Incremental Research on Cyber Security metrics in Android applications by implementing the ML algorithms in Malware Classification and Detection

Recent advances in Internet of Things (IoT) technologies require a new type of IoT security environment. Various heterogeneous smart devices have easy access to IoT environment, and as the number of users increases, they are exposed to various threats such as malicious attacks on IoT devices and IoT infrastructure, and data tampering by malicious code. Malware detection in IoT requires data and models for continuous and changing learning of smart devices. Methods/Statistical analysis: To minimize these security threats, various malware detection techniques in the field of IoT security have been studied. Malware detection in IoT environment is important for data derivation and learning model required for continuous and changing learning of smart devices. The metadata of malware detection can be normalized by the value of device id, time, behavior, location and state. This paper proposes behavior-based malware detection using deep learning (BMD-DL). Findings: BMD-DL was able to collect metadata about behavior-based malicious behavior and learn and detect malicious codes through deep learning. In addition, through the learned model, IoT Security is provided by disconnecting malicious devices that cause malicious behavior in the IoT environment. Improvements/Applications: BMD-DL collects behavioral data generated from multiple devices in the IoT and applies the results learned through deep learning to detect persistent malware.

GHGDroid: Global heterogeneous graph-based android malware detection

Android malware detection through hybrid features fusion and ensemble classifiers: The AndroPyTool framework and the OmniDroid dataset

The connection of physical devices, buildings, home appliances, vehicles, and other things embedded with electronics, sensors, software, and actuators that facilitate these objects in networking is called the Internet of Things (IoT). IoT has drastically changed our lives by way of automating jobs across almost all industries. The application and adoption of it in industrial machine utilization, industrial processes, and manufacturing is known as the Industrial Internet of Things (IIoT). This IIoT is coined Industry 4.0, and is a subclass of IoT that focuses on industrial applications such as machine control and maintenance, optimizing industrial robotic systems in manufacturing, and industrial central monitoring systems. Integrating the operations of manufacturing companies in Industry 4.0 means exposing the company’s IT infrastructure and data, which makes it more susceptible and vulnerable to cyber threats. Shielding IIoT against cyber threats is crucial in order to control and stimulate the economic engine of the future. Protecting cyber systems from thievery and adversaries is essential since it includes everything that is appropriate for securing sensitive data, personal information, cerebral property, industry, and governmental information systems. Cyber security refers to the umbrella of processes, technologies, and tools intended to protect networks, programs, devices, and data from various cyber attacks, damage, and unauthorized access. Cyber security ought to be an essential part of design, strategy, and operations, and needs to be focused on from the start for any new connected, Industry 4.0-driven approach. This chapter focuses on providing a clear understanding of cyber space, cyber systems, and the security challenges inherent to Industry 4.0.

The proliferation of the internet of things (IoT) has significantly enhanced the convenience and functionality of various applications ranging from personal devices to industrial systems. However, this expansion has also escalated the vulnerability of these networks to sophisticated malware attacks, posing a critical threat to the security and reliability of IoT systems. This chapter introduces an innovative defense framework based on the local outlier factor (LOF) technique for effective malware detection in IoT networks. The framework employs a systematic approach, starting from data collection and preprocessing to the application of LOF for anomaly detection. The research demonstrates through empirical analysis that the LOF-based method outperforms traditional malware detection techniques, offering higher precision, recall, and lower false positive rates. The comparative analysis with existing IoT malware detection methods such as Mobile-net IoT and Image-net IoT further validates the superiority of the LOF approach.

The number of malware in Android environment is increasing. As a result, the conventional detection algorithms that employ signature detection methods are facing challenges to cope with the huge number of attacks. In this respect, a supervised-based model that can enhance the accuracy and the depth of the malware detection and categorization process using a conversation-level feature is presented. The ensemble learning technique was employed in order to select the most useful features. A comparison between the methods provided in this research and the results of other studies that used the same dataset is given. The results show that Extra-trees classifier had achieved the highest weighted accuracy percentage among the other classifiers by 87.75% for malware detection and 79.97% for malware categorization. Finally, this study has achieved significant enhancement in malware categorization rate by 30.2% for precision and 31.14% recall in comparison with other studies that used the same dataset.

