Enterprise Credential Spear-phishing attack detection

Al-Hamar, Yuosuf; Kolivand, Hoshang; Tajdini, Mostafa; Saba, Tanzila; Ramachandran, Vasukidevi

doi:10.1016/j.compeleceng.2021.107363

Cited by 33 publications

(12 citation statements)

References 12 publications

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“…e technique will become more complex as a result, and more capital will be required. In addition, intrusion detection can be time and expense-consuming due to the many protocols involved [7]. Vigneswaran et al [8] developed an anomalybased IDS that functions in traditional networks and trains and tests the model using the KDDCup99 dataset.…”

Section: Related Workmentioning

confidence: 99%

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Securing the IoT System of Smart City against Cyber Threats Using Deep Learning

Saba

Khan

Sadad

et al. 2022

Discrete Dynamics in Nature and Society

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The idea of a smart city is to connect physical objects or things with sensors, software, electronics, and Internet connectivity for data communication through the Internet of Things (IoT) devices. IoT enhances productivity and efficacy intelligently using remote management, but the risk of security and privacy increases. Cyber threats are advancing day by day, causing insufficient measures of security and confidentiality. As the hackers use the Internet, several IoT vulnerabilities are introduced, demanding new security measures in the IoT devices of the smart city. The threads concerned with IoT need to be reduced for efficient Intrusion Detection Systems (IDSs). As a result, machine learning algorithms generate correct outputs from a large and complicated dataset. The output of machine learning could be used to detect anomalies in IoT-network systems. This paper employed several machine learning classifiers and a deep learning model for intrusion detection using seven datasets of the TON_IoT telemetry dataset. The proposed IDS achieved an accuracy of 99.7% using Thermostat, GPS Tracker, Garage Door, and Modbus datasets via voting classifier.

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Section: Related Workmentioning

confidence: 99%

“…It can only receive raw packets from the host router node, which contain con dential data. For genuine time-destroying behavior in domestic IoT gadgets, Anthi et al [7] proposed three-layer IDS architecture. e protection layers in this architecture de ne intrusion for IoT systems based on their normal or irregular behavior.…”

Section: Related Workmentioning

confidence: 99%

Securing the IoT System of Smart City against Cyber Threats Using Deep Learning

Saba

Khan

Sadad

et al. 2022

Discrete Dynamics in Nature and Society

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“…2019 Phishing Attacks Solution An anti-phishing 􀅫ilter should be applied to prevent access to vulnerable websites, even if the user accidentally clicks on an unknown URL [42].…”

Section: ) Phishing Attacksmentioning

confidence: 99%

A review on the impact of AI as big data in computer network environment

Ayub¹,

Mumtaz²,

Ahmad³

et al. 2021

J. adv. tec. eng. res.

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The rapid growth of Arti􀅫icial Intelligence (AI) with the evolution of network technology has improved and progressed to meet the needs of people. Big data place an important role in the 􀅫ield of AI due to enhanced utilization in computer networks. Many applications are used for big data-based AI in a computer network environment. The problems and the attacks (such as Ransomware, Phishing attacks, and AI attacks) occurring in Network Technology are widely associated with AI as big data along with the solutions such as detection and identi􀅫ication of given attacks and malicious activities. The computer network technology environment is used on a large scale and varies widely with the use of AI in the context of Big Data. However, more advanced technologies are applied with AI to ensure the security of Computer networks. Due to the vast amount of traf􀅫ic occurring in networks, the result leads to the occurrence of damage in the network, and intrusion detection is a process that proved to be the most essential to ensure the security provided to the system. The de􀅫ined attacks are speci􀅫ied as the main issues and will prevent vulnerable and malicious activities. The network requires a system to detect intrusions, which is further used to observe and identify the system and networks for anomalies and malicious intrusions as Intrusion Detection System (IDS), Firewall, Encryption, and Anti-spam technologies are used to identify any malicious activity and prevent it. For these attacks, Long Short Term Memory (LSTM) is used in deep learning, and many other technologies such as Machine learning and Data mining are associated with LSTM to detect intrusions. The computer networks with the application of big data as AI were made highly secure and ef􀅫icient systems by implementing the proposed LSTM method.

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“…Several technologies such as IDS, firewalls, and traffic shaping devices are available to secure a network. In addition, numerous attack modelling approaches are available to help organizations understand the nature of an assault [3][4][5]. One of the top concerns for businesses is to protect their networks from the external attacks.…”

Section: Introductionmentioning

confidence: 99%

Deep Learning for Intrusion Detection and Security of Internet of Things (IoT): Current Analysis, Challenges, and Possible Solutions

Khan

Kashif

Jhaveri

et al. 2022

Security and Communication Networks

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In the last decade, huge growth is recorded globally in computer networks and Internet of Things (IoT) networks due to the exponential data generation, approximately zettabyte to a petabyte. Consequently, security issues have also been arisen with the network growth. However, intrusion detection in such big data is challenging. Smart homes, cities, grids, devices, objects, e-commerce, e-banking, e-government, etc., are different advanced applications of the evolving networks. Many Intrusion Detection Systems (IDS) have been developed recently due to most computer networks’ exposure to security and privacy threats. Data confidentiality, integrity, and availability damage will occur in case of IDS prevention failure. Conventional techniques are not effective enough to cope the advanced attacks. Advanced deep learning techniques have been proposed for automatic intrusion detection and abnormal behavior identification of networks. This research aims to provide an inclusive analysis of intrusion detection based on deep learning techniques followed by different intrusion detection systems. In this review, public network-based datasets of IDS are fully explored and analyzed. Deep learning techniques for IDS have been critically evaluated based on different performance metrics (accuracy, precision, recall, f-1 score, false alarm rate, and detection rate). Furthermore, existing challenges and possible solutions for networks security and privacy have been discussed.

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Enterprise Credential Spear-phishing attack detection

Cited by 33 publications

References 12 publications

Securing the IoT System of Smart City against Cyber Threats Using Deep Learning

Securing the IoT System of Smart City against Cyber Threats Using Deep Learning

A review on the impact of AI as big data in computer network environment

Deep Learning for Intrusion Detection and Security of Internet of Things (IoT): Current Analysis, Challenges, and Possible Solutions

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