Internet usage has grown exponentially, with individuals and companies performing multiple daily transactions in cyberspace rather than in the real world. The coronavirus (COVID-19) pandemic has accelerated this process. As a result of the widespread usage of the digital environment, traditional crimes have also shifted to the digital space. Emerging technologies such as cloud computing, the Internet of Things (IoT), social media, wireless communication, and cryptocurrencies are raising security concerns in cyberspace. Recently, cyber criminals have started to use cyber attacks as a service to automate attacks and leverage their impact. Attackers exploit vulnerabilities that exist in hardware, software, and communication layers. Various types of cyber attacks include distributed denial of service (DDoS), phishing, man-in-the-middle, password, remote, privilege escalation, and malware. Due to new-generation attacks and evasion techniques, traditional protection systems such as firewalls, intrusion detection systems, antivirus software, access control lists, etc., are no longer effective in detecting these sophisticated attacks. Therefore, there is an urgent need to find innovative and more feasible solutions to prevent cyber attacks. The paper first extensively explains the main reasons for cyber attacks. Then, it reviews the most recent attacks, attack patterns, and detection techniques. Thirdly, the article discusses contemporary technical and nontechnical solutions for recognizing attacks in advance. Using trending technologies such as machine learning, deep learning, cloud platforms, big data, and blockchain can be a promising solution for current and future cyber attacks. These technological solutions may assist in detecting malware, intrusion detection, spam identification, DNS attack classification, fraud detection, recognizing hidden channels, and distinguishing advanced persistent threats. However, some promising solutions, especially machine learning and deep learning, are not resistant to evasion techniques, which must be considered when proposing solutions against intelligent cyber attacks.

Given the continually rising frequency of cyberattacks, the adoption of artificial intelligence methods, particularly Machine Learning (ML), Deep Learning (DL), and Reinforcement Learning (RL), has become essential in the realm of cybersecurity. These techniques have proven to be effective in detecting and mitigating cyberattacks, which can cause significant harm to individuals, organizations, and even countries. Machine learning algorithms use statistical methods to identify patterns and anomalies in large datasets, enabling security analysts to detect previously unknown threats. Deep learning, a subfield of ML, has shown great potential in improving the accuracy and efficiency of cybersecurity systems, particularly in image and speech recognition. On the other hand, RL is again a subfield of machine learning that trains algorithms to learn through trial and error, making it particularly effective in dynamic environments. We also evaluated the usage of ChatGPT-like AI tools in cyber-related problem domains on both sides, positive and negative. This article provides an overview of how ML, DL, and RL are applied in cybersecurity, including their usage in malware detection, intrusion detection, vulnerability assessment, and other areas. The paper also specifies several research questions to provide a more comprehensive framework to investigate the efficiency of AI and ML models in the cybersecurity domain. The state-of-the-art studies using ML, DL, and RL models are evaluated in each Section based on the main idea, techniques, and important findings. It also discusses these techniques’ challenges and limitations, including data quality, interpretability, and adversarial attacks. Overall, the use of ML, DL, and RL in cybersecurity holds great promise for improving the effectiveness of security systems and enhancing our ability to protect against cyberattacks. Therefore, it is essential to continue developing and refining these techniques to address the ever-evolving nature of cyber threats. Besides, some promising solutions that rely on machine learning, deep learning, and reinforcement learning are susceptible to adversarial attacks, underscoring the importance of factoring in this vulnerability when devising countermeasures against sophisticated cyber threats. We also concluded that ChatGPT can be a valuable tool for cybersecurity, but it should be noted that ChatGPT-like tools can also be manipulated to threaten the integrity, confidentiality, and availability of data.

Unmanned aerial vehicles (UAVs) are used in many areas where their usage is increasing constantly. Their popularity, therefore, maintains its importance in the technology world. Parallel to the development of technology, human standards, and surroundings should also improve equally. This study is developed based on the possibility of timely delivery of urgent medical requests in emergency situations. Using UAVs for delivering urgent medical requests will be very effective due to their flexible maneuverability and low costs. However, off-the-shelf UAVs suffer from limited payload capacity and battery constraints. In addition, urgent requests may be requested at an uncertain time, and delivering in a short time may be crucial. To address this issue, we proposed a novel framework that considers the limitations of the UAVs and dynamically requested packages. These previously unknown packages have source–destination pairs and delivery time intervals. Furthermore, we utilize deep reinforcement learning (DRL) algorithms, deep Q-network (DQN), proximal policy optimization (PPO), and advantage actor–critic (A2C) to overcome this unknown environment and requests. The comprehensive experimental results demonstrate that the PPO algorithm has a faster and more stable training performance than the other DRL algorithms in two different environmental setups. Also, we implemented an extension version of a Brute-force (BF) algorithm, assuming that all requests and environments are known in advance. The PPO algorithm performs very close to the success rate of the BF algorithm.

The scientific publishing industry is dominated by a few publishers that use centralized systems, which decrease the quality of studies and make the publication process longer. Traditional publication systems generally have high publication costs, slow and biased review processes, copyrights held by publishers, lack of rewards for contributors, lack of connection among researchers, etc. Accordingly, we propose a decentralized blockchain-based scientific publication platform to eliminate the traditional publication system deficiencies. The proposed system uses Ethereum smart contracts to accelerate the publication process and abate the biased evaluation process while reducing the publication cost. The proposed model also improves the quality of scientific studies by adding new features to the publication process. The proposed system increases the number of publishers, makes the publication process fully traceable, and makes scientific papers globally available to anyone with a small fee. In addition, the system provides journals with decentralized models and integrates scientific papers with related data or datasets. The editors, reviewers, and cited authors are also rewarded. The proposed system has been implemented using Ethereum Virtual Machine (EVM), which consists of a front-end, middleware, and back-end. When an author submits a manuscript for evaluation, the system automatically finds the most appropriate editors and reviewers for related fields. After the publication process finishes, editors, reviewers, cited authors, and other contributors are rewarded as a system token-based cryptocurrency.

Malware (Malicious Software) is any software which performs malicious activities on computer-based systems without the user's consent. The number, severity, and complexity of malware have been increasing recently. The detection of malware becomes challenging because new malware variants are using obfuscation techniques to hide themselves from the malware detection systems. In this paper, a new behavioral-based malware detection method is proposed based on file-registry operations. When malware features are generated, only the operations which are performed on specific file and registry locations are considered. The file-registry operations divided into five groups: autostart file locations, temporary file locations, specific system file locations, autostart registry locations, and DLLs related registry locations. Based on the file-registry operations and where they performed, the malware features are generated. These features are seen in malware samples with high frequencies, while rarely seen in benign samples. The proposed method is tested on malware and benign samples in a virtual environment, and a dataset is created. Well-known machine learning algorithms including C4.5 (J48), RF (Random Forest), SLR (Simple Logistic Regression), AdaBoost (Adaptive Boosting), SMO (Sequential Minimal Optimization), and KNN (K-Nearest Neighbors) are used for classification. In the best case, we obtained 98.8% true positive rate, 0% false positive rate, 100% precision and 99.05% accuracy which is quite high when compared with leading methods in the literature.