Topic models provide a convenient way to analyze large of unclassified text. A topic contains a cluster of words that frequently occur together. A topic modeling can connect words with similar meanings and distinguish between uses of words with multiple meanings. This paper provides two categories that can be under the field of topic modeling. First one discusses the area of methods of topic modeling, which has four methods that can be considerable under this category. These methods are Latent semantic analysis (LSA), Probabilistic latent semantic analysis (PLSA), Latent Dirichlet allocation (LDA), and Correlated topic model (CTM). The second category is called topic evolution models, which model topics by considering an important factor time. In the second category, different models are discussed, such as topic over time (TOT), dynamic topic models (DTM), multiscale topic tomography, dynamic topic correlation detection, detecting topic evolution in scientific literature, etc.

Using the Internet of Things (IoT) for various applications, such as home and wearables devices, network applications, and even self-driven vehicles, detecting abnormal traffic is one of the problematic areas for researchers to protect network infrastructure from adversary activities. Several network systems suffer from drawbacks that allow intruders to use malicious traffic to obtain unauthorized access. Attacks such as Distributed Denial of Service attacks (DDoS), Denial of Service attacks (DoS), and Service Scans demand a unique automatic system capable of identifying traffic abnormality at the earliest stage to avoid system damage. Numerous automatic approaches can detect abnormal traffic. However, accuracy is not only the issue with current Intrusion Detection Systems (IDS), but the efficiency, flexibility, and scalability need to be enhanced to detect attack traffic from various IoT networks. Thus, this study concentrates on constructing an ensemble classifier using the proposed Integrated Evaluation Metrics (IEM) to determine the best performance of IDS models. The automated Ranking and Best Selection Method (RBSM) is performed using the proposed IEM to select the best model for the ensemble classifier to detect highly accurate attacks using machine learning and deep learning techniques. Three datasets of real IoT traffic were merged to extend the proposed approach’s ability to detect attack traffic from heterogeneous IoT networks. The results show that the performance of the proposed model achieved the highest accuracy of 99.45% and 97.81% for binary and multi-classification, respectively.

IoT devices enable a massive amount of data to be aggregated and analyzed for anomaly detection. The nature of heterogeneous devices introduces the challenge of collecting and handling these massive datasets to perform data analyses to discover cyber attacks in near real-time. However, the traditional IDS cannot deal with such a problem due to scalability limitations and insufficient storage and processing capabilities. Moreover, issues such as network bandwidth, real-time support, and security limit the power of cloud server-based IDSs. This paper presents an edge-cloud deep IDS model in Lambda architecture for IoT security to address these challenges. Our system decreases the training phase's time compared to traditional machine learning algorithms and increases the accuracy of true positive detected attacks. Furthermore, neural network layers lead deep learning to achieve better performance and flexibility compared to conventional machine learning. Our solution enables the detection of suspicious activities in real-time and allows to classify them by analyzing historical data in a batch process.

The Hidden Markov models (HMMs) are statistical models used in various communities and applications. Such applications include speech recognition, mental task classification, biological analysis, and anomaly detection. In hidden Markov models, there are two states: one is a hidden state and the other is an observation state. The purpose of this survey paper is to further the understanding of hidden Markov models, as well as the solutions to the three central problems: evaluation problem, decoding problem and learning problem. In addition, applying HMMs in real world applications such as security and engineering will improve the classification and accuracy for the whole field.

Mobile devices have evolved from simple devices, which are used for a phone call and SMS messages to smartphone devices that can run third party applications. Nowadays, malicious software, which is also known as malware, imposes a larger threat to these mobile devices. Recently, many news items were posted about the increase of the Android malware. There were a lot of Android applications pulled from the Android Market because they contained malware. The vulnerabilities of those Applications or Android operating systems are being exploited by the attackers who got the capability of penetrating into the mobile systems without user authorization causing compromise the confidentiality, integrity and availability of the applications and the user. This paper, it gave an update to the work done in the project. Moreover, this paper focuses on the Android Operating System and aim to detect existing Android malware. It has a dataset that contained 104 malware samples. This Paper chooses several malware from the dataset and attempting to analyze them to understand their installation methods and activation. In addition, it evaluates the most popular existing anti-virus software to see if these 104 malware could be detected.