Malware detection constitutes a fundamental step in safe and secure computational systems, including industrial systems and the Internet of Things (IoT). Modern malware detection is based on machine learning methods that classify software samples as malware or benign, based on features that are extracted from the samples through static and/or dynamic analysis. State-of-the-art malware detection systems employ Deep Neural Networks (DNNs) whose accuracy increases as more data are analyzed and exploited. However, organizations also have significant privacy constraints and concerns which limit the data that they share with centralized security providers or other organizations, despite the malware detection accuracy improvements that can be achieved with the aggregated data. In this paper we investigate the effectiveness of federated learning (FL) methods for developing and distributing aggregated DNNs among autonomous interconnected organizations. We analyze a solution where multiple organizations use independent malware analysis platforms as part of their Security Operations Centers (SOCs) and train their own local DNN model on their own private data. Exploiting cross-silo FL, we combine these DNNs into a global one which is then distributed to all organizations, achieving the distribution of combined malware detection models using data from multiple sources without sample or feature sharing. We evaluate the approach using the EMBER benchmark dataset and demonstrate that our approach effectively reaches the same accuracy as the non-federated centralized DNN model, which is above 93\%.

Malwares have been being a major security threats to enterprises, government organizations and end-users. Beside traditional malwares, such as viruses, worms and trojans, new types of malwares, such as botnets, ransomwares, IoT malwares and crypto-jackings are released daily. To cope with malware threats, several measures for monitoring, detecting and preventing malwares have been developed and deployed in practice, such as signature-based detection, static and dynamic file analysis. This paper proposes 2 malware detection models based on statistics and machine learning using opcode n-grams. The proposed models aim at achieving high detection accuracy as well as reducing the amount of time for training and detection. Experimental results show that our proposed models give better performance measures than previous proposals. Specifically, the proposed statistics-based model is very fast and it achieves a high detection accuracy of 92.75\% and the random forest-based model produces the highest detection accuracy of 96.29\%.

The term Internet of Things(IoT) describes a network of real-world items, gadgets, structures, and other things that are equipped with communication and sensors for gathering and exchanging data online. The likelihood of Android malware attacks on IoT devices has risen due to their widespread use. Regular security precautions might not be practical for these devices because they frequently have limited resources. The detection of malware attacks on IoT environments has found hope in ML approaches. In this paper, some machine learning(ML) approaches have been utilized to detect IoT Android malware threats. This method uses a collection of Android malware samples and good apps to build an ML model. Using the Android Malware dataset, many ML techniques, including Naive Bayes (NB), K-Nearest Neighbour (KNN), Decision Tree (DT), and Random Forest (RF), are used to detect malware in IoT. The accuracy of the DT model is 95\%, which is the highest accuracy rate, while that of the NB, KNN, and RF models have accuracy rates of 84\%, 89\%, and 92\%, respectively.

The motive of this paper is to detect the malware from computer systems in order to protect the confidential data, information, documents etc. from being accessing. The detection of malware is necessary because it steals the data from that system which is affected by malware. There are different malware detection techniques (cloud-based, signature-based, Iot-based, heuristic based etc.) and different malware detection tools (static, dynamic) area used in this paper to detect new generation malware. It is necessary to detect malware because the attacks of malware badly affect our economy and no one sector is untouched by it. The detection of malware is compulsory because it exploits goal devices vulnerabilities, along with a Trojan horse in valid software e.g. browser that may be hijacked. There are also different tools used for detection of malware like static or dynamic that we see in this paper. We also see different methods of detection of malware in android.

One of the important characteristics envisioned for 6G is security function virtualization (SFV). Similar to network function virtualization (NFV) in 5G networks, SFV provides new opportunities for improving security while reducing the security overhead. In particular, it provides an attractive way of solving compatibility issues related to security. Malware in Internet of Things (IoT) systems is gaining popularity among cyber-criminals because of the expected number of IoT devices in 5G and 6G networks. To solve this issue, this article proposes a security framework that exploits softwarization of security functions via SFV to improve trust in IoT systems and contain the propagation of malware. IoT devices are categorized into trusted, vulnerable, and compromised levels using remote attestation. To isolate the devices in the three distinct categories, NFV is used to create separate networks for each category, and a distributed ledger is used to store the state of each device. Virtualized remote attestation routines are employed to avoid any compatibility issues among heterogeneous IoT devices and effectively contain malware propagation. The results show that the proposed framework can reduce the number of infected devices by 66 percent in only 10 seconds.

Advances in wireless networking, such as 5G, continue to enable the vision of the Internet of Things (IoT), where everything is connected, and much data is collected by IoT devices and made available to interested parties (i.e., application servers). However, events such as botnet attacks (e.g., [1]) demonstrate that there are important challenges in this evolution.

