Malware is a significant threat that has grown with the spread of technology. This makes detecting malware a critical issue. Static and dynamic methods are widely used in the detection of malware. However, traditional static and dynamic malware detection methods may fall short in advanced malware detection. Data obtained through memory analysis can provide important insights into the behavior and patterns of malware. This is because malwares leave various traces on memories. For this reason, the memory analysis method is one of the issues that should be studied in malware detection. In this study, the use of memory data in malware detection is suggested. Malware detection was carried out by using various deep learning and machine learning approaches in a big data environment with memory data. This study was carried out with Pyspark on Apache Spark big data platform in Google Colaboratory. Experiments were performed on the balanced CIC-MalMem-2022 dataset. Binary classification was made using Random Forest, Decision Tree, Gradient Boosted Tree, Logistic Regression, Naive Bayes, Linear Vector Support Machine, Multilayer Perceptron, Deep Feed Forward Neural Network, and Long Short-Term Memory algorithms. The performances of the algorithms used have been compared. The results were evaluated using the Accuracy, F1-score, Precision, Recall, and AUC performance metrics. As a result, the most successful malware detection was obtained with the Logistic Regression algorithm, with an accuracy level of 99.97% in malware detection by memory analysis. Gradient Boosted Tree follows the Logistic Regression algorithm with 99.94% accuracy. The Naive Bayes algorithm showed the lowest performance in malware analysis with memory data, with an accuracy of 98.41%. In addition, many of the algorithms used have achieved very successful results. According to the results obtained, the data obtained from memory analysis is very useful in detecting malware. In addition, deep learning and machine learning approaches were trained with memory datasets and achieved very successful results in malware detection.
Wireless Sensor Networks—WSNs, an important part of IoT—consist of sensor nodes with limited processing, memory capacities, and energy. Wireless Sensor Networks face many dangers as they are often distributed into untrusted regions. The accuracy of the data obtained in a WSN, where security threats cannot be prevented, is also questioned. In WSNs, the authentication of the resources and the data can be verified with the authentication mechanism. Authentication in WSNs allows the node to verify whether data have been sent from authorized sources and protects the original data from changes. However, there are some deficiencies in terms of security in existing authentication protocols such as ID spoofing attacks. In addition, blockchain, one of the emerging technologies, gives significant successful results in security applications. Cryptographically secured, immutable, non-repudiable, irrevocable, auditable, and verifiable can be given as security-related characteristics of the blockchain. This study aims to use these features of the blockchain in WSNs. In this study, a new blockchain-based authentication protocol was developed for WSNs. Based on the study’s system model, sensor nodes, cluster nodes, base station, and blockchain networks were created using a private blockchain, and users. A detailed security analysis was carried out for the study. At the same time, efficiency analysis was performed by implementing the proposed model on the WiSeN sensor node.
Wireless Sensor Networks(WSNs) are vulnerable to a variety of unique security risks and threats in their data collection and transmission processes. One of the most common attacks on WSNs that can target all layers of the protocol stack is the DoS attack. In this study, a unique DoS Intrusion Detection System (DDS) is proposed to detect DoS attacks specific to WSNs. The proposed system is an ensemble intrusion detection system called STLGBM-DDS, which is developed on Apache Spark big data platform in Google Colab environment, combining LightGBM machine learning algorithm, data balancing and feature selection processes. In order to reduce the effects of data imbalance on system performance, data imbalance processing consisting of Synthetic Minority Oversampling Technique (SMOTE) and Tomek-Links sampling methods called STL was used. In addition, Information Gain Ratio was used as a feature selection technique in the data preprocessing stage. The effects of both data balancing and feature selection stages on the detection performance of the system were investigated. The results obtained were evaluated using the Accuracy, F-Measure, Precision, Recall, ROC Curve and Precision-Recall Curve parameters. As a result, the proposed method achieved an overall accuracy of 99.95%. Also, it achieved 99.99%, 99.96%, 99.98%, 99.92%, 99.87% accuracy performance according to Normal, Grayhole, Blackhole, TDMA and Flooding classes, respectively. According to the results obtained, the proposed method has achieved very successful results in DoS attack detection in WSNs compared to current methods.
Derin öğrenme teknikleri özellikle 2000"li yılların başından bu yana yapay zeka alanının en önemli temsilcileri olarak bilinmektedir. Bu teknikler birçok farklı alanda yaygın bir biçimde kullanılıyor olsa da özellikle sağlık alanındaki başarılı performansları dikkatleri daha çok çekmektedir. Ancak bu tekniklerin geleneksel makine öğrenmesi tekniklerine göre çok daha fazla sayıda parametrelerle optimize ediliyor olması, çözüm süreçlerinin karmaşık olmasına ve insan taraflı algı düzeyine kapalı olmalarına sebep olmaktadır. Bu sebeple kara-kutu olarak da adlandırılan derin öğrenme tekniklerden oluşan zeki sistemleri insan gözünde güvenilir yapmak ve söz konusu sistemlerin sınırlılıklarını ya da hata yapma eğilimlerini anlayabilmek adına alternatif çalışmalar gerçekleştirilmeye başlanmıştır. Gelişmeler neticesinde açıklanabilir yapay zeka olarak adlandırılan bir alt-alanın doğmasına yol açan çözümler, derin öğrenme tekniklerinin sunduğu çözümlerin güvenli olup olmadığının anlaşılmasına olanak sağlamaktadır. Bu çalışmada, beyin tümörü tespiti için bir Evrişimsel Sinir Ağları (ESA) modeli kullanılmış ve modelin güvenlik düzeyi, Sınıf Aktivasyon Haritalama (SAH / CAM: Class Activation Mapping) destekli açıklanabilir bir modül üzerinden anlaşılabilmiştir. Geliştirilen ESA-SAH sistemi, hedef veri seti üzerindeki uygulama sürecinde ortalama %96,53 doğruluk, %96,10 duyarlılık ve %95,72 özgüllük sağlamıştır. Yine doktorların sistemdeki SAH görsellerine ve genel sistem performansına yönelik sundukları dönütler de ESA-SAH tabanlı çözümün pozitif yönde kabul edildiğini göstermiştir. Bu bulgular, ESA-SAH sisteminin tümör tespitinde güvenilir ve anlaşılır olduğunu ortaya koymaktadır.
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