• International Journal of Computer Science & Network Security
• /
• v.23 no.11
• /
• pp.99-109
• /
• 2023

The darknet is frequently referred to as the hub of illicit online activity. In order to keep track of real-time applications and activities taking place on Darknet, traffic on that network must be analysed. It is without a doubt important to recognise network traffic tied to an unused Internet address in order to spot and investigate malicious online activity. Any observed network traffic is the result of mis-configuration from faked source addresses and another methods that monitor the unused space address because there are no genuine devices or hosts in an unused address block. Digital systems can now detect and identify darknet activity on their own thanks to recent advances in artificial intelligence. In this paper, offer a generalised method for deep learning-based detection and classification of darknet traffic. Furthermore, analyse a cutting-edge complicated dataset that contains a lot of information about darknet traffic. Next, examine various feature selection strategies to choose a best attribute for detecting and classifying darknet traffic. For the purpose of identifying threats using network properties acquired from darknet traffic, devised a hybrid deep learning (DL) approach that combines Recurrent Neural Network (RNN) and Bidirectional LSTM (BiLSTM). This probing technique can tell malicious traffic from legitimate traffic. The results show that the suggested strategy works better than the existing ways by producing the highest level of accuracy for categorising darknet traffic using the Black widow optimization algorithm as a feature selection approach and RNN-BiLSTM as a recognition model.

• Journal of the Korea Institute of Information Security & Cryptology
• /
• v.22 no.1
• /
• pp.107-115
• /
• 2012

The level of information protection is relatively low, in comparison with the informatisation in this country. The budget for information protection is also quite marginal at 5% of the entire information-related policy budget. The passive information protection practices by companies, which focus more on the aftermaths, lead to repeated expenses for risk management. The responses to the violation of information protection should be changed from the current aftermaths-oriented focus to prevention and early detection of possible violations. We should also realize that the response to a violation of protected information is not a responsibility of an individual but a joint responsibility of the nation and the industry. South Korea has been working towards to building a systematic foundation since 2004 when guidelines were announced regarding the information protection policy and the safety diagnosis. The current level of safety policies cannot provide a perfect protection against actual violation cases in administrative, technological and physical ways. This research evaluates the level of prevention that the current systematic protection policy offers, and discusses its limitation and possible ways for improvement. It also recommends a list effective measures for protection against information violation that companies can employ to maintain the actual target safety level.

• Journal of Digital Contents Society
• /
• v.17 no.6
• /
• pp.499-507
• /
• 2016

The era of IoT, which figures exchanging data from the internet between things is coming. Recently, former electric power energy policy paradigm, namely Supply side paradigm, is changing, because electric power energy consumption is rapidly increasing. As new paradigm for this limit, convergence of existing electric power grid and ICT(Information and Communication Technology) will accelerate intellectualization of electric power device, its operation system. This change brought opened electric power grid. Consequently, attacks to the national electric power grid are increasing. On this paper, we will analyze security threats of existing IoT, discuss security weakness on electric power industry IoT and suggest needed security requirements, security technology.

• KIPS Transactions on Computer and Communication Systems
• /
• v.5 no.11
• /
• pp.419-426
• /
• 2016

As the threat of cyber incident grows continuously, the need of IOC(Indicators of Compromise) is increasing to identify the cause of incidents and share it for quick response to similar incidents. But only few companies use it domestically and the research about the application of IOC is deficient compared to foreign countries. Therefore in this paper, a quick and standardized way to create IOC automatically based on the analysis result of malwares from Cuckoo Sandbox and its application is suggested.

• IEIE Transactions on Smart Processing and Computing
• /
• v.5 no.2
• /
• pp.94-99
• /
• 2016

Application layer attacks have for years posed an ever-serious threat to network security, since they always come after a technically legitimate connection has been established. In recent years, cyber criminals have turned to fully exploiting the web as a medium of communication to launch a variety of forbidden or illicit activities by spreading malicious automated software (auto-ware) such as adware, spyware, or bots. When this malicious auto-ware infects a network, it will act like a robot, mimic normal behavior of web access, and bypass the network firewall or intrusion detection system. Besides that, in a private and large network, with huge Hypertext Transfer Protocol (HTTP) traffic generated each day, communication behavior identification and classification of auto-ware is a challenge. In this paper, based on a previous study, analysis of auto-ware communication behavior, and with the addition of new features, a method for classification of HTTP auto-ware communication is proposed. For that, a Not Only Structured Query Language (NoSQL) database is applied to handle large volumes of unstructured HTTP requests captured every day. The method is tested with real HTTP traffic data collected through a proxy server of a private network, providing good results in the classification and detection of suspicious auto-ware web access.

