Rahman, Mohammad, Ahamed, Sheikh, Wu, Fan, Agu, Emmanuel.  2019.  DroidPatrol: A Static Analysis Plugin For Secure Mobile Software Development. 2019 IEEE 43rd Annual Computer Software and Applications Conference (COMPSAC). 1:565–569.

While the number of mobile applications are rapidly growing, these applications are often coming with numerous security flaws due to the lack of appropriate coding practices. Security issues must be addressed earlier in the development lifecycle rather than fixing them after the attacks because the damage might already be extensive. Early elimination of possible security vulnerabilities will help us increase the security of our software and mitigate or reduce the potential damages through data losses or service disruptions caused by malicious attacks. However, many software developers lack necessary security knowledge and skills required at the development stage, and Secure Mobile Software Development (SMSD) is not yet well represented in academia and industry. In this paper, we present a static analysis-based security analysis approach through design and implementation of a plugin for Android Development Studio, namely DroidPatrol. The proposed plugins can support developers by providing list of potential vulnerabilities early.

Yao, Chuhao, Wang, Jiahong, Kodama, Eiichiro.  2019.  A Spam Review Detection Method by Verifying Consistency among Multiple Review Sites. 2019 IEEE 21st International Conference on High Performance Computing and Communications; IEEE 17th International Conference on Smart City; IEEE 5th International Conference on Data Science and Systems (HPCC/SmartCity/DSS). :2825–2830.

In recent years, websites that incorporate user reviews, such as Amazon, IMDB and YELP, have become exceedingly popular. As an important factor affecting users purchasing behavior, review information has been becoming increasingly important, and accordingly, the reliability of review information becomes an important issue. This paper proposes a method to more accurately detect the appearance period of spam reviews and to identify the spam reviews by verifying the consistency of review information among multiple review sites. Evaluation experiments were conducted to show the accuracy of the detection results, and compared the newly proposed method with our previously proposed method.

Lekha, J., Maheshwaran, J, Tharani, K, Ram, Prathap K, Surya, Murthy K, Manikandan, A.  2019.  Efficient Detection of Spam Messages Using OBF and CBF Blocking Techniques. 2019 3rd International Conference on Trends in Electronics and Informatics (ICOEI). :1175–1179.

Emails are the fundamental unit of web applications. There is an exponential growth in sending and receiving emails online. However, spam mail has turned into an intense issue in email correspondence condition. There are number of substance based channel systems accessible to be specific content based filter(CBF), picture based sifting and many other systems to channel spam messages. The existing technological solution consists of a combination of porter stemer algorithm(PSA) and k means clustering which is adaptive in nature. These procedures are more expensive in regard of the calculation and system assets as they required the examination of entire spam message and calculation of the entire substance of the server. These are the channels must additionally not powerful in nature life on the grounds that the idea of spam block mail and spamming changes much of the time. We propose a starting point based spam mail-sifting system benefit, which works considering top head notcher data of the mail message paying little respect to the body substance of the mail. It streamlines the system and server execution by increasing the precision, recall and accuracy than the existing methods. To design an effective and efficient of autonomous and efficient spam detection system to improve network performance from unknown privileged user attacks.

Benmalek, Mourad, Challal, Yacine, Derhab, Abdelouahid.  2019.  An Improved Key Graph Based Key Management Scheme for Smart Grid AMI Systems. 2019 IEEE Wireless Communications and Networking Conference (WCNC). :1–6.

In this paper, we focus on versatile and scalable key management for Advanced Metering Infrastructure (AMI) in Smart Grid (SG). We show that a recently proposed key graph based scheme for AMI systems (VerSAMI) suffers from efficiency flaws in its broadcast key management protocol. Then, we propose a new key management scheme (iVerSAMI) by modifying VerSAMI's key graph structure and proposing a new broadcast key update process. We analyze security and performance of the proposed broadcast key management in details to show that iVerSAMI is secure and efficient in terms of storage and communication overheads.