Embedded systems that make up the Internet of Things (IoT), Supervisory Control and Data Acquisition (SCADA) networks, and Smart Grid applications are coming under increasing scrutiny in the security field. Remote Attestation (RA) is a security mechanism that allows a trusted device, the verifier, to determine the trustworthiness of an untrusted device, the prover. RA has become an area of high interest in academia and industry and many research works on RA have been published in recent years. This paper reviewed the published RA research works from 2003-2020. Our contributions are fourfold. First, we have re-framed the problem of RA into 5 smaller problems: root of trust, evidence type, evidence gathering, packaging and verification, and scalability. We have provided a holistic review of RA by discussing the relationships between these problems and the various solutions that exist in modern RA research. Second, we have presented an enhanced threat model that allows for a greater understanding of the security benefits of a given RA scheme. Third, we have proposed a taxonomy to classify and analyze RA research works and use it to categorize 58 RA schemes reported in literature. Fourth, we have provided cost benefit analysis details of each RA scheme surveyed such that security professionals may perform a cost benefit analysis in the context of their own challenges. Our classification and analysis has revealed areas of future research that have not been addressed by researchers rigorously.

Trusted data transmission is the foundation of the Internet of Things (IoT) security, so in the process of data transmission, the trust of IoT nodes needs to be confirmed in real time, and the real-time tracking of node trust is also expected. Yet, modern IoT devices provide limited security capabilities, forming a new attack focus. Remote attestation is a kind of technology to detect network threats by remotely checking the internal situation of terminal devices by a trusted entity. Multidevice attestation is rarely studied although the ongoing single device attestation techniques lack scalability in the application of IoT. In this article, we present a lightweight attestation protocol based on an IoT system under an ideal physical unclonable functions environment. Our protocol can resilient against any strong adversary who physically accesses IoT devices. Simulation results show that our protocol is scalable and can be applied to dynamic networks.

One of the important characteristics envisioned for 6G is security function virtualization (SFV). Similar to network function virtualization (NFV) in 5G networks, SFV provides new opportunities for improving security while reducing the security overhead. In particular, it provides an attractive way of solving compatibility issues related to security. Malware in Internet of Things (IoT) systems is gaining popularity among cyber-criminals because of the expected number of IoT devices in 5G and 6G networks. To solve this issue, this article proposes a security framework that exploits softwarization of security functions via SFV to improve trust in IoT systems and contain the propagation of malware. IoT devices are categorized into trusted, vulnerable, and compromised levels using remote attestation. To isolate the devices in the three distinct categories, NFV is used to create separate networks for each category, and a distributed ledger is used to store the state of each device. Virtualized remote attestation routines are employed to avoid any compatibility issues among heterogeneous IoT devices and effectively contain malware propagation. The results show that the proposed framework can reduce the number of infected devices by 66 percent in only 10 seconds.

Advances in wireless networking, such as 5G, continue to enable the vision of the Internet of Things (IoT), where everything is connected, and much data is collected by IoT devices and made available to interested parties (i.e., application servers). However, events such as botnet attacks (e.g., [1]) demonstrate that there are important challenges in this evolution.

Embedded systems that make up the Internet of Things (IoT), Supervisory Control and Data Acquisition (SCADA) networks, and Smart Grid applications are coming under increasing scrutiny in the security field. Remote Attestation (RA) is a security mechanism that allows a trusted device, the verifier, to determine the trustworthiness of an untrusted device, the prover. RA has become an area of high interest in academia and industry and many research works on RA have been published in recent years. This paper reviewed the published RA research works from 2003-2020. Our contributions are fourfold. First, we have re-framed the problem of RA into 5 smaller problems: root of trust, evidence type, evidence gathering, packaging and verification, and scalability. We have provided a holistic review of RA by discussing the relationships between these problems and the various solutions that exist in modern RA research. Second, we have presented an enhanced threat model that allows for a greater understanding of the security benefits of a given RA scheme. Third, we have proposed a taxonomy to classify and analyze RA research works and use it to categorize 58 RA schemes reported in literature. Fourth, we have provided cost benefit analysis details of each RA scheme surveyed such that security professionals may perform a cost benefit analysis in the context of their own challenges. Our classification and analysis has revealed areas of future research that have not been addressed by researchers rigorously.

Trusted data transmission is the foundation of the Internet of Things (IoT) security, so in the process of data transmission, the trust of IoT nodes needs to be confirmed in real time, and the real-time tracking of node trust is also expected. Yet, modern IoT devices provide limited security capabilities, forming a new attack focus. Remote attestation is a kind of technology to detect network threats by remotely checking the internal situation of terminal devices by a trusted entity. Multidevice attestation is rarely studied although the ongoing single device attestation techniques lack scalability in the application of IoT. In this article, we present a lightweight attestation protocol based on an IoT system under an ideal physical unclonable functions environment. Our protocol can resilient against any strong adversary who physically accesses IoT devices. Simulation results show that our protocol is scalable and can be applied to dynamic networks.

