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The recent digital revolution led robots to become integrated more than ever into different domains such as agricultural, medical, industrial, military, police (law enforcement), and logistics. Robots are devoted to serve, facilitate, and enhance the human life. However, many incidents have been occurring, leading to serious injuries and devastating impacts such as the unnecessary loss of human lives. Unintended accidents will always take place, but the ones caused by malicious attacks represent a very challenging issue. This includes maliciously hijacking and controlling robots and causing serious economic and financial losses. This paper reviews the main security vulnerabilities, threats, risks, and their impacts, and the main security attacks within the robotics domain. In this context, different approaches and recommendations are presented in order to enhance and improve the security level of robotic systems such as multi-factor device/user authentication schemes, in addition to multi-factor cryptographic algorithms. We also review the recently presented security solutions for robotic systems.

With the recent increase in internet usage, the number of important, sensitive, confidential individual and corporate data passing through internet has increasingly grown. With gaps in the security systems, attackers have attempted to intrude the network, thereby gaining access to essential and confidential information, which may cause harm to the operation of the systems, and also affect the confidentiality of the data. To counter these possible attacks, intrusion detection systems (IDSs), which is an essential branch of cybersecurity, were employed to monitor and analyze network traffic thereby detects and reports malicious activities. A large number of review papers have covered different approaches for intrusion detection in networks, most of which follow a non-systematic approach, merely made a comparison of the existing techniques without reflecting an in-depth analytical synthesis of the methodologies and performances of the approaches to give a complete understanding of the state of IDS. Nonetheless, many of these reviews investigated more about the anomaly-based IDS with more emphasis on deep-learning models, while signature, hybrid-based (signature + anomaly-based) have received minimal focus. Hence, by adhering to the principles of Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA), this work reviewed existing contributions on anomaly-, signature-, and hybrid-based approaches to provide a comprehensive overview of network IDS's state of the art. The articles were retrieved from seven databases (ScienceDirect, SpringerNature, IEEE, MDPI, Hindawi, PeerJ, and Taylor & Francis) which cut across various reputable journals and conference Proceedings. Among the 776 pieces of the literature identified, 71 were selected for analysis and synthesis to answer the research questions. Based on the research findings, we identified unexplored study areas and unresolved research challenges. In order to create a better IDS model, we conclude by presenting promising, high-impact future research areas.

A lot of malicious applications appears every day, threatening numerous users. Therefore, a surge of studies have been conducted to protect users from newly emerging malware by using machine learning algorithms. Albeit existing machine or deep learning-based Android malware detection approaches achieve high accuracy by using a combination of multiple features, it is not possible to employ them on our mobile devices due to the high cost for using them. In this paper, we propose MAPAS, a malware detection system, that achieves high accuracy and adaptable usages of computing resources. MAPAS analyzes behaviors of malicious applications based on API call graphs of them by using convolution neural networks (CNN). However, MAPAS does not use a classifier model generated by CNN, it only utilizes CNN for discovering common features of API call graphs of malware. For efficiently detecting malware, MAPAS employs a lightweight classifier that calculates a similarity between API call graphs used for malicious activities and API call graphs of applications that are going to be classified. To demonstrate the effectiveness and efficiency of MAPAS, we implement a prototype and thoroughly evaluate it. And, we compare MAPAS with a state-of-the-art Android malware detection approach, MaMaDroid. Our evaluation results demonstrate that MAPAS can classify applications 145.8% faster and uses memory around ten times lower than MaMaDroid. Also, MAPAS achieves higher accuracy (91.27%) than MaMaDroid (84.99%) for detecting unknown malware. In addition, MAPAS can generally detect any type of malware with high accuracy.

This paper successfully tackles the problem of processing a vast amount of security related data for the task of network intrusion detection. It employs Apache Spark, as a big data processing tool, for processing a large size of network traffic data. Also, we propose a hybrid scheme that combines the advantages of deep network and machine learning methods. Initially, stacked autoencoder network is used for latent feature extraction, which is followed by several classification-based intrusion detection methods, such as support vector machine, random forest, decision trees, and naive Bayes which are used for fast and efficient detection of intrusion in massive network traffic data. A real time UNB ISCX 2012 dataset is used to validate our proposed method and the performance is evaluated in terms of accuracy, f-measure, sensitivity, precision and time.

