• International Journal of Computer Science & Network Security
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• 제23권4호
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• pp.111-115
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• 2023

Knowledge Based Authentication is the most well-known technique for user authentication in a computer security framework. Most frameworks utilize a straightforward PIN (Personal Identification Number) or psssword as an data authenticator. Since password based authenticators typically will be software based, they are inclined to different attacks and weaknesses, from both human and software.Some of the attacks are talked about in this paper.

• 한국정보보호학회:학술대회논문집
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• 한국정보보호학회 2003년도 하계학술대회논문집
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• pp.121-125
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• 2003

In this paper, we propose a framework of Transmission Control Protocol with Software Rejuvenation methodology, which is applicable for network system survivability. This method is utilized to improve the survivability because it can limit the damage caused by successful attacks. The main objectives are to detect intrusions in real time, to characterize attacks, and to survive in face of attacks. To counter act the attacks' attempts or intrusions, we perform the Software Rejuvenation methods such as killing the intruders' processes in their tracks, halting abuse before it happens, shutting down unauthorized connection, and responding and restarting in real time. These slogans will really frustrate and deter the attacks, as the attacker can't make their progress. This is the way of survivability to maximize the deterrence against an attack in the target environment. We address a framework to model and analyze the critical intrusion tolerance problems ahead of intrusion detection on Transmission Control Protocol (TCP).

• International Journal of Computer Science & Network Security
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• 제21권1호
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• pp.174-183
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• 2021

The advent of social media has revolutionized the speed of communication between millions of people around the world in various cultures and disciplines. Social media is the best platform for exchanging opinions and ideas, interacting with other users of similar interests and sharing different types of media and files. With the phenomenal increase in the use of social media platforms, the need to pay attention to protection and security from attacks and misuse has also increased. The present study conducts a comprehensive survey of the latest and most important research studies published from 2018-20 on security and privacy on social media and types of threats and attacks that affect the users. We have also reviewed the recent challenges that affect security features in social media. Furthermore, this research pursuit also presents effective and feasible solutions that address these threats and attacks and cites recommendations to increase security and privacy for the users of social media.

• International Journal of Computer Science & Network Security
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• 제23권5호
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• pp.179-192
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• 2023

The widespread use of Cloud Computing, Internet of Things (IoT), and social media in the Information Communication Technology (ICT) field has resulted in continuous and unavoidable cyber-attacks on users and critical infrastructures worldwide. Traditional security measures such as firewalls and encryption systems are not effective in countering these sophisticated cyber-attacks. Therefore, Intrusion Detection and Prevention Systems (IDPS) are necessary to reduce the risk to an absolute minimum. Although IDPSs can detect various types of cyber-attacks with high accuracy, their performance is limited by a high false alarm rate. This study proposes a new technique called Fuzzy Logic - Objective Risk Analysis (FLORA) that can significantly reduce false positive alarm rates and maintain a high level of security against serious cyber-attacks. The FLORA model has a high fuzzy accuracy rate of 90.11% and can predict vulnerabilities with a high level of certainty. It also has a mechanism for monitoring and recording digital forensic evidence which can be used in legal prosecution proceedings in different jurisdictions.

• 디지털산업정보학회논문지
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• 제11권4호
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• pp.33-45
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• 2015

Currently, Internet is used an essential tool in the business area. Despite this importance, there is a risk of network attacks attempting collection of fraudulence, private information, and cyber terrorism. Firewalls and IDS(Intrusion Detection System) are tools against those attacks. IDS is used to determine whether a network data is a network attack. IDS analyzes the network data using various techniques including expert system, data mining, and state transition analysis. This paper tries to compare the performance of two data mining models in detecting network attacks. They are decision tree (C4.5), and neural network (FANN model). I trained and tested these models with data and measured the effectiveness in terms of detection accuracy, detection rate, and false alarm rate. This paper tries to find out which model is effective in intrusion detection. In the analysis, I used KDD Cup 99 data which is a benchmark data in intrusion detection research. I used an open source Weka software for C4.5 model, and C++ code available for FANN model.