Due to recent notorious security threats, like Miraibotnet, it is challenging to perform efficient data communication and routing in low power and lossy networks (LLNs) such as Internet of Things (IoT), in which huge data collection and processing are predictable. The Routing Protocol for low power and Lossy networks (RPL) is recently standardized as a routing protocol for LLNs. However, the lack of scalability and the vulnerabilities towards various security threats still pose a significant challenge in the broader adoption of RPL in LLNs.

The edge computing-based Internet of Things (IoT) offers benefits in terms of efficiency, low latency, security, and privacy. However, programming models and platforms for this edge-based IoT are still an open problem, particularly regarding security and privacy. This paper proposes concrete and realizable ideas for building a secure programming platform called Secure Swarm Programming Platform (SSPP) to ensure platform-level security for the edge-based IoT while utilizing existing systemlevel security mechanisms. SSPP’s easy-to-use software components can enable static and dynamic security analysis of IoT applications, preventing vulnerabilities and detecting intrusions. Software deployed through SSPP can be remotely attested by a verifier on the edge, ensuring it remains untampered with. This paper also plans out future research and evaluation of SSPP’s programmability, security, and remote attestation.

With the proliferation of IoT devices, the number of devices connected to the Internet has been rapidly increasing. An edge computing platform must flexible and efficient data control. Also, edge nodes are not always reliable. Edge node administrators can leak data through intentional mishandling. In this paper, we propose an edge computing platform on modular architecture that protects data and processing from interception and a processing flow based on data characteristics using Intel SGX and multi-authority attribute-based encryption. In addition, we report a performance evaluation of our method.

The wide adoption of IoT gadgets and CyberPhysical Systems (CPS) makes embedded devices increasingly important. While some of these devices perform mission-critical tasks, they are usually implemented using Micro-Controller Units (MCUs) that lack security mechanisms on par with those available to general-purpose computers, making them more susceptible to remote exploits that could corrupt their software integrity. Motivated by this problem, prior work has proposed techniques to remotely assess the trustworthiness of embedded MCU software. Among them, Control Flow Attestation (CFA) enables remote detection of runtime abuses that illegally modify the program’s control flow during execution (e.g., control flow hijacking and code reuse attacks).

Technology integration has enabled value-added services and quality-of-life enhancement in almost all aspects of modern life. In this paper, we present a UAV and low-cost Bluetooth low energy (BLE) tags-based location search system which enables a cart take-home service for shoppers of a supermarket in a model smart colony. The presented system has quality-of-life enhancement as well as carbon footprint reduction effects and can be integrated with the existing security and/or transport system of the model smart colony. Conducted field trials on location accuracy of the system are also presented, showing that carts left by residents outside the home can be located within 6.58m and carts taken inside homes or buildings can be located within 16.43m.

IoT-Based Smart Bag and Women Security System is an novel solution to address the raising problem of women s safety and offers protection to their personal belongings while providing real-time status updates. In recent days, women often face insecure situations in society. To overcome this, a safety-oriented method has been proposed. When the person is attacked by any of the strangers of thieves, the person can use the push button by which an alert notification is delivered to the registered smart phone number with the person’s location. Additionally, the bag is provided with a shock generator that can be used by women to defend themselves against attacks from strangers or theft people, which generates an electric shock of 550V. The bag is also assisted with a finger print detector is used for securing the zipper to avoid theft. An internal lighting system have been used which detects the intensity of light and automatically switches ON when the intensity is low for ease of locating items and a wireless charger for consumer’s convenience. This system utilizes components such as ESP32, a fingerprint sensor, and a GPS system helps tracing the exact location of the bag. The collected data can visualize through the Adafruit dashboard, that offers users a clear view of the bag s location, and ON and OFF status of LED and fingerprint sensor.

Logistic transportation is the backbone of the supply chain. An uninterrupted transportation of any goods keeps the supply chain well balanced and thus helps the business as well as the economy. But in this current world, the transportation of goods is being harmed in many ways. One of those is theft, where the driver is also involved or not, but the thief steals the goods with or without breaking the seal. Both the supplying company and the client are affected by this. To reduce the problem, we are proposing a two-step security system. So that even if one system is deactivated somehow, the other system can be alerted, and necessary steps can be taken accordingly. By doing so, we can maintain a constant connection with the vehicle. Through this proposed project, the outer door seal of the cargo vehicle can be locked or unlocked, and the server can observe in real time whether any items inside are being stolen without opening the door. The security of logistics supply vehicles through the proposed paper will be more robust and beneficial to both the transport service provider and the service taker.