Li, Shu, Tian, Jianwei, Zhu, Hongyu, Tian, Zheng, Qiao, Hong, Li, Xi, Liu, Jie.  2019.  Research in Fast Modular Exponentiation Algorithm Based on FPGA. 2019 11th International Conference on Measuring Technology and Mechatronics Automation (ICMTMA). :79–82.
Modular exponentiation of large number is widely applied in public-key cryptosystem, also the bottleneck in the computation of public-key algorithm. Modular multiplication is the key calculation in modular exponentiation. An improved Montgomery algorithm is utilized to achieve modular multiplication and converted into systolic array to increase the running frequency. A high efficiency fast modular exponentiation structure is developed to bring the best out of the modular multiplication module and enhance the ability of defending timing attacks and power attacks. For 1024-bit key operands, the design can be run at 170MHz and finish a modular exponentiation in 4,402,374 clock cycles.
Xiao, Kaiming, Zhu, Cheng, Xie, Junjie, Zhou, Yun, Zhu, Xianqiang, Zhang, Weiming.  2018.  Dynamic Defense Strategy against Stealth Malware Propagation in Cyber-Physical Systems. IEEE INFOCOM 2018 - IEEE Conference on Computer Communications. :1790–1798.
Stealth malware, a representative tool of advanced persistent threat (APT) attacks, in particular poses an increased threat to cyber-physical systems (CPS). Due to the use of stealthy and evasive techniques (e.g., zero-day exploits, obfuscation techniques), stealth malwares usually render conventional heavyweight countermeasures (e.g., exploits patching, specialized ant-malware program) inapplicable. Light-weight countermeasures (e.g., containment techniques), on the other hand, can help retard the spread of stealth malwares, but the ensuing side effects might violate the primary safety requirement of CPS. Hence, defenders need to find a balance between the gain and loss of deploying light-weight countermeasures. To address this challenge, we model the persistent anti-malware process as a shortest-path tree interdiction (SPTI) Stackelberg game, and safety requirements of CPS are introduced as constraints in the defender's decision model. Specifically, we first propose a static game (SSPTI), and then extend it to a multi-stage dynamic game (DSPTI) to meet the need of real-time decision making. Both games are modelled as bi-level integer programs, and proved to be NP-hard. We then develop a Benders decomposition algorithm to achieve the Stackelberg Equilibrium of SSPTI. Finally, we design a model predictive control strategy to solve DSPTI approximately by sequentially solving an approximation of SSPTI. The extensive simulation results demonstrate that the proposed dynamic defense strategy can achieve a balance between fail-secure ability and fail-safe ability while retarding the stealth malware propagation in CPS.
Sun, Xiaoyan, Dai, Jun, Liu, Peng, Singhal, Anoop, Yen, John.  2016.  Towards probabilistic identification of zero-day attack paths. 2016 IEEE Conference on Communications and Network Security (CNS). :64–72.
Zero-day attacks continue to challenge the enterprise network security defense. A zero-day attack path is formed when a multi-step attack contains one or more zero-day exploits. Detecting zero-day attack paths in time could enable early disclosure of zero-day threats. In this paper, we propose a probabilistic approach to identify zero-day attack paths and implement a prototype system named ZePro. An object instance graph is first built from system calls to capture the intrusion propagation. To further reveal the zero-day attack paths hiding in the instance graph, our system constructs an instance-graph-based Bayesian network. By leveraging intrusion evidence, the Bayesian network can quantitatively compute the probabilities of object instances being infected. The object instances with high infection probabilities reveal themselves and form the zero-day attack paths. The experiment results show that our system can effectively identify zero-day attack paths.
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.

Halimaa A., Anish, Sundarakantham, K..  2019.  Machine Learning Based Intrusion Detection System. 2019 3rd International Conference on Trends in Electronics and Informatics (ICOEI). :916–920.

In order to examine malicious activity that occurs in a network or a system, intrusion detection system is used. Intrusion Detection is software or a device that scans a system or a network for a distrustful activity. Due to the growing connectivity between computers, intrusion detection becomes vital to perform network security. Various machine learning techniques and statistical methodologies have been used to build different types of Intrusion Detection Systems to protect the networks. Performance of an Intrusion Detection is mainly depends on accuracy. Accuracy for Intrusion detection must be enhanced to reduce false alarms and to increase the detection rate. In order to improve the performance, different techniques have been used in recent works. Analyzing huge network traffic data is the main work of intrusion detection system. A well-organized classification methodology is required to overcome this issue. This issue is taken in proposed approach. Machine learning techniques like Support Vector Machine (SVM) and Naïve Bayes are applied. These techniques are well-known to solve the classification problems. For evaluation of intrusion detection system, NSL- KDD knowledge discovery Dataset is taken. The outcomes show that SVM works better than Naïve Bayes. To perform comparative analysis, effective classification methods like Support Vector Machine and Naive Bayes are taken, their accuracy and misclassification rate get calculated.

