• Journal of the Korea Institute of Information and Communication Engineering
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• v.12 no.4
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• pp.661-669
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• 2008

Nowadays, authentication is essential to identify the legal users in a network communication. Usually, there are few wars to achieve authentication over a publicly accessible network system in order to protect certain private data from the unauthorized users, ranging from simple ID/Password to Biometrics System. One of the most active areas in OTP(One Time Password) research today aims at exploiting OTP to provide authentication in the finance and security industry. OTP is usually discarded once it has been used. this prevents huge loophole of traditional authentication system which employs the same ID and Password every time. However this OTP system also has its weaknesses in surviving some attacks. this paper proposes an advanced OTP protocol using PingPong-128 without loop hole of pre-existing OTP.

• Convergence Security Journal
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• v.18 no.5_1
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• pp.59-66
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• 2018

The performance of a network intrusion detection system using the machine learning method depends heavily on the composition and the size of the feature set. The detection accuracy, such as the detection rate or the false positive rate, of the system relies on the feature composition. And the time it takes to train and detect depends on the size of the feature set. Therefore, in order to enable the system to detect intrusions in real-time, the feature set to beused should have a small size as well as an appropriate composition. In this paper, we show that the size of the feature set can be further reduced without decreasing the detection rate through using Pearson correlation coefficient between features along with the multi-objective genetic algorithm which was used to shorten the size of the feature set in previous work. For the evaluation of the proposed method, the experiments to classify 10 kinds of attacks and benign traffic are performed against NSL_KDD data set.

• International Journal of Computer Science & Network Security
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• v.21 no.12
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• pp.235-247
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• 2021

Today, the cloud computing has become a major demand of many organizations. The major reason behind this expansion is due to its cloud's sharing infrastructure with higher computing efficiency, lower cost and higher fle3xibility. But, still the security is being a hurdle that blocks the success of the cloud computing platform. Therefore, a novel Multi-tenant Decentralized Information Flow Control (MT-DIFC) model is introduced in this research work. The proposed system will encapsulate four types of entities: (1) The central authority (CA), (2) The encryption proxy (EP), (3) Cloud server CS and (4) Multi-tenant Cloud virtual machines. Our contribution resides within the encryption proxy (EP). Initially, the trust level of all the users within each of the cloud is computed using the proposed two-stage trust computational model, wherein the user is categorized bas primary and secondary users. The primary and secondary users vary based on the application and data owner's preference. Based on the computed trust level, the access privilege is provided to the cloud users. In EP, the cipher text information flow security strategy is implemented using the blowfish encryption model. For the data encryption as well as decryption, the key generation is the crucial as well as the challenging part. In this research work, a new optimal key generation is carried out within the blowfish encryption Algorithm. In the blowfish encryption Algorithm, both the data encryption as well as decryption is accomplishment using the newly proposed optimal key. The proposed optimal key has been selected using a new Self Improved Cat and Mouse Based Optimizer (SI-CMBO), which has been an advanced version of the standard Cat and Mouse Based Optimizer. The proposed model is validated in terms of encryption time, decryption time, KPA attacks as well.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.22 no.11
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• pp.1544-1553
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• 2018

Recently internal information leakage in industries is severely increasing in spite of industry security policy. Thus, it is essential to prepare an information leakage prevention measure by industries. Most of the leaks result from the insiders, not from external attacks. In this paper, a real-time internal information leakage prevention system via both storage and network is implemented in order to protect confidential file leakage. In addition, a Hadoop-based user behavior analysis and statistics system is designed and implemented for storing and analyzing information log data in industries. The proposed system stores a large volume of data in HDFS and improves data processing capability using RHive, consequently helps the administrator recognize and prepare the confidential file leak trials. The implemented audit system would be contributed to reducing the damage caused by leakage of confidential files inside of the industries via both portable data media and networks.

• The Journal of Society for e-Business Studies
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• v.24 no.2
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• pp.15-27
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• 2019

In the past, the control system was a closed network that was not connected to the external network. However, in recent years, many cases have been opened to the outside for the convenience of management. Are connected to the Internet, and the number of operating control systems is increasing. As a result, it is obvious that hackers are able to make various attack attempts targeting the control system due to external open, and they are exposed to various security threats and are targeted for attack. Industrial control systems that are open to the outside have most of the remote management ports for web services or remote management, and the expansion of web services through web programs inherits the common web vulnerability as the control system is no exception. In this study, we classify and compare existing web vulnerability items in order to derive the most commonly tried web hacking attacks against control system from the attacker's point of view. I tried to confirm.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.11
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• pp.5568-5587
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• 2018