In today s world, security is a very important issue. People should always keep their belongings safe . To increase security, this research work proposes a IoT-based smart lockers with sensors and access keys with security, verification, and user-friendly tools. This model alerts the user when someone else tries to access their locker and quickly sends an alarm to the authorized user, and provides the option to either grant or reject access to the valid user. In this paper, smart locker is kept registered early to use a locker in the bank, office, home, etc. to ensure safety. The user demands to send an unlock direction with the help of microcontroller NUDE MCU ES P8266 and after accepting the command from the cloud (BLYNK APP), only the user can unlock the closet and access the valuables. This study has also introduced the encroachment detection in lockers with sensors and finally installed smart lockers with fire alarms for security and reliability.

Advances in sensor and communication technologies have transformed traditional homes into smart homes, equipped with sensors and actuators for various functionalities like smart lighting, temperature control, irrigation, solar monitoring, entertainment, and security. This transition is powered by the Internet of Things (IoT) architecture, enabling smart home hubs to integrate and control devices with different communication protocols. However, this shift has also introduced new security and privacy issues in the Smart Home IoT (SH-IoT) environment. To address these challenges, new communication protocols with cryptographic features have been developed, and a unified standard called Matter has been created to promote interoperability among different device manufacturers. This paper presents a comprehensive survey of recent trends and advances in the smart home IoT landscape, focusing on communication protocols, their security issues and protection features against vulnerabilities in the SH-IoT environment.

In the last decade the rapid development of the communications and IoT systems have risen many challenges regarding the security of the devices that are handled wirelessly. Therefore, in this paper, we intend to test the possibility of spoofing the parameters for connection of the Bluetooth Low Energy (BLE) devices, to make several recommendations for increasing the security of the usage of those devices and to propose basic counter measurements regarding the possibility of hacking them.

With the advancement in Internet of things smart homes are rapidly developing. Smart home is the major key component of Internet of thing. With the help of IOT technology we can stay connected to our home appliance. Internet of Things is the Associations of inserted advancements that. Contained physical protests and is utilized to convey and keenness or collaborate with the internal states or the outer surroundings. Rather than individuals to individuals’ correspondence, IoT accentuation on machine-to-machine correspondence. Smart home connects the physical components of our home with the help of software and sensors so that we can access them via internet from one place. Building home automation includes computerizing a home, likewise, mentioned to as a sensible home or smart home. Domestic machines are an urgent part of the Web of Things whenever they are associated with the web. Controlled devices are commonly connected to a focal center or entryway through a domestic automation framework. A smartphone application, tablet PC, personal computer, wall-mounted terminals, or even a web interface that can be gotten to from off-website over the Web are completely utilized by the program to work the framework. Since all the devices are interconnected and interlinked to one an-another they are lot of chances for security breach and data theft. If the security layer is easily breakable any third-party attacker can easily theft the private data of the user. Which leads us to pay more attention to protecting and securing private data. With the day-to-day development of Smart Home, the safety also got to be developed and updated day to day the safety challenges of the IoT for a wise home scenario are encountered, and a comprehensive IoT security management for smart homes has been proposed. This paper acquaints the status of IoT development, and furthermore contains security issues challenges. Finally, this paper surveys the Gamble factor, security issues and challenges in every point of view.

Multiple smart operations, similar as smart technologies in homes, smart metropolises, smart husbandry, and smart health and fitness centres, use a new technology known as the Internet of effects. They correspond of an multifariousness of multiple networked bias that link to multiple detectors and the internet. Among the layers that comprise an IoT armature are the perception subcaste, network subcaste, and operation subcaste. Due to their wide use, these smart biases have fairly minimum protection and are vulnerable to attacks. Comprehensive explanations of operation subcaste security issues and protocols, similar as Advance Message Queuing Protocol(AMQP) in application layer protocol, Constrained operation protocol( CoAP), and REST( Emblematic State Transport).

The Internet of Things (IoT) connects the physical world to the digital world, and wireless sensor networks (WSNs) play a significant role. There are billions of IoT products in the market. We found that security was not the primary focus of software developers. The first step of designing a secure product is to analyze and note down the security requirements. This research paper proposes a modified approach, incorporating elements from the SREP (Software Requirements Engineering Process) and SQUARE (Security Quality Requirement Engineering), to define security requirements for IoT products. The revised process is applied to determine the security requirements of a Smart Lock system that utilizes the publish/subscribe protocol MQTT-SN (Message Queuing Telemetry Transport for Sensor Networks) communication protocol architecture.