Exploiting Radio Frequency Identification (RFID) technology in healthcare systems has become a common practice, as it ensures better patient care and safety. However, these systems are prone to security vulnerabilities that can jeopardize patient privacy and the secure management of patient credentials. This paper aims to advance state-of-the-art approaches by developing more secure and private RFID-based healthcare systems. More specifically, we propose a lightweight RFID protocol that safeguards patients’ privacy in the Internet of Healthcare Things (IoHT) domain by utilizing pseudonyms instead of real IDs, thereby ensuring secure communication between tags and readers. The proposed protocol has undergone rigorous testing and has been proven to be secure against various security attacks. This article provides a comprehensive overview of how RFID technology is used in healthcare systems and benchmarks the challenges faced by these systems. Then, it reviews the existing RFID authentication protocols proposed for IoT-based healthcare systems in terms of their strengths, challenges, and limitations. To overcome the limitations of existing approaches, we proposed a protocol that addresses the anonymity and traceability issues in existing schemes. Furthermore, we demonstrated that our proposed protocol had a lower computational cost than existing protocols and ensured better security. Finally, our proposed lightweight RFID protocol ensured strong security against known attacks and protected patient privacy using pseudonyms instead of real IDs.

In recent years, massive development in the malware industry changed the entire landscape for malware development. Therefore, cybercriminals became more sophisticated by advancing their development techniques from file-based to fileless malware. As file-based malware depends on files to spread itself, on the other hand, fileless malware does not require a traditional file system and uses benign processes to carry out its malicious intent. Therefore, it evades conventional detection techniques and remains stealthy. This paper briefly explains fileless malware, its life cycle, and its infection chain. Moreover, it proposes a detection technique based on feature analysis using machine learning for fileless malware detection. The virtual machine acquired the memory dumps upon executing the malicious and non-malicious samples. Then the necessary features are extracted using the Volatility memory forensics tool, which is then analyzed using machine learning classification algorithms. After that, the best algorithm is selected based on the k-fold cross-validation score. Experimental evaluation has shown that Random Forest outperforms other machine learning classifiers (Decision Tree, Support Vector Machine, Logistic Regression, K-Nearest Neighbor, XGBoost, and Gradient Boosting). It achieved an overall accuracy of 93.33% with a True Positive Rate (TPR) of 87.5% at zeroFalse Positive Rate (FPR) for fileless malware collected from five widely used datasets (VirusShare, AnyRun, PolySwarm, HatchingTriage, and JoESadbox).

Malware, short for malicious software, is any software program designed to cause harm to a computer or computer network. Malware can take many forms, such as viruses, worms, Trojan horses, and ransomware. Because malware can cause significant damage to a computer or network, it is important to avoid its installation to prevent any potential harm. This paper proposes a machine learning-based malware detection method called MalwD&C to allow the secure installation of Programmable Executable (PE) files. The proposed method uses machine learning classifiers to analyze the PE files and classify them as benign or malware. The proposed MalwD&C scheme was evaluated on a publicly available dataset by applying several machine learning classifiers in two settings: two-class classification (malware detection) and multi-class classification (malware categorization). The results showed that the Random Forest (RF) classifier outperformed all other chosen classifiers, achieving as high as 99.56% and 97.69% accuracies in the two-class and multi-class settings, respectively. We believe that MalwD&C will be widely accepted in academia and industry due to its speed in decision making and higher accuracy.

More than 2000 different cryptocurrencies are currently available in business and FinTech applications. Cryptocurrency is a digital payment system that does not rely on banks to verify their financial transactions and can enable anyone anywhere to send and receive their payments. Crypto mining attracts investors to mine and gets some coins as a reward for using the cryptocurrency. However, hackers can exploit the computing power without the explicit authorization of a user by launching a cryptojacking attack and then using it to mine cryptocurrency. The detection and protection of cryptojacking attacks are essential, and thus, miners are continuously working to find innovative ways to overcome this issue. This chapter provides an overview of the cryptojacking landscape. It offers recommendations to guide researchers and practitioners to overcome the identified challenges faced while realizing a mitigation strategy to combat cryptojacking malware attacks.

Trojan Detection—the process of understanding the behaviour of a suspicious file has been the talk of the town these days. Existing approaches, e.g., signature-based, have not been able to classify them accurately as Trojans. This paper proposes TrojanDetector—a simple yet effective multi-layer hybrid approach for Trojan detection. TrojanDetector analyses every downloaded application and extracts and correlates its features on three layers (i.e., application-, user-, and package layer) to identify it as either a benign application or a Trojan. TrojanDetector adopts a hybrid approach, combining static and dynamic analysis characteristics, for feature extraction from any downloaded application. We have evaluated our scheme on three publicly available datasets, namely (i) CCCS- CIC-AndMal-2020, (ii) Cantagio-Mobile, and (iii) Virus share, by using simple yet state-of-the-art classifiers, namely, random forest (RF), decision tree (DT), support vector machine (SVM), and logistic regression (LR) in binary—class settings. SVM outperformed its counterparts and attained the highest accuracy of 96.64%. Extensive experimentation shows the effectiveness of our proposed Trojan detection scheme.