The astonishing growth of sophisticated ever-evolving cyber threats and attacks throws the entire Internet-of-Things (IoT) infrastructure into chaos. As the IoT belongs to the infrastructure of interconnected devices, it brings along significant security challenges. Cyber threat analysis is an augmentation of a network security infrastructure that primarily emphasizes on detection and prevention of sophisticated network-based threats and attacks. Moreover, it requires the security of network by investigation and classification of malicious activities. In this study, we propose a DL-enabled malware detection scheme using a hybrid technique based on the combination of a Deep Neural Network(DNN) and Long Short-Term Memory(LSTM) for the efficient identification of multi-class malware families in IoT infrastructure. The proposed scheme utilizes latest 2018 dataset named as N_BaIoT. Furthermore, our proposed scheme is evaluated using standard performance metrics such as accuracy, recall, precision, F1-score, and so forth. The DL-based malware detection system achieves 99.96% detection accuracy for IoT based threats. Finally, we also compare our proposed work with other robust and state-of-the-art detection schemes.

Network forensics can be an expansion associated with network security design which typically emphasizes avoidance and detection of community assaults. It covers the necessity for dedicated investigative abilities. When you look at the design, this indeed currently allows investigating harmful behavior in communities. It will help organizations to examine external and community this is undoubtedly around. It is also important for police force investigations. Network forensic techniques can be used to identify the source of the intrusion and the intruder’s location. Forensics can resolve many cybercrime cases using the methods of network forensics. These methods can extract intruder’s information, the nature of the intrusion, and how it can be prevented in the future. These techniques can also be used to avoid attacks in near future. Modern network forensic techniques face several challenges that must be resolved to improve the forensic methods. Some of the key challenges include high storage speed, the requirement of ample storage space, data integrity, data privacy, access to IP address, and location of data extraction. The details concerning these challenges are provided with potential solutions to these challenges. In general, the network forensic tools and techniques cannot be improved without addressing these challenges of the forensic network. This paper proposed a thematic taxonomy of classifications of network forensic techniques based on extensive. The classification has been carried out based on the target datasets and implementation techniques while performing forensic investigations. For this purpose, qualitative methods have been used to develop thematic taxonomy. The distinct objectives of this study include accessibility to the network infrastructure and artifacts and collection of evidence against the intruder using network forensic techniques to communicate the information related to network attacks with minimum false-negative results. It will help organizations to investigate external and internal causes of network security attacks.

Evidence shows that computer system users, small companies, and multinational corporations require Network Forensics Analysis to prevent attacks that may compromise their data. To this purpose, logs automatically generated by computer systems can be analyzed to identify, control, and properly fight different attacks. However, large amounts of data make it difficult to accurately analyze certain behaviors that may be considered risky to computer systems. This paper focuses on developing and training a Machine Learning (ML) Decision Tree Model to predict potential malicious attacks originated from specific networks. The KDD Cup dataset, which includes a wide variety of network intrusions simulated in a military network environment, was used. Dataset was analyzed and subsequently used to train, test, correct, and adjust the proposed model. The used dataset also showed a high risk at the connection ends. The proposed model was coded in Python to detect malicious connections and successfully achieve 99% accuracy. The subsequent development of a variant model using Principal Component Analysis showed its effectiveness and robustness by reducing its complexity. This paper also offers the possibility of developing tools to detect attacks and potential threats to security systems automatically, thus suggesting computer administrators use Network Forensics Analysis, data mining, and machine learning to provide security to their computer system.