Cyber security mitigation is becoming an increasingly urgent aspect in today's digital era, in response to the threat of theft and disruption to information systems, software and hardware. The Covid-19 pandemic has exacerbated this situation by increasing the chances of success of cyberattacks, as the number and scope of such attacks increases. The impact of increasing levels of cyberattacks is the wider disruption to various sectors. This study aims to investigate cybersecurity issues that arose during the Covid-19 pandemic. Researchers analyzed the current conditions as well as cybercrime acts and various types of cyberattacks carried out during this period. In addition, this research also proposes a practical approach to mitigating the risk of cyber attacks that can be implemented by organizations. It is important for organizations to strengthen the protection of their data and critical assets by implementing a comprehensive cybersecurity approach. The results of this study present various techniques for detecting and avoiding cybercrime threats, even after the pandemic is over. By implementing this approach, damage caused by cyber attacks can be minimized, maintaining the integrity of an organization's data and systems. For future research, it is necessary to carry out further development in the field of cyber security by integrating the latest technologies such as artificial intelligence, blockchain, Internet of Things, and other technologies. In this way, efforts to mitigate cyber attacks can continue to be improved, maintaining the integrity and security of information systems in this ever-growing digital era. This research makes an important contribution to understanding and addressing cyber security issues during the pandemic and provides a foundation for further development in the future.

Android is the most widely used mobile operating system (OS). A large number of third-party Android application (app) markets have emerged. The absence of third-party market regulation has prompted research institutions to propose different malware detection techniques. However, due to improvements of malware itself and Android system, it is difficult to design a detection method that can efficiently and effectively detect malicious apps for a long time. Meanwhile, adopting more features will increase the complexity of the model and the computational cost of the system. Permissions play a vital role in the security of the Android apps. Term Frequency—Inverse Document Frequency (TF-IDF) is used to assess the importance of a word for a file set in a corpus. The static analysis method does not need to run the app. It can efficiently and accurately extract the permissions from an app. Based on this cognition and perspective, in this paper, a new static detection method based on TF-IDF and Machine Learning is proposed. The system permissions are extracted in Android application package’s (Apk’s) manifest file. TF-IDF algorithm is used to calculate the permission value (PV) of each permission and the sensitivity value of apk (SVOA) of each app. The SVOA and the number of the used permissions are learned and tested by machine learning. 6070 benign apps and 9419 malware are used to evaluate the proposed approach. The experiment results show that only use dangerous permissions or the number of used permissions can’t accurately distinguish whether an app is malicious or benign. For malware detection, the proposed approach achieve up to 99.5% accuracy and the learning and training time only needs 0.05s. For malware families detection, the accuracy is 99.6%. The results indicate that the method for unknown/new sample’s detection accuracy is 92.71%. Compared against other state-of-the-art approaches, the proposed approach is more effective by detecting malware and malware families.

As edge computing paradigm achieves great popularity in recent years, there remain some technical challenges that must be addressed to guarantee smart device security in Internet of Things (IoT) environment. Generally, smart devices transmit individual data across the IoT for various purposes nowadays, and it will cause losses and impose a huge threat to users since malware may steal and damage these data. To improve malware detection performance on IoT smart devices, we conduct a malware categorization analysis based on the Kaggle competition of Microsoft Malware Classification Challenge (BIG 2015) dataset in this article. Practically speaking, motivated by temporal convolutional network (TCN) structure, we propose a malware categorization scheme mainly using Word2Vec pre-trained model. Considering that the popular one-hot encoding converts input names from malicious files to high-dimensional vectors since each name is represented as one dimension in one-hot vector space, more compact vectors with fewer dimensions are obtained through the use of Word2Vec pre-training strategy, and then it can lead to fewer parameters and stronger malware feature representation. Moreover, compared with long short-term memory (LSTM), TCN demonstrates better performance with longer effective memory and faster training speed in sequence modeling tasks. The experimental comparisons on this malware dataset reveal better categorization performance with less memory usage and training time. Especially, through the performance comparison between our scheme and the state-of-the-art Word2Vec-based LSTM approach, our scheme shows approximately 1.3% higher predicted accuracy than the latter on this malware categorization task. Additionally, it also demonstrates that our scheme reduces about 90 thousand parameters and more than 1 hour on the model training time in this comparison.