Krasnobaev, Victor, Kuznetsov, Alexandr, Babenko, Vitalina, Denysenko, Mykola, Zub, Mihael, Hryhorenko, Vlada.  2019.  The Method of Raising Numbers, Represented in the System of Residual Classes to an Arbitrary Power of a Natural Number. 2019 IEEE 2nd Ukraine Conference on Electrical and Computer Engineering (UKRCON). :1133–1138.

Methods for implementing integer arithmetic operations of addition, subtraction, and multiplication in the system of residual classes are considered. It is shown that their practical use in computer systems can significantly improve the performance of the implementation of arithmetic operations. A new method has been developed for raising numbers represented in the system of residual classes to an arbitrary power of a natural number, both in positive and in negative number ranges. An example of the implementation of the proposed method for the construction of numbers represented in the system of residual classes for the value of degree k = 2 is given.

Rezaeighaleh, Hossein, Laurens, Roy, Zou, Cliff C..  2018.  Secure Smart Card Signing with Time-based Digital Signature. 2018 International Conference on Computing, Networking and Communications (ICNC). :182–187.
People use their personal computers, laptops, tablets and smart phones to digitally sign documents in company's websites and other online electronic applications, and one of the main cybersecurity challenges in this process is trusted digital signature. While the majority of systems use password-based authentication to secure electronic signature, some more critical systems use USB token and smart card to prevent identity theft and implement the trusted digital signing process. Even though smart card provides stronger security, any weakness in the terminal itself can compromise the security of smart card. In this paper, we investigate current smart card digital signature, and illustrate well-known basic vulnerabilities of smart card terminal with the real implementation of two possible attacks including PIN sniffing and message alteration just before signing. As we focus on second attack in this paper, we propose a novel mechanism using time-based digital signing by smart card to defend against message alteration attack. Our prototype implementation and performance analysis illustrate that our proposed mechanism is feasible and provides stronger security. Our method uses popular timestamping protocol packets and does not require any new key distribution and certificate issuance.
Kim, Sang Wu, Liu, Xudong.  2018.  Crypto-Aided Bayesian Detection of False Data in Short Messages. 2018 IEEE Statistical Signal Processing Workshop (SSP). :253-257.

We propose a crypto-aided Bayesian detection framework for detecting false data in short messages with low overhead. The proposed approach employs the Bayesian detection at the physical layer in parallel with a lightweight cryptographic detection, followed by combining the two detection outcomes. We develop the maximum a posteriori probability (MAP) rule for combining the cryptographic and Bayesian detection outcome, which minimizes the average probability of detection error. We derive the probability of false alarm and missed detection and discuss the improvement of detection accuracy provided by the proposed method.

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).