In the domain of cyber-physical systems, wireless sensor networks (WSNs) play a pivotal role as infrastructures, encompassing both stationary and mobile sensors. These sensors self-organize and establish multi-hop connections for communication, collectively sensing, gathering, processing, and transmitting data about their surroundings. Despite their significance, WSNs face rapid and detrimental attacks that can disrupt functionality. Existing intrusion detection methods for WSNs encounter challenges such as low detection rates, computational overhead, and false alarms. These issues stem from sensor node resource constraints, data redundancy, and high correlation within the network. To address these challenges, we propose an innovative intrusion detection approach that integrates machine learning (ML) techniques with the Synthetic Minority Oversampling Technique Tomek Link (SMOTE-TomekLink) algorithm. This blend synthesizes minority instances and eliminates Tomek links, resulting in a balanced dataset that significantly enhances detection accuracy in WSNs. Additionally, we incorporate feature scaling through standardization to render input features consistent and scalable, facilitating more precise training and detection. To counteract imbalanced WSN datasets, we employ the SMOTE-Tomek resampling technique, mitigating overfitting and underfitting issues. Our comprehensive evaluation, using the wireless sensor network dataset (WSN-DS) containing 374,661 records, identifies the optimal model for intrusion detection in WSNs. The standout outcome of our research is the remarkable performance of our model. In binary classification scenarios, it achieves an accuracy rate of 99.78%, and in multiclass classification scenarios, it attains an exceptional accuracy rate of 99.92%. These findings underscore the efficiency and superiority of our proposal in the context of WSN intrusion detection, showcasing its effectiveness in detecting and mitigating intrusions in WSNs.

The Internet of Things (IoT) has garnered considerable attention from academic and industrial circles as a pivotal technology in recent years. The escalation of security risks is observed to be associated with the growing interest in IoT applications. Intrusion detection systems (IDS) have been devised as viable instruments for identifying and averting malicious actions in this context. Several techniques described in academic papers are thought to be very accurate, but they cannot be used in the real world because the datasets used to build and test the models do not accurately reflect and simulate the IoT network. Existing methods, on the other hand, deal with these issues, but they are not good enough for commercial use because of their lack of precision, low detection rate, receiver operating characteristic (ROC), and false acceptance rate (FAR). The effectiveness of these solutions is predominantly dependent on individual learners and is consequently influenced by the inherent limitations of each learning algorithm. This study introduces a new approach for detecting intrusion attacks in an IoT network, which involves the use of an ensemble learning technique based on gray wolf optimizer (GWO). The novelty of this study lies in the proposed voting gray wolf optimizer (GWO) ensemble model, which incorporates two crucial components: a traffic analyzer and a classification phase engine. The model employs a voting technique to combine the probability averages of the base learners. Secondly, the combination of feature selection and feature extraction techniques is to reduce dimensionality. Thirdly, the utilization of GWO is employed to optimize the parameters of ensemble models. Similarly, the approach employs the most authentic intrusion detection datasets that are accessible and amalgamates multiple learners to generate ensemble learners. The hybridization of information gain (IG) and principal component analysis (PCA) was employed to reduce dimensionality. The study utilized a novel GWO ensemble learning approach that incorporated a decision tree, random forest, K-nearest neighbor, and multilayer perceptron for classification. To evaluate the efficacy of the proposed model, two authentic datasets, namely, BoT-IoT and UNSW-NB15, were scrutinized. The GWO-optimized ensemble model demonstrates superior accuracy when compared to other machine learning-based and deep learning models. Specifically, the model achieves an accuracy rate of 99.98%, a DR of 99.97%, a precision rate of 99.94%, an ROC rate of 99.99%, and an FAR rate of 1.30 on the BoT-IoT dataset. According to the experimental results, the proposed ensemble model optimized by GWO achieved an accuracy of 100%, a DR of 99.9%, a precision of 99.59%, an ROC of 99.40%, and an FAR of 1.5 when tested on the UNSW-NB15 dataset.

