Showing papers in "Computers & Security in 2021"

TL;DR: The analysis shows how following what appeared to be large gaps between the initial outbreak of the pandemic in China and the first COVID-19 related cyber-attack, attacks steadily became much more prevalent to the point that on some days, three or four unique cyber-attacks were being reported.

TL;DR: Wang et al. as discussed by the authors proposed an effective intrusion detection framework based on SVM with naive Bayes feature embedding, which takes the data quality into consideration, which is essential for constructing a well-performed intrusion detection system beyond machine learning techniques.

TL;DR: A new group-agent strategy with trust computing is designed to ensure the reliability of edge devices during interactions and improve transmission efficiency and a stacked task sorting and ranking mechanism which improves resource allocation in each edge device is introduced.

TL;DR: In this article, the authors analyzed previously conducted attack and defense studies described in 151 papers from 2008 to 2019 for a systematic and comprehensive investigation of autonomous vehicles and classified autonomous attacks into the three categories of autonomous control system, autonomous driving systems components, and vehicle-to-everything communications.

TL;DR: The privacy issues related to the implementation of blockchain in IoT and present privacy preservation techniques to cope with the privacy issues are described and open research gaps are addressed for future work.

TL;DR: This paper surveys existing empirical performance evaluations of different permissioned blockchain platforms published between 2015 and 2019, using a comparative framework and concludes with a number of potential future research directions.

TL;DR: This paper proposes a novel approach for Android malware detection and familial classification based on the Graph Convolutional Network (GCN), and is the first study to explore the application of graph neural network in the field of malware classification.

TL;DR: In this article, a hybrid deep learning (HDL) network consisting of CNN and LSTM is used for a better intrusion detection system, and data imbalance processing consisting of Synthetic Minority Oversampling Technique (SMOTE) and Tomek-Links sampling methods called STL is used to reduce the effects of data imbalance on system performance.

TL;DR: Surveying on the state-of-the-art privacy-preserving techniques which can be employed in FL in a systematic fashion, as well as how these techniques mitigate data security and privacy risks.

TL;DR: A systematic review of the literature on ISA and a state-of-the-art collection of ISA methods and factors for enhancing employees’ ISA within both private and public sector organisations are put forward.

TL;DR: This research presents a practical approach for the integration of Blockchain with FL to provide privacy-preserving and secure big data analytics services and proposes utilizing fuzzy hashing to detect variations and anomalies in FL-trained models against poisoning attacks.

TL;DR: This paper proposes a method relying on application representation in terms on images used to input an explainable deep learning model designed by authors for Android malware detection and family identification, and demonstrates the effectiveness of the proposed method.

TL;DR: An enhanced Genetic Algorithm (GA)-based feature selection method, named as GA-based Feature Selection (GbFS), is contributed, to increase the classifiers’ accuracy in the domain of network security and intrusion detection.

TL;DR: This present study aimed to propose an anomaly-based Web attack detection architecture in a Web application using deep learning methods, and the proposed CNN deep learning architecture presented successful outcomes.

TL;DR: A research framework for zero-trust is developed to structure the identified literature and to highlight future research avenues, and economic analyses and user-related studies have been neglected by both academia and practice.

TL;DR: A comprehensive survey of WBAN technology is provided in this article, with a particular focus on the security and privacy concerns along with their countermeasures, followed by proposed research directions and open issues.

TL;DR: This paper proposes a rule-based approach towards generating AML attack samples and explores how they can be used to target a range of supervised machine learning classifiers used for detecting Denial of Service attacks in an IoT smart home network.

TL;DR: A systematic literature review shows the dimensions of risk assessment techniques today available for the surface, deep, and darknets areas and what website features should be used in order to identify a cyber threat or attack.

TL;DR: Wang et al. as discussed by the authors proposed a network intrusion detection system based on adaptive synthetic (ADASYN) oversampling technology and LightGBM, which can reduce the time complexity of the system while ensuring the accuracy of detection.

TL;DR: A novel Android malware detection scheme based on feature weighting with the joint optimization of weight-mapping and classifier parameters, called JOWMDroid is proposed, which outperforms four state-of-the-artfeature weighting methods and makes the weight-aware classifiers more competitive.

TL;DR: This work proposes a neural network "laundering" algorithm to remove black-box backdoor watermarks from neural networks even when the adversary has no prior knowledge of the structure of the watermark.

TL;DR: Wang et al. as mentioned in this paper designed an integrated deep intrusion detection model based on SDAE-ELM to overcome the long training time and low classification accuracy of existing deep neural network models, and to achieve timely response to intrusion behavior.

TL;DR: This paper covers the current trends and open challenges in IoHT device authentication mechanisms, such as the physically unclonable function (PUF) and blockchain-based techniques, and offers a comprehensive review of the IoHT or the Internet of Medical Things (IoMT).

TL;DR: In this paper, a wrapper-based feature selection method called "Tabu Search - Random Forest (TS-RF)" was proposed for Network Intrusion Detection Systems (NIDS) to reduce dimensionality of data.

TL;DR: Techniques that can be used to enhance the robustness of machine learning-based binary manipulation detectors in various adversarial scenarios are surveyed.

TL;DR: A phishing email classifier model that applies deep learning algorithms using a graph convolutional network (GCN) and natural language processing over an email body text to improve phishing detection accuracy is proposed.

TL;DR: This paper investigates the effectiveness of a new approach that uses malware visualization, for overcoming the problems related to the features selection and extraction, along with deep learning classification, whose performances are less sensitive to a small dataset than machine learning.

TL;DR: A DDoS attack detection system based on an improved Self-adaptive evolutionary extreme learning machine (SaE-ELM) that can adapt the best suitable crossover operator and automatically determine the appropriate number of hidden layer neurons is presented.

TL;DR: In this paper, the authors presented a novel approach to recognize malware by capturing the memory dump of suspicious processes which can be represented as a RGB image and applied a state-of-the-art manifold learning scheme named UMAP to improve the detection of unknown malware files through binary classification.

TL;DR: In this paper, the authors explored recent advances in ransomware prevention and detection and highlighted future research challenges and directions, and also carried out an analysis of a few popular ransomware samples and developed AESthetic, which was able to evade detection against eight popular antivirus programs.