This research uses artificial intelligence methods for computer network intrusion detection system modeling. Primary classification is done using self-organized maps (SOM) in two levels, while the secondary classification of ambiguous data is done using Sugeno type Fuzzy Inference System (FIS). FIS is created by using Adaptive Neuro-Fuzzy Inference System (ANFIS). The main challenge for this system was to successfully detect attacks that are either unknown or that are represented by very small percentage of samples in training dataset. Improved algorithm for SOMs in second layer and for the FIS creation is developed for this purpose. Number of clusters in the second SOM layer is optimized by using our improved algorithm to minimize amount of ambiguous data forwarded to FIS. FIS is created using ANFIS that was built on ambiguous training dataset clustered by another SOM (which size is determined dynamically). Proposed hybrid model is created and tested using NSL KDD dataset. For our research, NSL KDD is especially interesting in terms of class distribution (overlapping). Objectives of this research were: to successfully detect intrusions represented in data with small percentage of the total traffic during early detection stages, to successfully deal with overlapping data (separate ambiguous data), to maximize detection rate (DR) and minimize false alarm rate (FAR). Proposed hybrid model with test data achieved acceptable DR value 0.8883 and FAR value 0.2415. The objectives were successfully achieved as it is presented (compared with the similar researches on NSL KDD dataset). Proposed model can be used not only in further research related to this domain, but also in other research areas.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.22 no.4
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• pp.869-882
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• 2012

With the growth of Web services and the development of web exploit toolkits, web-based malware has increased dramatically. Using Javascript Obfuscation, recent web-based malware hide a malicious URL and the exploit code. Thus, pattern matching for network intrusion detection systems has difficulty of detecting malware. Though various methods have proposed to detect Javascript malware on a users' web browser, the overall detection is needed to counter advanced attacks such as APTs(Advanced Persistent Treats), aimed at penetration into a certain an organization's intranet. To overcome the limitation of previous pattern matching for network intrusion detection systems, a novel deobfuscating method to handle obfuscated Javascript is needed. In this paper, we propose a framework for effective hidden malware detection through an automated deobfuscation regardless of advanced obfuscation techniques with overriding JavaScript functions and a separate JavaScript interpreter through to improve jsunpack-n.

• International Journal of Computer Science & Network Security
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• v.22 no.1
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• pp.105-112
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• 2022

The development and spread of IT-technologies has raised interest in teaching programming pupils. The article deals with problems related to programming and ways to overcome them. The importance of programming skills is emphasized, as this process promotes the formation of algorithmic thinking of pupils. The authors determined the level of pupils' interest to programing learning depending on the age. The analysis has showed that the natural interest of younger pupils in programming is decreasing over the years and in the most productive period of its study is minimized. It is revealed that senior school pupils are characterized by low level of interest in the study of programming; lack of motivation; the presence of psychological blocks on their own abilities in the context of programming; law level of computer science understanding. To overcome these problems, we conducted the second stage of the experiment, which was based on a change in the approach to programing learning, which involved pupils of non-specialized classes of senior school (experimental group). During the study of programming, special attention was paid to the motivational and psychological component, as well as the use of game technologies and teamwork of pupils. The results of the pedagogical experiment on studying the effectiveness of teaching programming for pupils of nonspecialized classes are presented. Improvement of the results provided the use of social and cognitive motives; application of verbal and non-verbal, external and internal means; communicative attacks; stimulation and psychological setting; game techniques, independent work and reflection, teamwork. The positive effect of the implemented methods is shown by the results verified by the methods of mathematical statistics in the experimental and control groups of pupils.

• Journal of Internet Computing and Services
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• v.23 no.3
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• pp.1-12
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• 2022

As the performance of Unmanned Aerial Vehicles (UAVs) are improving and the prices are lowering, it is expected that the use of UAVs will continuously grow in the future. It is important to always maintain control signal and video communication to operate remote UAVs stably, especially in military UAV operations, as unexpected jamming attacks can result in fatal UAV crashes. In this paper, to improve the network reliability and low latency when supporting UAV operations, the anti-jamming performance of Second generation Anti-jam Tactical UHF Radio for NATO (SATURN) networks is analyzed and enhanced by applying Forward Error Correction (FEC) and Minimum Shift Keying (MSK) modulation as well as Hamming code based multiple transmission techniques.

• International Journal of Computer Science & Network Security
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• v.22 no.5
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• pp.342-347
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• 2022

On Cloud, the important data of the user that is protected on remote servers can be accessed via internet. Due to rapid shift in technology nowadays, there is a swift increase in the confidential and pivotal data. This comes up with the requirement of data security of the user's data. Data is of different type and each need discrete degree of conservation. The idea of data security data science permits building the computing procedure more applicable and bright as compared to conventional ones in the estate of data security. Our focus with this paper is to enhance the safety of data on the cloud and also to obliterate the problems associated with the data security. In our suggested plan, some basic solutions of security like cryptographic techniques and authentication are allotted in cloud computing world. This paper put your heads together about how machine learning techniques is used in data security in both offensive and defensive ventures, including analysis on cyber-attacks focused at machine learning techniques. The machine learning technique is based on the Supervised, UnSupervised, Semi-Supervised and Reinforcement Learning. Although numerous research has been done on this topic but in reference with the future scope a lot more investigation is required to be carried out in this field to determine how the data can be secured more firmly on cloud in respect with the Machine Learning Techniques and cryptographic methods.