Security issues in text-based password authentication are rarely caused by technical issues, but rather by the limitations of human memory, and human perceptions together with their consequential responses. This study introduces a new user-friendly guideline approach to password creation, including persuasive messages that motivate and influence users to select more secure and memorable text passwords without overburdening their memory. From a broad understanding of human factors-caused security problems, we offer a reliable solution by encouraging users to create their own formula to compose passwords. A study has been conducted to evaluate the efficiency of the proposed password guidelines. Its results suggest that the password creation methods and persuasive message provided to users convinced them to create cryptographically strong and memorable passwords. Participants were divided into two groups in the study. The participants in the experimental group who were given several password creation methods along with a persuasive message created more secure and memorable passwords than the participants in the control group who were asked to comply with the usual strict password creation rules. The study also suggests that our password creation methods are much more efficient than strict password policy rules. The security and usability evaluation of the proposed password guideline showed that simple improvements such as adding persuasive text to the usual password guidelines consisting of several password restriction rules make significant changes to the strength and memorability of passwords. The proposed password guidelines are a low-cost solution to the problem of improving the security and usability of text-based passwords.

Machine learning (ML) models have proved efficient in classifying data samples into their respective categories. The standard ML evaluation methodology assumes that test data samples are derived from pre-observed classes used in the training phase. However, in applications such as Network Intrusion Detection Systems (NIDSs), obtaining data samples of all attack classes to be observed is challenging. ML-based NIDSs face new attack traffic known as zero-day attacks that are not used in training due to their non-existence at the time. Therefore, this paper proposes a novel zero-shot learning methodology to evaluate the performance of ML-based NIDSs in recognising zero-day attack scenarios. In the attribute learning stage, the learning models map network data features to semantic attributes that distinguish between known attacks and benign behaviour. In the inference stage, the models construct the relationships between known and zero-day attacks to detect them as malicious. A new evaluation metric is defined as Zero-day Detection Rate (Z-DR) to measure the effectiveness of the learning model in detecting unknown attacks. The proposed framework is evaluated using two key ML models and two modern NIDS data sets. The results demonstrate that for certain zero-day attack groups discovered in this paper, ML-based NIDSs are ineffective in detecting them as malicious. Further analysis shows that attacks with a low Z-DR have a significantly distinct feature distribution and a higher Wasserstein Distance range than the other attack classes.

Information and communication technology has evolved dramatically, and now the majority of people are using internet and sharing their opinion more openly, which has led to the creation, collection and circulation of hate speech over multiple platforms. The anonymity and movability given by these social media platforms allow people to hide themselves behind a screen and spread the hate effortlessly. Online hate speech (OHS) recognition can play a vital role in stopping such activities and can thus restore the position of public platforms as the open marketplace of ideas. To study hate speech detection in social media, we surveyed the related available datasets on the web-based platform. We further analyzed approximately 200 research papers indexed in the different journals from 2010 to 2022. The papers were divided into various sections and approaches used in OHS detection, i.e., feature selection, traditional machine learning (ML) and deep learning (DL). Based on the selected 111 papers, we found that 44 articles used traditional ML and 35 used DL-based approaches. We concluded that most authors used SVM, Naive Bayes, Decision Tree in ML and CNN, LSTM in the DL approach. This survey contributes by providing a systematic approach to help researchers identify a new research direction in online hate speech.

Software-Defined Networking (SDN) is a contemporary network strategy utilized instead of a traditional network structure. It provides significantly more administrative efficiency and ease than traditional networks. However, the centralized control used in SDN entails an elevated risk of single-point failure that is more susceptible to different kinds of network assaults like Distributed Denial of Service (DDoS), DoS, spoofing, and API exploitation which are very complex to identify and mitigate. Thus, a powerful intrusion detection system (IDS) based on deep learning is created in this study for the detection and mitigation of network intrusions. This system contains several stages and begins with the data augmentation method named Deep Convolutional Generative Adversarial Networks (DCGAN) to over the data imbalance problem. Then, the features are extracted from the input data using a CenterNet-based approach. After extracting effective characteristics, ResNet152V2 with Slime Mold Algorithm (SMA) based deep learning is implemented to categorize the assaults in InSDN and Edge IIoT datasets. Once the network intrusion is detected, the proposed defense module is activated to restore regular network connectivity quickly. Finally, several experiments are carried out to validate the algorithm's robustness, and the outcomes reveal that the proposed system can successfully detect and mitigate network intrusions.