Do Wook Kang

Bio: Do Wook Kang is an academic researcher from Electronics and Telecommunications Research Institute. The author has contributed to research in topics: Orthogonal frequency-division multiplexing & Communications system. The author has an hindex of 3, co-authored 9 publications receiving 52 citations.

Performance Analysis on Channel Estimation with Antenna Diversity of OFDM Reception in Multi-path Fast Fading Channel

Abstract: Vehicle to everything (V2X) communication supports vehicle to anything communication for vehicle safety and cooperative Intelligent Transport System in vehicular environments. IEEE 802.11p modem has been developed and applied for V2X communication system. V2X radio channel has multipath fast fading due to moving vehicles and surrounded road structure. We proposed a new DFCE-AD which combines DFCE and antenna diversity for OFDM reception and analyzed the performance improvement in multipath fading channel. Through computer simulation, SNR gain of DFCE-AD over DFCE for QPSK modulation is approximately 6 dB at PER = 10%. In other word, PER of DFCE-AD is improved over that of DFCE by about 20% at SNR = 10 dB. This result will be applied for the short sized packet and low order OFDM modulation in vehicular multipath fading channel.

Design and implementation of high speed A/D converter using time interleaving

Abstract: Time Interleaved A/D Converter (TI-ADC) is a parallel combination of multiple ADCs having low sampling rates and it is capable of high-speed sampling in real time. The performance of the digital system to receive the analog signal depends on the performance of the ADC. However it has the disadvantage of increasing the cost using high-speed sampling ADC. To solve this problem, high-speed sampling ADC is implemented using time interleaving by a combination of a low cost, low-speed ADC and can be used for a system demanding high-speed real time sampling technology.

Ensemble-based multi-filter feature selection method for DDoS detection in cloud computing

Abstract: Widespread adoption of cloud computing has increased the attractiveness of such services to cybercriminals. Distributed denial of service (DDoS) attacks targeting the cloud’s bandwidth, services and resources to render the cloud unavailable to both cloud providers, and users are a common form of attacks. In recent times, feature selection has been identified as a pre-processing phase in cloud DDoS attack defence which can potentially increase classification accuracy and reduce computational complexity by identifying important features from the original dataset during supervised learning. In this work, we propose an ensemble-based multi-filter feature selection method that combines the output of four filter methods to achieve an optimum selection. We then perform an extensive experimental evaluation of our proposed method using intrusion detection benchmark dataset, NSL-KDD and decision tree classifier. The findings show that our proposed method can effectively reduce the number of features from 41 to 13 and has a high detection rate and classification accuracy when compared to other classification techniques.

Ensemble-based Multi-Filter Feature Selection Method for DDoS Detection in Cloud Computing

Abstract: Increasing interest in the adoption of cloud computing has exposed it to cyber-attacks. One of such is distributed denial of service (DDoS) attack that targets cloud bandwidth, services and resources to make it unavailable to both the cloud providers and users. Due to the magnitude of traffic that needs to be processed, data mining and machine learning classification algorithms have been proposed to classify normal packets from an anomaly. Feature selection has also been identified as a pre-processing phase in cloud DDoS attack defence that can potentially increase classification accuracy and reduce computational complexity by identifying important features from the original dataset, during supervised learning. In this work, we propose an ensemble-based multi-filter feature selection method that combines the output of four filter methods to achieve an optimum selection. An extensive experimental evaluation of our proposed method was performed using intrusion detection benchmark dataset, NSL-KDD and decision tree classifier. The result obtained shows that our proposed method effectively reduced the number of features from 41 to 13 and has a high detection rate and classification accuracy when compared to other classification techniques.

Towards a Reliable Intrusion Detection Benchmark Dataset

Abstract: The urgently growing number of security threats on Internet and intranet networks highly demands reliable security solutions. Among various options, Intrusion Detection (IDSs) and Intrusion Prevention Systems (IPSs) are used to defend network infrastructure by detecting and preventing attacks and malicious activities. The performance of a detection system is evaluated using benchmark datasets. There exist a number of datasets, such as DARPA98, KDD99, ISC2012, and ADFA13, that have been used by researchers to evaluate the performance of their intrusion detection and prevention approaches. However, not enough research has focused on the evaluation and assessment of the datasets themselves and there is no reliable dataset in this domain. In this paper, we present a comprehensive evaluation of the existing datasets using our proposed criteria, a design and evaluation framework for IDS and IPS datasets, and a dataset generation model to create a reliable IDS or IPS benchmark dataset.

An Evaluation Framework for Intrusion Detection Dataset

Abstract: The growing number of security threats on the Internet and computer networks demands highly reliable security solutions. Meanwhile, Intrusion Detection (IDSs) and Intrusion Prevention Systems (IPSs) have an important role in the design and development of a robust network infrastructure that can defend computer networks by detecting and blocking a variety of attacks. Reliable benchmark datasets are critical to test and evaluate the performance of a detection system. There exist a number of such datasets, for example, DARPA98, KDD99, ISC2012, and ADFA13 that have been used by the researchers to evaluate the performance of their intrusion detection and prevention approaches. However, not enough research has focused on the evaluation and assessment of the datasets themselves. In this paper we present a comprehensive evaluation of the existing datasets using our proposed criteria, and propose an evaluation framework for IDS and IPS datasets.

Computational intelligence intrusion detection techniques in mobile cloud computing environments: Review, taxonomy, and open research issues

Abstract: With the increasing utilization of the Internet and its provided services, an increase in cyber-attacks to exploit the information occurs. A technology to store and maintain user's information that is mostly used for its simplicity and low-cost services is cloud computing (CC). Also, a new model of computing that is noteworthy today is mobile cloud computing (MCC) that is used to reduce the limitations of mobile devices by allowing them to offload certain computations to the remote cloud. The cloud environment may consist of critical or essential information of an organization; therefore, to prevent this environment from possible attacks a security solution is needed. An intrusion detection system (IDS) is a solution to these security issues. An IDS is a hardware or software device that can examine all inside and outside network activities and recognize doubtful patterns that may demonstrate a network attack and automatically alert the network (or system) administrator. Because of the ability of an IDS to detect known/unknown (inside/outside) attacks, it is an excellent choice for securing cloud computing. Various methods are used in an intrusion detection system to recognize attacks more accurately. Unlike survey papers presented so far, this paper aims to present a comprehensive survey of intrusion detection systems that use computational intelligence (CI) methods in a (mobile) cloud environment. We firstly provide an overview of CC and MCC paradigms and service models, also reviewing security threats in these contexts. Previous literature is critically surveyed, highlighting the advantages and limitations of previous work. Then we define a taxonomy for IDS and classify CI-based techniques into single and hybrid methods. Finally, we highlight open issues and future directions for research on this topic.