Practical In-Depth Analysis of IDS Alerts for Tracing and Identifying Potential Attackers on Darknet

Song, Jungsuk; Lee, Youn-Su; Choi, Jangwon; Gil, Joon‐Min; Han, Jaekyung; Choi, Sang-Soo

doi:10.3390/su9020262

Cited by 9 publications

(5 citation statements)

References 20 publications

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“…CPS sensing devices communicate via wireless communication technologies to enhance the system's flexibility, but due to the broadcast nature of wireless media, they pose more security risks [121,122] and are susceptible to multiple cyber-physical attacks. A cyber-attack is the attack performed via malware or access to communication network components.…”

Section: Data Security and Privacymentioning

confidence: 99%

Cyber-Physical Systems for Water Supply Network Management: Basics, Challenges, and Roadmap

Adedeji

Hamam

2020

Sustainability

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Water supply systems are dynamic in nature, owing to the effect of climate change and consumer demand uncertainties. The operation of such a system must be managed effectively to meet up with the uncertainties, thus posing a key challenge. Unfortunately, previous information and communication technology (ICT) solutions could not provide the necessary support for applications to deal with the dynamics of the changing physical environment. Nevertheless, tremendous growth in technology offers better possibilities to enhance water supply systems’ operations. As a result, development in technology in sensing and instrumentation, communication and networking, computing and control is now jointly integrated with water supply system infrastructures to enhance the water system operations. One such technological paradigm shift is the cyber-physical system (CPS). In this paper, we present the concept of the CPS in the water system context and investigate the CPS applications to water supply system monitoring. Also, the various applications of CPSs and the application domain requirements are outlined. More importantly, research studies on its application to water system monitoring are scrutinized. As such, key challenges sounding the applications in WSSs are identified. We then outline the areas of improvement for further studies.

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Section: Data Security and Privacymentioning

confidence: 99%

Cyber-Physical Systems for Water Supply Network Management: Basics, Challenges, and Roadmap

Adedeji

Hamam

2020

Sustainability

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“…In this section, we are going to discuss some of the previous works that applied machine learning in malware analysis. Machine learning has been successfully applied to the identification and detection of malware [9,10]. It was shown in [11] that machine learning can also be used to characterize malware families.…”

Section: Related Workmentioning

confidence: 99%

An Ensemble-Based Malware Detection Model Using Minimum Feature Set

Zelinka

Amer²

2019

mendel

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Current commercial antivirus detection engines still rely on signature-based methods. However, with the huge increase in the number of new malware, current detection methods become not suitable. In this paper, we introduce a malware detection model based on ensemble learning. The model is trained using the minimum number of signification features that are extracted from the file header. Evaluations show that the ensemble models slightly outperform individual classification models. Experimental evaluations show that our model can predict unseen malware with an accuracy rate of 0.998 and with a false positive rate of 0.002. The paper also includes a comparison between the performance of the proposed model and with different machine learning techniques. We are emphasizing the use of machine learning based approaches to replace conventional signature-based methods.

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“…Extensive research has been proposed and new research is still performed regarding the provision of solutions for malware detection systems [13,14]. For instance, in the case of known malware, content signatures-based methods that map samples of activities against known malware have been proposed [15,16].…”

Section: Related Workmentioning

confidence: 99%

Cross-Method-Based Analysis and Classification of Malicious Behavior by API Calls Extraction

Bruce¹,

Kim

Kang

et al. 2019

Applied Sciences

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Data-driven public security networking and computer systems are always under threat from malicious codes known as malware; therefore, a large amount of research and development is taking place to find effective countermeasures. These countermeasures are mainly based on dynamic and statistical analysis. Because of the obfuscation techniques used by the malware authors, security researchers and the anti-virus industry are facing a colossal issue regarding the extraction of hidden payloads within packed executable extraction. Based on this understanding, we first propose a method to de-obfuscate and unpack the malware samples. Additional, cross-method-based big data analysis to dynamically and statistically extract features from malware has been proposed. The Application Programming Interface (API) call sequences that reflect the malware behavior of its code have been used to detect behavior such as network traffic, modifying a file, writing to stderr or stdout, modifying a registry value, creating a process. Furthermore, we include a similarity analysis and machine learning algorithms to profile and classify malware behaviors. The experimental results of the proposed method show that malware detection accuracy is very useful to discover potential threats and can help the decision-maker to deploy appropriate countermeasures.

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Practical In-Depth Analysis of IDS Alerts for Tracing and Identifying Potential Attackers on Darknet

Cited by 9 publications

References 20 publications

Cyber-Physical Systems for Water Supply Network Management: Basics, Challenges, and Roadmap

Cyber-Physical Systems for Water Supply Network Management: Basics, Challenges, and Roadmap

An Ensemble-Based Malware Detection Model Using Minimum Feature Set

Cross-Method-Based Analysis and Classification of Malicious Behavior by API Calls Extraction

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