Big Data in Intrusion Detection Systems and Intrusion Prevention Systems

Wang, Lidong

doi:10.12691/jcn-4-1-5

Cited by 24 publications

(9 citation statements)

References 18 publications

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“…Although UTM features have been discussed here, the issue is that it’s used for small security solutions. The Lidong Wang et al paper discussed intrusion, typically done by individuals outside the association [ 33 ]. The IDS/IPS intrusion detection and intrusion prevention system are discussed to deal with intrusion detection; however, IDS/IPS are not useful in all situations.…”

Section: Related Workmentioning

confidence: 99%

Achieving data privacy for decision support systems in times of massive data sharing

et al. 2022

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The world is suffering from a new pandemic of Covid-19 that is affecting human lives. The collection of records for Covid-19 patients is necessary to tackle that situation. The decision support systems (DSS) are used to gather that records. The researchers access the patient’s data through DSS and perform predictions on the severity and effect of the Covid-19 disease; in contrast, unauthorized users can also access the data for malicious purposes. For that reason, it is a challenging task to protect Covid-19 patient data. In this paper, we proposed a new technique for protecting Covid-19 patients’ data. The proposed model consists of two folds. Firstly, Blowfish encryption uses to encrypt the identity attributes. Secondly, it uses Pseudonymization to mask identity and quasi-attributes, then all the data links with one another, such as the encrypted, masked, sensitive, and non-sensitive attributes. In this way, the data becomes more secure from unauthorized access.

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Section: Related Workmentioning

confidence: 99%

Achieving data privacy for decision support systems in times of massive data sharing

et al. 2022

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“…The work done in can be consulted for further reading [23] as it compared the various approaches using features, advantages, and disadvantages of each approach. [5] proposed "an efficient hybrid system for anomaly detection in social networks".…”

Section: Mechanisms For Intrusion Detectionmentioning

confidence: 99%

Social Media Network Attacks and their Preventive Mechanisms: A Review

Etuh¹,

Bakpo²,

A.H³

2021

Computing Advances &Amp; Trends

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We live in a virtual world where actual lifestyles are replicated. The growing reliance on the use of social media networks worldwide has resulted in great concern for information security. One of the factors popularizing the social media platforms is how they connect people worldwide to interact, share content, and engage in mutual interactions of common interest that cut across geographical boundaries. Behind all these incredible gains are digital crime equivalence that threatens the physical socialization. Criminal minded elements and hackers are exploiting social media platforms (SMP) for many nefarious activities to harm others. As detection tools are developed to control these crimes so also hackers’ tactics and techniques are constantly evolving. Hackers are constantly developing new attacking tools and hacking strategies to gain malicious access to systems and attack social media network thereby making it difficult for security administrators and organizations to develop and implement the proper policies and procedures necessary to prevent the hackers’ attacks. The increase in cyber-attacks on the social media platforms calls for urgent and more intelligent security measures to enhance the effectiveness of social media platforms. This paper explores the mode and tactics of hackers’ mode of attacks on social media and ways of preventing their activities against users to ensure secure social cyberspace and enhance virtual socialization. Social media platforms are briefly categorized, the various types of attacks are also highlighted with current state-of-the-art preventive mechanisms to overcome the attacks as proposed in research works, finally, social media intrusion detection mechanism is suggested as a second line of defense to combat cybercrime on social media networks.

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“…Lack of knowledge of packet payload causes poor performance of anomaly-based detection on application level, while in signature-based, an intense and strong analysis has been done on the payload of packet to extract unique signatures. Signature-based approach provides a high accuracy on existing attacks [44].…”

Section: Additionalmentioning

confidence: 99%

Distributed Intrusion Detection Systems in Big Data: A Survey

Hameed

AlHabshy

ElDahshan

2021

Al-Azhar Bulletin of Science

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We live in a time where data stream by the second, which makes intrusion detection a more difficult and tiresome task, and in turn intrusion detection systems require an efficient and improved detection mechanism to detect the intrusive activities. Moreover, handling the size, complexity, and availability of big data requires techniques that can create beneficial knowledge from huge streams of the information, which imposes the challenges on the process of both designing and management of both Intrusion Detection System (IDS) and Intrusion Prevention System (IPS) in terms of performance, sustainability, security, reliability, privacy, energy consumption, fault tolerance, scalability, and flexibility. IDSs and IPSs utilize various methodologies to guarantee security, accessibility and reliability of enterprise computer networks. This paper presents a comprehensive study of the Distributed Intrusion Detection Systems in Big Data, and presents intrusion detection and prevention techniques that utilize machine learning, big data analytics techniques in distributed systems of the intrusion detection.

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Big Data in Intrusion Detection Systems and Intrusion Prevention Systems

Cited by 24 publications

References 18 publications

Achieving data privacy for decision support systems in times of massive data sharing

Achieving data privacy for decision support systems in times of massive data sharing

Social Media Network Attacks and their Preventive Mechanisms: A Review

Distributed Intrusion Detection Systems in Big Data: A Survey

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