Mohan, K Manju.  2018.  An Efficient system to stumble on and Mitigate DDoS attack in cloud Environment. 2018 Second International Conference on Inventive Communication and Computational Technologies (ICICCT). :1855–1857.
Cloud computing is an assured progression inside the future of facts generation. It's far a sub-domain of network security. These days, many huge or small organizations are switching to cloud which will shop and arrange their facts. As a result, protection of cloud networks is the want of the hour. DDoS is a killer software for cloud computing environments on net today. It is a distributed denial of carrier. we will beat the ddos attacks if we have the enough assets. ddos attacks can be countered by means of dynamic allocation of the assets. In this paper the attack is detected as early as possible and prevention methods is done and also mitigation method is also implemented thus attack can be avoided before it may occur.
Karve, Shreya, Nagmal, Arati, Papalkar, Sahil, Deshpande, S. A..  2018.  Context Sensitive Conversational Agent Using DNN. 2018 Second International Conference on Electronics, Communication and Aerospace Technology (ICECA). :475–478.
We investigate a method of building a closed domain intelligent conversational agent using deep neural networks. A conversational agent is a dialog system intended to converse with a human, with a coherent structure. Our conversational agent uses a retrieval based model that identifies the intent of the input user query and maps it to a knowledge base to return appropriate results. Human conversations are based on context, but existing conversational agents are context insensitive. To overcome this limitation, our system uses a simple stack based context identification and storage system. The conversational agent generates responses according to the current context of conversation. allowing more human-like conversations.
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.
Alemán, Concepción Sánchez, Pissinou, Niki, Alemany, Sheila, Boroojeni, Kianoosh, Miller, Jerry, Ding, Ziqian.  2018.  Context-Aware Data Cleaning for Mobile Wireless Sensor Networks: A Diversified Trust Approach. 2018 International Conference on Computing, Networking and Communications (ICNC). :226–230.
In mobile wireless sensor networks (MWSN), data imprecision is a common problem. Decision making in real time applications may be greatly affected by a minor error. Even though there are many existing techniques that take advantage of the spatio-temporal characteristics exhibited in mobile environments, few measure the trustworthiness of sensor data accuracy. We propose a unique online context-aware data cleaning method that measures trustworthiness by employing an initial candidate reduction through the analysis of trust parameters used in financial markets theory. Sensors with similar trajectory behaviors are assigned trust scores estimated through the calculation of “betas” for finding the most accurate data to trust. Instead of devoting all the trust into a single candidate sensor's data to perform the cleaning, a Diversified Trust Portfolio (DTP) is generated based on the selected set of spatially autocorrelated candidate sensors. Our results show that samples cleaned by the proposed method exhibit lower percent error when compared to two well-known and effective data cleaning algorithms in tested outdoor and indoor scenarios.
Wang, Pengfei, Wang, Fengyu, Lin, Fengbo, Cao, Zhenzhong.  2018.  Identifying Peer-to-Peer Botnets Through Periodicity Behavior Analysis. 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). :283-288.

Peer-to-Peer botnets have become one of the significant threat against network security due to their distributed properties. The decentralized nature makes their detection challenging. It is important to take measures to detect bots as soon as possible to minimize their harm. In this paper, we propose PeerGrep, a novel system capable of identifying P2P bots. PeerGrep starts from identifying hosts that are likely engaged in P2P communications, and then distinguishes P2P bots from P2P hosts by analyzing their active ratio, packet size and the periodicity of connection to destination IP addresses. The evaluation shows that PeerGrep can identify all P2P bots with quite low FPR even if the malicious P2P application and benign P2P application coexist within the same host or there is only one bot in the monitored network.

Sathiyamurthi, P, Ramakrishnan, S, Shobika, S, Subashri, N, Prakavi, M.  2018.  Speech and Audio Cryptography System using Chaotic Mapping and Modified Euler's System. 2018 Second International Conference on Inventive Communication and Computational Technologies (ICICCT). :606–611.
Security often requires that the data must be kept safe from unauthorized access. And the best line of speech communication is security. However, most computers are interconnected with each other openly, thereby exposing them and the communication channels that person uses. Speech cryptography secures information by protecting its confidentiality. It can also be used to protect information about the integrity and authenticity of data. Stronger cryptographic techniques are needed to ensure the integrity of data stored on a machine that may be infected or under attack. So far speech cryptography is used in many forms but using it with Audio file is another stronger technique. The process of cryptography happens with audio file for transferring more secure sensitive data. The audio file is encrypted and decrypted by using Lorenz 3D mapping and then 3D mapping function is converted into 2D mapping function by using euler's numerical resolution and strong algorithm provided by using henon mapping and then decrypted by using reverse of encryption. By implementing this, the resultant audio file will be in secured form.
Arpitha, R, Chaithra, B R, Padma, Usha.  2019.  Performance Analysis of Channel Coding Techniques for Cooperative Adhoc Network. 2019 3rd International conference on Electronics, Communication and Aerospace Technology (ICECA). :752–756.
-In wireless networks, Cooperative communication can be used to increase the strength of the communication by means of spatial diversity. Basic idea that exists behind Cooperative communication is, if the transmission from source to destination is not successful, a helping node called relay can be used to send the same information to the destination through independent paths. In order to improve the performance of such communication, channel coding techniques can be used which reduces the Bit Error Rate. Previous works on cooperative communication only concentrated on improving channel capacity through cooperation. Hence this paper presents different Channel coding methods such as Turbo coding, Convolutional coding, and low-density parity-check coding over Rayleigh fading channels in the presence of Additive white Gaussian noise. Performance of these Channel coding techniques are measured in terms of noise power spectral density (NO ) vs. Bit error rate.