Cyber Security Incident Handling, Warning and Response System for the European Critical Information Infrastructures (CyberSANE)

Mouratidis, Haralambos; Kalogeraki, Eleni Maria

doi:10.1007/978-3-030-20257-6_41

Cited by 12 publications

(7 citation statements)

References 17 publications

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“…As a result, to minimize further losses, businesses must create and implement a comprehensive cybersecurity strategy. The most recent socioeconomic studies show that [11] the nation's security is dependent on governments, people with access to data, applications and tools that require high security clearance.…”

Section: Introductionmentioning

confidence: 99%

Cybersecurity Threats and Their Mitigation Approaches Using Machine Learning—A Review

Ahsan

Nygard

Gomes

et al. 2022

JCP

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Machine learning is of rising importance in cybersecurity. The primary objective of applying machine learning in cybersecurity is to make the process of malware detection more actionable, scalable and effective than traditional approaches, which require human intervention. The cybersecurity domain involves machine learning challenges that require efficient methodical and theoretical handling. Several machine learning and statistical methods, such as deep learning, support vector machines and Bayesian classification, among others, have proven effective in mitigating cyber-attacks. The detection of hidden trends and insights from network data and building of a corresponding data-driven machine learning model to prevent these attacks is vital to design intelligent security systems. In this survey, the focus is on the machine learning techniques that have been implemented on cybersecurity data to make these systems secure. Existing cybersecurity threats and how machine learning techniques have been used to mitigate these threats have been discussed. The shortcomings of these state-of-the-art models and how attack patterns have evolved over the past decade have also been presented. Our goal is to assess how effective these machine learning techniques are against the ever-increasing threat of malware that plagues our online community.

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Section: Introductionmentioning

confidence: 99%

Cybersecurity Threats and Their Mitigation Approaches Using Machine Learning—A Review

Ahsan

Nygard

Gomes

et al. 2022

JCP

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show abstract

“…For example, social engineering or phishing ( Kushwaha, Buckchash & Raman, 2017 ), zero-day attack ( Jyothsna & Prasad, 2019 ), malware attack ( McIntosh et al, 2019 ), denial of service (DoS) ( Verma & Ranga, 2020 ), unauthorized access of confidential and valuable resources ( Saleh, Talaat & Labib, 2019 ). Additionally, according to the authors of Papastergiou, Mouratidis & Kalogeraki (2020) , a nation’s competitive edge in the global market and national security is currently driven by harnessing these efficient, productive, and highly secure leading-edge technologies with intelligent and dynamic means of timely detection and prevention of cyberattacks. Nevertheless, irrespective of the tireless efforts of security experts in defense mechanisms, hackers have always found ways to get away with targeted resources from valuable and most trusted sources worldwide by launching versatile, sophisticated, and automated cyber-attacks.…”

Section: Introductionmentioning

confidence: 99%

A novel hybrid-based approach of snort automatic rule generator and security event correlation (SARG-SEC)

Jaw

Wang

2022

PeerJ Computer Science

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The rapid advanced technological development alongside the Internet with its cutting-edge applications has positively impacted human society in many aspects. Nevertheless, it equally comes with the escalating privacy and critical cybersecurity concerns that can lead to catastrophic consequences, such as overwhelming the current network security frameworks. Consequently, both the industry and academia have been tirelessly harnessing various approaches to design, implement and deploy intrusion detection systems (IDSs) with event correlation frameworks to help mitigate some of these contemporary challenges. There are two common types of IDS: signature and anomaly-based IDS. Signature-based IDS, specifically, Snort works on the concepts of rules. However, the conventional way of creating Snort rules can be very costly and error-prone. Also, the massively generated alerts from heterogeneous anomaly-based IDSs is a significant research challenge yet to be addressed. Therefore, this paper proposed a novel Snort Automatic Rule Generator (SARG) that exploits the network packet contents to automatically generate efficient and reliable Snort rules with less human intervention. Furthermore, we evaluated the effectiveness and reliability of the generated Snort rules, which produced promising results. In addition, this paper proposed a novel Security Event Correlator (SEC) that effectively accepts raw events (alerts) without prior knowledge and produces a much more manageable set of alerts for easy analysis and interpretation. As a result, alleviating the massive false alarm rate (FAR) challenges of existing IDSs. Lastly, we have performed a series of experiments to test the proposed systems. It is evident from the experimental results that SARG-SEC has demonstrated impressive performance and could significantly mitigate the existing challenges of dealing with the vast generated alerts and the labor-intensive creation of Snort rules.

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“…As a consequence, companies should implement and create a robust cybersecurity strategy to prevent such losses. According to a recent sociological study, the nation's future is contingent on the government's public with information access programs and high-security equipment [11], 0 billion per year [9]. The intelligence community anticipates that the number of records broken will triple over the following 5 years [10].…”

Section: Introductionmentioning

confidence: 99%

“…As an outcome, businesses should implement and create an effective security strategy to prevent such losses. The nation's upcoming is dependent on the government, persons with the right to use high-security equipment, and knowledge programmes, according to a recent sociological study [11]. Additionally, it depends on companies granting access to their staff members, who are capable of and knowledgeable about recognizing such cyber threats immediately and efficiently.…”

Section: Introductionmentioning

confidence: 99%

A Novel Ensemble Model Using Learning Classifiers to Enhance Malware Detection for Cyber Security Systems

Das

Pramod

2022

IJRITCC

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In the Internet of Things arena, smart gadgets are employed to offer quick and dependable access to services. IoT technology has the ability to recognize extensive information, provide information reliably, and process that information intelligently. Data networks, controllers, and sensors are increasingly used in industrial systems nowadays. Attacks have increased as a result of the growth in connected systems and the technologies they employ. These attacks may interrupt international business and result in significant financial losses. Utilizing a variety of methods, including deep learning (DL) and machine learning (ML), cyber assaults have been discovered. In this research, we provide an ensemble staking approach to efficiently and quickly detect cyber-attacks in the IoT. The NSL, credit card, and UNSW information bases were the three separate datasets used for the experiments. The suggested novel combinations of ensemble classifiers are done better than the other individual classifiers from the base model. Additionally, based on the test outcomes, it could be concluded that all tree and bagging-based combinations performed admirably and that, especially when their corresponding hyperparameters are set properly, differences in performance across methods are not significant statistically. Additionally, compared to other comparable PE (Portable Executable) malware detectors that were published recently, the suggested tree-based ensemble approaches outperformed them.

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Cyber Security Incident Handling, Warning and Response System for the European Critical Information Infrastructures (CyberSANE)

Cited by 12 publications

References 17 publications

Cybersecurity Threats and Their Mitigation Approaches Using Machine Learning—A Review

Cybersecurity Threats and Their Mitigation Approaches Using Machine Learning—A Review

A novel hybrid-based approach of snort automatic rule generator and security event correlation (SARG-SEC)

A Novel Ensemble Model Using Learning Classifiers to Enhance Malware Detection for Cyber Security Systems

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