IoT-KEEPER: Detecting Malicious IoT Network Activity Using Online Traffic Analysis at the Edge

Hafeez, Ibbad; Antikainen, Markku; Ding, Aaron Yi; Tarkoma, Sasu

doi:10.1109/tnsm.2020.2966951

Cited by 147 publications

(83 citation statements)

References 31 publications

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“…The ensemble scheme obtained 99% detection accuracy. IoT-keeper proposed an anomaly detection system based on fuzzy C-means clustering and fuzzy interpolation scheme and recurrent neural network [13]. The proposed technique achieved 98% accuracy having a 0.02 false alarm rate and for evaluation purpose, it uses the Kitsune dataset.…”

Section: A Organizationmentioning

confidence: 99%

A Hybrid DL-Based Detection Mechanism for Cyber Threats in Secure Networks

Qureshi

Tunio

et al. 2021

IEEE Access

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The astonishing growth of sophisticated ever-evolving cyber threats and attacks throws the entire Internet-of-Things (IoT) infrastructure into chaos. As the IoT belongs to the infrastructure of interconnected devices, it brings along significant security challenges. Cyber threat analysis is an augmentation of a network security infrastructure that primarily emphasizes on detection and prevention of sophisticated network-based threats and attacks. Moreover, it requires the security of network by investigation and classification of malicious activities. In this study, we propose a DL-enabled malware detection scheme using a hybrid technique based on the combination of a Deep Neural Network(DNN) and Long Short-Term Memory(LSTM) for the efficient identification of multi-class malware families in IoT infrastructure. The proposed scheme utilizes latest 2018 dataset named as N_BaIoT. Furthermore, our proposed scheme is evaluated using standard performance metrics such as accuracy, recall, precision, F1score, and so forth. The DL-based malware detection system achieves 99.96% detection accuracy for IoT based threats. Finally, we also compare our proposed work with other robust and state-of-the-art detection schemes.

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Section: A Organizationmentioning

confidence: 99%

A Hybrid DL-Based Detection Mechanism for Cyber Threats in Secure Networks

Qureshi

Tunio

et al. 2021

IEEE Access

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“…A security system focusing on the IoT network devices was proposed by Hafeez [1] to detect the malicious activity in the IoT network devices. The authors use the IoT keeper method to detect and analysis the malicious activity in IoT devices.…”

Section: Literature Surveymentioning

confidence: 99%

Malicious Traffic classification using Long Short-Term Memory (LSTM) model

Thapa

Duraipandian²

2021

Preprint

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Malicious traffic classification is the initial and primary step for any network-based security systems. This traffic classification systems include behavior-based anomaly detection system and Intrusion Detection System. Existing methods always relies on the conventional techniques and process the data in the fixed sequence, which may leads to performance issues. Furthermore, conventional techniques require proper annotation to process the volumetric data. Relying on the data annotation for efficient traffic classification may leads to network loops and bandwidth issues within the network. To address the above-mentioned issues, this paper presents a novel solution based on artificial intelligence perspective. The key idea of this paper is to propose a novel malicious classification system using Long Short-Term Memory (LSTM) model. To validate the efficiency of the proposed model, an experimental setup along with experimental validation is carried out. From the experimental results, it is proven that the proposed model is better in terms of accuracy, throughput when compared to the state-of-the-art models. Further, the accuracy of the proposed model outperforms the existing state of the art models with increase in 5% and overall 99.5% in accuracy.

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“…Traffic Analysis: Traffic analysis can achieve various applications, including detecting drones [31,35], inferring apps [36,38], monitoring misbehaving apps [42], enforcing network access restrictions [14], identifying actions on apps [11], and detecting hidden wireless cameras [10]. Unlike existing traffic analysis based approaches (e.g., [10,36,38,42]), which utilize the inherent traffic patterns (side-channel information leaks) to detect devices or apps which generate them, our work correlates the traffic pattern with human activities.…”

Section: Related Workmentioning

confidence: 99%

MotionCompass

Fang

et al. 2021

Proceedings of the 19th Annual International Conference on Mobile Systems, Applications, and Services

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Wireless security cameras are integral components of security systems used by military installations, corporations, and, due to their increased affordability, many private homes. These cameras commonly employ motion sensors to identify that something is occurring in their fields of vision before starting to record and notifying the property owner of the activity. In this paper, we discover that the motion sensing action can disclose the location of the camera through a novel wireless camera localization technique we call MotionCompass. In short, a user who aims to avoid surveillance can find a hidden camera by creating motion stimuli and sniffing wireless traffic for a response to that stimuli. With the motion trajectories within the motion detection zone, the exact location of the camera can be then computed. We develop an Android app to implement MotionCompass. Our extensive experiments using the developed app and 18 popular wireless security cameras demonstrate that for cameras with one motion sensor, MotionCompass can attain a mean localization error of around 5 cm with less than 140 seconds. This localization technique builds upon existing work that detects the existence of hidden cameras, to pinpoint their exact location and area of surveillance. CCS CONCEPTS• Security and privacy → Mobile and wireless security; Privacy protections.

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IoT-KEEPER: Detecting Malicious IoT Network Activity Using Online Traffic Analysis at the Edge

Cited by 147 publications

References 31 publications

A Hybrid DL-Based Detection Mechanism for Cyber Threats in Secure Networks

A Hybrid DL-Based Detection Mechanism for Cyber Threats in Secure Networks

Malicious Traffic classification using Long Short-Term Memory (LSTM) model

MotionCompass

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