As cyber attacks become more frequent at all levels, the commercial aviation industry is gearing up to respond accordingly. Commercial Aviation and Cyber Security: A Critical Intersection is a timely contribution to those responsible for keeping aircraft and infrastructure safe. It covers areas of vital interest such as aircraft communications, next-gen air transportation systems, the impact of the Internet of Things (IoT), regulations, the efforts being developed by the Federal Aviation Administration (FAA), and other regulatory bodies. The book also collects important information on the best practices already adopted by other industries such as utilities, defense and the National Highway Traffic Safety Administration in the US. It equally addresses risk management, response plans to cyber attacks, managing supply chains and their cyber- security flaws, personnel training, and the sharing of information among industry players. Commercial Aviation and Cyber Security: A Critical Intersection looks at possible future scenarios and how to respond to ever-growing cyber threats, how standards development will help combat this issue, listing the recommendations proposed by international agencies.

Now a days cyber crime growing and has a big effect everywhere globally. ethical hackers are normally involved in identifying flaws and recommending mitigation measures. the cyber safety international, there's a pressing need for the improvement of powerful techniques. Because of the effectiveness of machine learning in cyber security issues, machine learning for cyber security has recently become a hot topic. In cyber security, machine learning approaches have been utilized to handle important concerns such as intrusion detection, malware classification and detection, spam detection, and phishing detection. Although ML cannot fully automate a cyber-security system, it can identify cyber-security threats more efficiently than other software-oriented approaches, relieving security analysts of their burden. As a result, effective adaptive methods, such as machine learning techniques, can yield higher detection rates, lower false alarm rates, and cheaper computing and transmission costs. Our key goal is that the challenge of detecting attacks is fundamentally different from those of these other applications, making it substantially more difficult for the intrusion detection community to apply machine learning effectively. In this study, the CPS is modeled as a network of agents that move in unison with one another, with one agent acting as a leader and commanding the other agents. The proposed strategy in this study is to employ the structure of deep neural networks for the detection phase, which should tell the system of the attack's existence in the early stages of the attack. The use of robust control algorithms in the network to isolate the misbehaving agent in the leader-follower mechanism has been researched. Following the attack detection phase with a deep neural network, the control system uses the reputation algorithm to isolate the misbehaving agent in the presented control method. Experiment results show that deep learning algorithms can detect attacks more effectively than traditional methods, making cyber security simpler, more proactive, and less expensive and more expensive.

Due to the Internet of Things and the digitalization revolution, there is nowadays an overwhelming amount of cybersecurity information in the tech world. Dealing efficiently with cyber deviations and attacks is an emerging challenge to any cyber security industry all over the world. Traditional security solutions can no longer address today's security threats because of the rapid proliferation of various cyber-attacks and threats. Artificial intelligence understanding, especially machine learning technology can make a security system up to date by analyzing its security data so that the safety mechanism can be upgraded continuously and so that it can remain programmatic and automated. This study provides a comprehensive overview of existing machine-learning techniques inspired by the notion of AI. We explore its capabilities of advanced information analytics and management by deriving meaningful conclusions out of cyber data in Cybersecurity. The performance of the proposed method will be compared against that of a standard machine learning technique, Support Vector Machine (SVM), using cross-validation. This scheme is known as a Secured Learning based Data Evaluation Scheme (SLDES). Based on its analysis of past incidents and knowledge summaries from experts, the proposed AI approach SLDES (Shift Left Diverse Experiments and Simulations) would change or reform newly appearing sections as it retrieves recent events. Last but not least we present our research about the future of artificial intelligence in information security etc. Machine learning and its related approaches are being investigated and used in cyber defense, and the focus of the described section is to evaluate the current and future uses of machine learning in cyber defense, for the scheme proposed the section presented the way to demonstrate how to effect the proposed scheme is and the result metrics are plotted in histogram form To derive such insights and information necessary for designing countermeasures for such attacks, even as stated in, a thorough data analysis is important. The main aim of this research is to establish that clients can develop a safe situation to protect their electronic data from intruders.

