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RISS 인기검색어
- 검색키워드
- 저자 : Prem Jacob T. (검색결과 3 건)
- 무료
- 기관 내 무료
- 유료
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V. Kanimozhi,T. Prem Jacob 한국통신학회 2021 ICT Express Vol.7 No.3
Our paramount task is to examine and detect network attacks, is one of the daunting tasks because the variety of attacks are day by day existing in colossal number. The program proposed detects botnet attacks using the newest CSE-CIC-IDS2018 cyber dataset published by the Canadian Cybersecurity Establishment (CIC). The cyber dataset can be accessed on AWS (Amazon Web Services). The realistic network dataset consists of all the modern and existing attacks such as Brute-force attacks and password cracking, Heartbleed, Botnet, DoS (Denial of Service), DDoS also known as Distributed Denial of Service, Web attacks i.e. vulnerable web app attacks, and infiltration of the network from inside. The objective of the proposed research is to identify a classification of Botnet attacks. Botnet attack is a Trojan Horse malware attack that poses a serious security threat to the banking and financial sectors. Since a specific classifier could possibly work for such datasets it is crucial to finish a comparative examination of classifiers in order to achieve the most noteworthy execution in such basic detection of network attacks. The proposed framework is to incorporate different classifier methods such as KNearset Neighbor classifier, Naïve Bayes, Adaboost with Decision Tree, Support Vector Machine classifier, Random Forest classifier, and Artificial Intelligence to distinguish a portrayal of botnet attacks on the recent and realistic cyber dataset CSE-CIC-IDS2018. The results of the classification are given as precise precision for the specific classifiers. And furthermore, the proposed framework uses the Calibration curve as a standard approach in analytical methods which generates reliability diagrams to check the predicted probabilities of various classifiers are well-calibrated or not. Finally, the displayed graph proves how well the artificial intelligence technique outperforms all other classifiers which generates reliability diagrams to check the predicted probabilities of various classifiers are well-calibrated or not.
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V. Kanimozhi,T. Prem Jacob 한국통신학회 2019 ICT Express Vol.5 No.3
One of the latest emerging technologies is artificial intelligence, which makes the machine mimic human behaviour. The most important component used to detect cyber attacks or malicious activities is the intrusion detection system (IDS). Artificial intelligence plays a vital role in detecting intrusions and widely considered as the better way in adapting and building IDS. In modern days, neural network algorithms are emerging as a new artificial intelligence technique that can be applied to real-time problems. The proposed system is to detect a classification of botnet attack which poses a serious threat to financial sectors and banking services. The proposed system is created by applying artificial intelligence on a realistic cyber defence dataset (CSE-CIC-IDS2018), the latest IDS Dataset in 2018 by Canadian Institute for Cybersecurity (CIC) on AWS (Amazon Web Services). The proposed system of Artificial Neural Networks provides an outstanding performance of Accuracy score is 99.97% and an average area under ROC(Receiver Operator Characteristic) curve is 0.999 and an average False Positive rate is a mere value of 0.03. The proposed system of Artificial Intelligence-based Intrusion detection of botnet attack classification is powerful, more accurate and precise. The novel proposed system can be applied to conventional network traffic analysis, cyber-physical system traffic analysis and also can be applied to the real-time network traffic data analysis.
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BIP: A dimensionality reduction for image indexing
Minu R.I.,Nagarajan G.,Prem Jacob T.,Pravin A. 한국통신학회 2019 ICT Express Vol.5 No.3
Searching on internet is one of the daily task done by millions of users around the Globe. There is an urge for effective indexing scheme for unstructured data, which provide better search results. The image, content report, and site pages are said to be unstructured database. These information are not sorted out so the recovery rates of these information are purposely low when contrasted with indexed database. These information are indexed concerning their cues is one of the challenging areas to be explored. For the client require, the data recovery segment endeavors to coordinate the client question with the indexed database will provide effective recovery rates. In this paper a new image indexing scheme called Basic intrinsic pattern (BIP) is elaborated, which uses the image intensity as the key variable for image indexing. The algorithm is evaluated with respect to the size of the feature vector and the retrieval rate.
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