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Detecting android malware by using fuzzy set-based weighting method and firefly optimization algorithm
Android OS is open-source and easy to use mobile operating system. It is also user-friendly with many other features. In this way, it is a very preferred operating system on mobile phones. As a result, it becomes the target of malicious people. Applications installed on the Android operating system from the Google Play Store or by third-party application providers, also known as Android package files, may contain malicious software. So far, a variety of analyzes and detections have been made to detect such malware. While detecting malware, good results have been obtained with various methods, but malicious people have developed methods of hiding themselves against these methods. We propose a new feature selection method based on Firefly Optimization Algorithm with the Fuzzy Set-Based weighting method. The proposed method performs better than traditional feature selection methods with fewer features. The experimental results of this study proved that Firefly Optimization is an acceptable optimization algorithm for feature selection to detect malware in terms of classification performance and classification runtime. In addition, experimental evaluation of TF-IDF and Fuzzy Set-Based weighting methods indicates the effectiveness of the Fuzzy Set-Based weighting with a full feature set.
Transmitting vital health data with coap observe
Internet of Things (IoT) is one of the most preferred technologies of recent times. Today, the use of IoT in daily life is increasing and it is expected to increase further over time. IoT devices can communicate automatically without the need for any user. It can receive data from other devices or generate data through the sensors on them. IoT can be used in smart city systems, home automation systems, traffic, and many other areas. One of the areas where the IoT system is used is the health sector. In healthcare technology, IoT systems play an important role in the automatic acquisition, monitoring, and processing of patients' health data. Elderly patients who do not want to be in a crowded environment or who need compulsory home care can expect to be monitored and treated remotely. As a solution to these situations, IoT systems can be considered to collect the health data of individuals and transmit them to a health institution. In this case, it is necessary to analyze the performance of network protocol stacks that can work with IoT systems in order to transmit this health data quickly and reliably. In this study, the health data of the patients were updated every second using the CoAP Observe method. The standard IoT protocol stack is used. 4 different metrics (latency, energy efficiency, reliability, and throughput) were determined and performance evaluation was made. A total of 12 scenarios were evaluated with 4 different scenarios and 3 different PDR (Packet Delivery Ratio) values (100, 95, 90) using MAC (CSMA, nullMAC) and RDC (contikiMAC, nullRDC) protocols.
Phishing website detection based on a novel artificial intelligence technique
Due to the increasing use of Internet, researchers widely study to prevent cybercrimes. One of the cybercrimes is phishing websites. As cybercriminals employ increasingly sophisticated tactics, there is a growing need for innovative approaches to detect and prevent these threats. In this thesis, the time-varying mirrored S-shaped transfer function is applied for BGWO, BPSO, and BHHO, and the proposed models are performed on the phishing websites dataset. The proposed models have promising results on the phishing websites dataset