Conference Papers
Permanent URI for this collectionhttps://idr.nitk.ac.in/handle/123456789/28506
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Item Distributed Adaptive Video Streaming using Inter-Server Data Distribution and Agent-based Adaptive Load Balancing(Institute of Electrical and Electronics Engineers Inc., 2020) Bhowmik, M.; Raghunandan, A.; Rudra, B.As the number and hours of videos available within an organisation increases, as well as it's demand, the need for fast video streaming applications arises. Cloud based services are not cost effective and are not an ideal choice for storing the ever-increasing video data that is usually stored and used only within a particular organisation, like a University. Hence, this paper proposes a web based system design to store and stream videos at a small-scale within an organisation. To improve the video viewing experience for the user, the system is flexible to handle sudden changes, like increase in number of requests. The system requires the use of a cluster of servers to deliver the content as a single server cannot handle the load as number of requests increases. This requires effective load distribution among the servers. This paper proposes a way to design this system for adaptive video streaming. This system is highly scalable and can handle high loads, i.e. a higher number of users connecting to the application simultaneously. This paper proposes an algorithm called inter-server load balancing algorithm with Adaptive Agent-based load balancing to solve this problem. The algorithms also incorporates dynamic video resolution delivery techniques to ensure smooth viewing experience in the whole user experience irrespective of the network speed and bandwidth. © 2020 IEEE.Item Similarity Calculation of Executable Using Intel Pin Instrumentation Framework(Institute of Electrical and Electronics Engineers Inc., 2020) Bhowmik, M.; Nara, M.; Mohan, B.R.With the increase in the number of open-source and commercial code in the market, copyright and license infringement cases are on the rise. The lack of availability of source code makes identification a difficult task as existing techniques heavily rely on the source code. We propose two similarity measurement methods using the instruction log and the call-trace of each executable using Intel Pin tool. A Software Plagiarism Detector(SPD) was developed using the Intel Pin instrumentation tool and we have tested this approach on different small executable single-threaded and multi-threaded files. The results portray the validity of this method. We also talk about the possibility to expand this method for bigger software. © 2020 IEEE.Item Software verification using state diagrams(Grenze Scientific Society, 2021) Bhowmik, M.; Chowdhary, A.; Rudra, B.During the development of software, a programmer will commit many logical errors unknowingly such that the software is not in accordance with the requirements. Such logical errors affect the correctness of the software. The requirements specify some important properties of the software and this knowledge about it will allow to know the behavior of the software which can be leveraged to find certain logical errors. This paper proposes a method which helps to find bugs as well as describes a way by which the programmer can specify software requirements. Based on these programmer specified requirements, the system can be automatically used to verify the software. Also, the method proposed in this paper does not need to use the expected result of a test case to verify the software’s correctness. The proposed algorithm completely relies on the requirements specified by the programmer for finding bugs in the software. The software verification process and the algorithm used is explained with the help of a case study. The paper highlights the advantages of the method and algorithm proposed for software verification along with the implementation details. © Grenze Scientific Society, 2021.Item FQDN similarity and cache-miss property based DNS tunneling detection technique(Grenze Scientific Society, 2021) Bhowmik, M.; Chowdhary, A.; Rudra, B.Although there are many effective methods to detect DNS Tunneling attacks, the attacks still happen, and the attackers can mock genuine queries to bypass such checks. However, in data exfiltration, the DNS queries are continuously changing as some part of it represents the data itself. Thus, all such queries would result in a cache miss, and therefore we can use such properties to detect DNS Tunneling attacks. However, relying on this is not enough as it will also have many false positives. To overcome the problem, we propose three criteria-based methods that consider DNS Tunneling queries’ properties and use them to reduce the number of false positives and thus accurately detect DNS Tunneling traffic. We even discussed the bypassing checks in this paper, and they are both costly and require the attacker to make redundant queries. © Grenze Scientific Society, 2021.Item Comparative Study of Machine Learning Algorithms for Fraud Detection in Blockchain(Institute of Electrical and Electronics Engineers Inc., 2021) Bhowmik, M.; Sai Siri Chandana, T.; Rudra, B.Fraudulent transactions have a huge impact on the economy and trust of a blockchain network. Consensus algorithms like proof of work or proof of stake can verify the validity of the transaction but not the nature of the users involved in the transactions or those who verify the transactions. This makes a blockchain network still vulnerable to fraudulent activities. One of the ways to eliminate fraud is by using machine learning techniques. Machine learning can be of supervised or unsupervised nature. In this paper, we use various supervised machine learning techniques to check for fraudulent and legitimate transactions. We also provide an extensive comparative study of various supervised machine learning techniques like decision trees, Naive Bayes, logistic regression, multilayer perceptron, and so on for the above task. © 2021 IEEE.Item DNS tunneling detection using machine learning and cache miss properties(Institute of Electrical and Electronics Engineers Inc., 2021) Chowdhary, A.; Bhowmik, M.; Rudra, B.In a DNS Tunneling attack, data or other useful information is embedded within a DNS query and exfiltrated. Such attacks are difficult to detect because DNS is a fundamental protocol and blocking legitimate domain names can lead to an unpleasant experience for the users. Thus, detecting whether the DNS query is exfiltrating data or not is a challenging task. Mimicking genuine queries by the attacker makes this even more difficult. This research work presents two different methods for detecting the DNS Tunneling query and later they are combined to build a DNS Tunneling Attack Detector that can inform the client about a potential attack going on in real time. The first method uses cache misses in a DNS cache server and the second method utilizes machine learning techniques to classify a given DNS query. Overall, with around 93% accuracy of certain Machine Learning classifiers on classifying on a per packet basis along with extra validation from the cache-miss approach, a detector has been developed to accurately report DNS tunneling traffic © 2021 IEEE.