• Journal of Korea Multimedia Society
• /
• v.24 no.1
• /
• pp.58-66
• /
• 2021

The user's password must be encrypted one-way through the hash function and stored in the database. Widely used hash functions such as MD5 and SHA-1 have also been found to have vulnerabilities, and hash functions that are considered safe can also have vulnerabilities over time. Salt enhances password security by adding it before or after the password before putting it to the hash function. In the case of the existing Salt, even if it is randomly assigned to each user, once it is assigned, it is a fixed value in a specific column of the database. If the database is exposed to an attacker, it poses a great threat to password cracking. In this paper, we suggest variable-dynamic Salt that dynamically changes according to the user's password during the login process. The variable-dynamic Salt can further enhance password security during login process by making it difficult to know what the Salt is, even if the database or source code is exposed.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.15 no.3
• /
• pp.1100-1118
• /
• 2021

In a wide range of ML applications, the training data contains privacy-sensitive information that should be kept secure. Training the ML systems by privacy-sensitive data makes the ML model inherent to the data. As the structure of the model has been fine-tuned by training data, the model can be abused for accessing the data by the estimation in a reverse process called model inversion attack (MIA). Although, MIA has been applied to shallow neural network models of recognizers in literature and its threat in privacy violation has been approved, in the case of a deep learning (DL) model, its efficiency was under question. It was due to the complexity of a DL model structure, big number of DL model parameters, the huge size of training data, big number of registered users to a DL model and thereof big number of class labels. This research work first analyses the possibility of MIA on a deep learning model of a recognition system, namely a face recognizer. Second, despite the conventional MIA under the white box scenario of having partial access to the users' non-sensitive information in addition to the model structure, the MIA is implemented on a deep face recognition system by just having the model structure and parameters but not any user information. In this aspect, it is under a semi-white box scenario or in other words a gray-box scenario. The experimental results in targeting five registered users of a CNN-based face recognition system approve the possibility of regeneration of users' face images even for a deep model by MIA under a gray box scenario. Although, for some images the evaluation recognition score is low and the generated images are not easily recognizable, but for some other images the score is high and facial features of the targeted identities are observable. The objective and subjective evaluations demonstrate that privacy cyber-attack by MIA on a deep recognition system not only is feasible but also is a serious threat with increasing alert state in the future as there is considerable potential for integration more advanced ML techniques to MIA.

• Journal of the Korea Academia-Industrial cooperation Society
• /
• v.11 no.3
• /
• pp.920-925
• /
• 2010

A home network system is made up of home devices and wire and wireless network can not only be the subject of cyber attack from a variety factors of threatening, but also have security weakness in cases of hacking, vicious code, worm virus, DoS attack, tapping of communication network, and more. As a result, a variety of problems such as abuse of private life, and exposure and stealing of personal information arose. Therefore, the necessity for a security protocol to protect user asset and personal information within a home network is gradually increasing. Thus, this dissertation designs and suggests a home network security protocol using user authentication and approach-control technology to prevent the threat by unauthorized users towards personal information and user asset in advance by providing the gradual authority to corresponding devices based on authorized information, after authorizing the users with a Public Key Certificate.

• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.23 no.10
• /
• pp.1311-1319
• /
• 2019

Currently, the web environment is a commonly used area for sharing information and conducting business. It is becoming an attack point for external hacking targeting on personal information leakage or system failure. Conventional signature-based detection is used in cyber threat but signature-based detection has a limitation that it is difficult to detect the pattern when it is changed like polymorphism. In particular, injection attack is known to the most critical security risks based on web vulnerabilities and various variants are possible at any time. In this paper, we propose a novelty detection technique to detect abnormal state that deviates from the normal state on web-server log dataset(WSLD). The proposed method is a machine learning-based technique to detect a minor anomalous data that tends to be different from a large number of normal data after replacing strings in web-server log dataset with vectors using machine learning-based embedding algorithm.

• Convergence Security Journal
• /
• v.22 no.1
• /
• pp.129-134
• /
• 2022

A smart platform is defined as an evolved platform that realizes physical and virtual space into a hyper-connected environment by combining the existing platform and advanced IT technology. The hyper-connection that is the connection between information and information, infrastructure and infrastructure, infrastructure and information, or space and service, enables the realization and provision of high-quality services that significantly change the quality of life and environment of users. In addition, it is providing everyone with the effect of significantly improving the social safety net and personal health management level by implementing smart government and smart healthcare. A lot of information produced and consumed in these processes can act as a factor threatening the basic rights of the public and individuals by the informations themselves or through big data analysis. In particular, as the smart platform as a core function that forms the ecosystem of a smart city is naturally and continuously expanded, it faces a huge security burden in data processing and network operation. In this paper, platform components as core functions of smart city and appropriate security threats and countermeasures are studied.