This article will discuss the Internet of things, which is a completely new technology. Currently, the world's most talked about new electronics use Internet of Things technology. This technology includes Google, Yandex, YouTube and all our social networks. The current benefits of Internet of Things technology will be discussed, as well as its future position. What is the role of smart technologies in the development of robotics, new electronics and the impetus for new inventions? The Internet of Things is a new paradigm that transforms traditional lifestyles into high-tech lifestyles. Many important studies and researches have been conducted to improve the technology through IoT. The Internet of Things, or IoT, refers to the billions of physical devices around the world that are now connected to the internet, all collecting and sharing data. The main purpose of this review article is to provide a detailed discussion, both technologically and socially. It is not just about the connected devices but also about the hardware, software, connectivity and communication protocols, middleware and so much more to create Internet of Things solutions as mentioned. And it’s also about many processes and technologies. So, this is most important theme in our world. The article discusses various problems and key issues in IoT, architecture and important applications. The Internet of Things is an emerging paradigm that enables the communication between electronic devices and sensors through the internet in order to facilitate our lives. IoT has also demonstrated its importance and potential for economic and industrial growth in the developing region. It is also seen as a revolutionary step in the trading and stock markets. However, data and information security is an important concern and is highly desirable, which is a major challenge that needs to be addressed. The Internet, the biggest source of security threats and cyber attacks, has opened various doors for hackers, thereby discrediting information and data. IoT is committed to providing the best possible solutions to deal with data and information security issues. IoT's most important concern in trade and the economy is security. The IoT system consists of a large number of devices and sensors that communicate with each other. They can range from a Wi-Fi pet camera on your bookshelf to a medical device implanted in your body, like a pacemaker. As long as the device is able to connect to the internet and has sensors that transmit data, it can be considered an IoT device. As the IoT network grows and expands, the number of these sensors and devices is growing rapidly. These devices communicate with each other and transmit large amounts of information over the Internet. This information is very large and flows every second, and therefore deserves to be called great information. The IoT was initially most interesting to business and manufacturing, where its application is sometimes known as machine-to-machine (mean - M2M), but the emphasis is now on filling our homes and offices with smart devices, transforming it into something. Today, these IoT projects are evolving and rapidly spreading to all platforms. It is possible to find IoT projects for each topic. As mentioned earlier, IoT projects are designed to make life easier. These projects, created to help as many people as possible, will be the most important part of our future lives. It will be impossible to imagine our lives without the Internet of Things and smart projects. A smart city with sensors covering all its regions using diverse tangible gadgets and objects all over the community and connected with the help of internet. The continuous expansion of IoT-based networks poses complex challenges such as data management and collection, storage and processing, and analytics. This article sheds light on the existing literature and describes their contributions to various aspects of IoT. This article will help readers and researchers understand IoT and its application to the real world Keywords: arduino, sensors, security systems, robotics, intelligent technology.

In this era of technology and network, the battleship between security experts and malware developers is a never-ending fight as every day a new signatures malware comes into the battle to fight. To compete in this technological battle, AI-based technique would effectively combat and triumph. Various machine learning and deep learning techniques involved in the process of malware detection and classification with good accuracy are analyzed. ML-based techniques perform very well and were able to detect and classify even zero-day malware. A new hybrid methodology for the efficient detection and classification of malware is proposed. The accuracy of malware detection is improved by the proposed ensemble method.

This paper proposes a technique for automatically learning semantic malware signatures for Android from very few samples of a malware family.The key idea underlying our technique is to look for a maximally suspicious common subgraph (MSCS) that is shared between all known instances of a malware family.An MSCS describes the shared functionality between multiple Android applications in terms of inter-component call relations and their semantic metadata (e.g., data-flow properties).Our approach identifies such maximally suspicious common subgraphs by reducing the problem to maximum satisfiability.Once a semantic signature is learned, our approach uses a combination of static analysis and a new approximate signature matching algorithm to determine whether an Android application matches the semantic signature characterizing a given malware family.We have implemented our approach in a tool called ASTROID and show that it has a number of advantages over state-of-theart malware detection techniques.First, we compare the semantic malware signatures automatically synthesized by ASTROID with manually-written signatures used in previous work and show that the signatures learned by ASTROID perform better in terms of accuracy as well as precision.Second, we compare ASTROID against two state-of-the-art malware detection tools and demonstrate its advantages in terms of interpretability and accuracy.Finally, we demonstrate that ASTROID's approximate signature matching algorithm is resistant to behavioral obfuscation and that it can be used to detect zero-day malware.In particular, we were able to find 22 instances of zero-day malware in Google Play that are not reported as malware by existing tools.This paper aims to overcome these disadvantages of existing malware detectors by proposing a new technique to automatically infer malware signatures.By identifying malware based on inferred signatures, our approach retains all the advantages of signature-based approaches: it can pinpoint