A Deception Model Robust to Eavesdropping Over Communication for Social Network Systems

Omolara, Abiodun Esther; Jantan, Aman; Abiodun, Oludare Isaac; Dada, Kemi Victoria; Arshad, Humaira; Etuh, Emmanuel

doi:10.1109/access.2019.2928359

Cited by 24 publications

(12 citation statements)

References 41 publications

(50 reference statements)

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“…Recently, convolutional neural networks (CNN) have wide applications in video and image recognition, processing of natural language [124], and recommender systems [125]. Also, CNN has a popular application in speech recognition and distant speech recognition that has produced a better result than the DNN.…”

Section: Cnn Models For Pattern Recognitionmentioning

confidence: 99%

Comprehensive Review of Artificial Neural Network Applications to Pattern Recognition

et al. 2019

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The era of artificial neural network (ANN) began with a simplified application in many fields and remarkable success in pattern recognition (PR) even in manufacturing industries. Although significant progress achieved and surveyed in addressing ANN application to PR challenges, nevertheless, some problems are yet to be resolved like whimsical orientation (the unknown path that cannot be accurately calculated due to its directional position). Other problem includes; object classification, location, scaling, neurons behavior analysis in hidden layers, rule, and template matching. Also, the lack of extant literature on the issues associated with ANN application to PR seems to slow down research focus and progress in the field. Hence, there is a need for state-of-the-art in neural networks application to PR to urgently address the abovehighlights problems for more successes. The study furnishes readers with a clearer understanding of the current, and new trend in ANN models that effectively addresses PR challenges to enable research focus and topics. Similarly, the comprehensive review reveals the diverse areas of the success of ANN models and their application to PR. In evaluating the performance of ANN models, some statistical indicators for measuring the performance of the ANN model in many studies were adopted. Such as the use of mean absolute percentage error (MAPE), mean absolute error (MAE), root mean squared error (RMSE), and variance of absolute percentage error (VAPE). The result shows that the current ANN models such as GAN, SAE, DBN, RBM, RNN, RBFN, PNN, CNN, SLP, MLP, MLNN, Reservoir computing, and Transformer models are performing excellently in their application to PR tasks. Therefore, the study recommends the research focus on current models and the development of new models concurrently for more successes in the field.INDEX TERMS Artificial neural networks, application to pattern recognition, feedforward neural networks, feedback neural networks, hybrid models.

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Section: Cnn Models For Pattern Recognitionmentioning

confidence: 99%

Comprehensive Review of Artificial Neural Network Applications to Pattern Recognition

et al. 2019

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“…The adversary will be confused and may be misdirected to reveal himself if the bogus plaintext is a credential honeytoken. To make the bogus plaintext in HE more deceptive, i.e., contextually correct and domain specific, natural language processing (NLP) based techniques [94] and deep learning (DL) based ones [95] have been used.…”

Section: B Evading the Honeypotmentioning

confidence: 99%

Three Decades of Deception Techniques in Active Cyber Defense -- Retrospect and Outlook

Zhang¹,

Thing²

2021

Preprint

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Deception techniques have been widely seen as a game changer in cyber defense. In this paper, we review representative techniques in honeypots, honeytokens, and moving target defense, spanning from the late 1980s to the year 2021. Techniques from these three domains complement with each other and may be leveraged to build a holistic deception based defense. However, to the best of our knowledge, there has not been a work that provides a systematic retrospect of these three domains all together and investigates their integrated usage for orchestrated deceptions. Our paper aims to fill this gap. By utilizing a tailored cyber kill chain model which can reflect the current threat landscape and a four-layer deception stack, a twodimensional taxonomy is developed, based on which the deception techniques are classified. The taxonomy literally answers which phases of a cyber attack campaign the techniques can disrupt and which layers of the deception stack they belong to. Cyber defenders may use the taxonomy as a reference to design an organized and comprehensive deception plan, or to prioritize deception efforts for a budget conscious solution. We also discuss two important points for achieving active and resilient cyber defense, namely deception in depth and deception lifecycle, where several notable proposals are illustrated. Finally, some outlooks on future research directions are presented, including dynamic integration of different deception techniques, quantified deception effects and deception operation cost, hardware-supported deception techniques, as well as techniques developed based on better understanding of the human element.

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“…36 Deception and decoy-based measures have been applied extensively in the cyber world for curtailing several economic problems. 37 For instance, honeypots are decoy systems intended to lure potential attackers away from critical systems and urging the attackers to remain attached to the system long enough for the system administrator to collect information about their malicious activities. 38 Decoy data —such as decoy documents or valid-looking but fake information generated on demand—are used for detecting and luring away unauthorized access to data.…”

Section: Deception and Decoy-based Systems And Applicationsmentioning

confidence: 99%

HoneyDetails: A prototype for ensuring patient’s information privacy and thwarting electronic health record threats based on decoys

Omolara¹,

Jantan

Abiodun

et al. 2020

Health Informatics J

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Advancements in electronic health record system allow patients to store and selectively share their medical records as needed with doctors. However, privacy concerns represent one of the major threats facing the electronic health record system. For instance, a cybercriminal may use a brute-force attack to authenticate into a patient’s account to steal the patient’s personal, medical or genetic details. This threat is amplified given that an individual’s genetic content is connected to their family, thus leading to security risks for their family members as well. Several cases of patient’s data theft have been reported where cybercriminals authenticated into the patient’s account, stole the patient’s medical data and assumed the identity of the patients. In some cases, the stolen data were used to access the patient’s accounts on other platforms and in other cases, to make fraudulent health insurance claims. Several measures have been suggested to address the security issues in electronic health record systems. Nevertheless, we emphasize that current measures proffer security in the short-term. This work studies the feasibility of using a decoy-based system named HoneyDetails in the security of the electronic health record system. HoneyDetails will serve fictitious medical data to the adversary during his hacking attempt to steal the patient’s data. However, the adversary will remain oblivious to the deceit due to the realistic structure of the data. Our findings indicate that the proposed system may serve as a potential measure for safeguarding against patient’s information theft.

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A Deception Model Robust to Eavesdropping Over Communication for Social Network Systems

Cited by 24 publications

References 41 publications

Comprehensive Review of Artificial Neural Network Applications to Pattern Recognition

Comprehensive Review of Artificial Neural Network Applications to Pattern Recognition

Three Decades of Deception Techniques in Active Cyber Defense -- Retrospect and Outlook

HoneyDetails: A prototype for ensuring patient’s information privacy and thwarting electronic health record threats based on decoys

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