• International Journal of Computer Science & Network Security
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• 제21권1호
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• pp.107-118
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• 2021

The term Internet of Things (IoTs) refers to the future where things are known daily through the Internet, whether in one way or another, as it is done by the method of collecting various information from various sensors to form a huge network through which people, things and machines are helped to make a link between them at all time and anywhere. The IoTs is everywhere around us such as connected appliances, smart homes security systems and wearable health monitors. However, the question is what if there is a malfunction or outside interference that affects the work of these IoTs based devises? This is the reason of the spread of security causes great concern with the widespread availability of the Internet and Internet devices that are subject to many attacks. Since there aren't many studies that combines requirements, mechanisms, and the attacks of the IoTs, this paper which explores recent published studies between 2017 and 2020 considering different security approaches of protection related to the authentication, integrity, availability and confidentiality Additionally, the paper addresses the different types of attacks in IoTs. We have also addressed the different approaches aim to prevention mechanisms according to several researchers' conclusions and recommendations.

• Journal of information and communication convergence engineering
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• 제20권4호
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• pp.250-258
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• 2022

The emerging scope of the Internet-of-Things (IoT) has piqued the interest of industry and academia in recent times. Therefore, security becomes the main issue to prevent the possibility of cyberattacks. Jamming attacks are threads that can affect performance and cause significant problems for IoT device. This study explores a smart jamming attack (coalition attack) in which the attackers were previously a part of the legitimate network and are now back to attack it based on the gained knowledge. These attackers regroup into a coalition and begin exchanging information about the legitimate network to launch attacks based on the gained knowledge. Our system enables jammer nodes to select the optimal transmission rates for attacks based on the attack probability table, which contains the most probable link transmission rate between nodes in the legitimate network. The table is updated constantly throughout the life cycle of the coalition. The simulation results show that a coalition of jammers can cause highly successful attacks.

• 사물인터넷융복합논문지
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• 제3권1호
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• pp.9-11
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• 2017

최근에 인터넷과 모바일 장치가 널리 보급되면서 해커들이 네트워크를 이용해 공격하는 횟수 또한 증가하고 있다. 네트워크를 연결할 때 패킷을 주고받으며 통신을 하게 되는데, 여기에는 다양한 정보가 포함되어 있다. 이 패킷들의 정보를 Hierarchical Clustering 분석을 사용해 분석하고 정상적인 패킷과 비정상적인 패킷을 분류하여 공격자들의 공격을 탐지하였다. 이 분석 방법을 통해 새로운 패킷을 분석하여 공격을 탐지하는 것이 가능할 것이다.

• Journal of Communications and Networks
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• 제14권3호
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• pp.310-318
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• 2012

Detecting intrusion attacks accurately and rapidly in wireless networks is one of the most challenging security problems. Intrusion attacks of various types can be detected by the change in traffic flow that they induce. Wireless industrial networks based on the wireless networks for industrial automation-process automation (WIA-PA) standard use a superframe to schedule network communications. We propose an intrusion detection system for WIA-PA networks. After modeling and analyzing traffic flow data by time-sequence techniques, we propose a data traffic prediction model based on autoregressive moving average (ARMA) using the time series data. The model can quickly and precisely predict network traffic. We initialized the model with data traffic measurements taken by a 16-channel analyzer. Test results show that our scheme can effectively detect intrusion attacks, improve the overall network performance, and prolong the network lifetime.

• International Journal of Computer Science & Network Security
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• 제24권4호
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• pp.170-178
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• 2024

Through the growth of the fifth-generation networks and artificial intelligence technologies, new threats and challenges have appeared to wireless communication system, especially in cybersecurity. And IoT networks are gradually attractive stages for introduction of DDoS attacks due to integral frailer security and resource-constrained nature of IoT devices. This paper emphases on detecting DDoS attack in wireless networks by categorizing inward network packets on the transport layer as either "abnormal" or "normal" using the integration of machine learning algorithms knowledge-based system. In this paper, deep learning algorithms and CNN were autonomously trained for mitigating DDoS attacks. This paper lays importance on misuse based DDOS attacks which comprise TCP SYN-Flood and ICMP flood. The researcher uses CICIDS2017 and NSL-KDD dataset in training and testing the algorithms (model) while the experimentation phase. accuracy score is used to measure the classification performance of the four algorithms. the results display that the 99.93 performance is recorded.