Towards Accurate Run-Time Hardware-Assisted Stealthy Malware Detection: A Lightweight, yet Effective Time Series CNN-Based Approach

According to recent security analysis reports, malicious software (a.k.a. malware) is rising at an alarming rate in numbers, complexity, and harmful purposes to compromise the security of modern computer systems. Recently, malware detection based on low-level hardware features (e.g., Hardware Performance Counters (HPCs) information) has emerged as an effective alternative solution to address the complexity and performance overheads of traditional software-based detection methods. Hardware-assisted Malware Detection (HMD) techniques depend on standard Machine Learning (ML) classifiers to detect signatures of malicious applications by monitoring built-in HPC registers during execution at run-time. Prior HMD methods though effective have limited their study on detecting malicious applications that are spawned as a separate thread during application execution, hence detecting stealthy malware patterns at run-time remains a critical challenge. Stealthy malware refers to harmful cyber attacks in which malicious code is hidden within benign applications and remains undetected by traditional malware detection approaches. In this paper, we first present a comprehensive review of recent advances in hardware-assisted malware detection studies that have used standard ML techniques to detect the malware signatures. Next, to address the challenge of stealthy malware detection at the processor’s hardware level, we propose StealthMiner, a novel specialized time series machine learning-based approach to accurately detect stealthy malware trace at run-time using branch instructions, the most prominent HPC feature. StealthMiner is based on a lightweight time series Fully Convolutional Neural Network (FCN) model that automatically identifies potentially contaminated samples in HPC-based time series data and utilizes them to accurately recognize the trace of stealthy malware. Our analysis demonstrates that using state-of-the-art ML-based malware detection methods is not effective in detecting stealthy malware samples since the captured HPC data not only represents malware but also carries benign applications’ microarchitectural data. The experimental results demonstrate that with the aid of our novel intelligent approach, stealthy malware can be detected at run-time with 94% detection performance on average with only one HPC feature, outperforming the detection performance of state-of-the-art HMD and general time series classification methods by up to 42% and 36%, respectively

Enhancing Security in Internet of Healthcare Application using Secure Convolutional Neural Network

The ubiquity of Internet of Things (IoT) devices has completely changed the healthcare industry by presenting previously unheard-of potential for remote patient monitoring and individualized care. In this regard, we suggest a unique method that makes use of Secure Convolutional Neural Networks (SCNNs) to improve security in Internet-of-Healthcare (IoH) applications. IoT-enabled healthcare has advanced as a result of the integration of IoT technologies, giving it impressive data processing powers and large data storage capacity. This synergy has led to the development of an intelligent healthcare system that is intended to remotely monitor a patient's medical well-being via a wearable device as a result of the ongoing advancement of the Industrial Internet of Things (IIoT). This paper focuses on safeguarding user privacy and easing data analysis. Sensitive data is carefully separated from user-generated data before being gathered. Convolutional neural network (CNN) technology is used to analyse health-related data thoroughly in the cloud while scrupulously protecting the privacy of the consumers.The paper provide a secure access control module that functions using user attributes within the IoT-Healthcare system to strengthen security. This module strengthens the system's overall security and privacy by ensuring that only authorised personnel may access and interact with the sensitive health data. The IoT-enabled healthcare system gets the capacity to offer seamless remote monitoring while ensuring the confidentiality and integrity of user information thanks to this integrated architecture

DEDA: An algorithm for early detection of topology attacks in the internet of things

The internet of things (IoT) is used in domestic, industrial as well as mission-critical systems including homes, transports, power plants, industrial manufacturing and health-care applications. Security of data generated by such systems and IoT systems itself is very critical in such applications. Early detection of any attack targeting IoT system is necessary to minimize the damage. This paper reviews security attack detection methods for IoT Infrastructure presented in the state-of-the-art. One of the major entry points for attacks in IoT system is topology exploitation. This paper proposes a distributed algorithm for early detection of such attacks with the help of predictive descriptor tables. This paper also presents feature selection from topology control packet fields. The performance of the proposed algorithm is evaluated using an extensive simulation carried out in OMNeT++. Performance parameter includes accuracy and time required for detection. Simulation results presented in this paper show that the proposed algorithm is effective in detecting attacks ahead in time

SHARKS: Smart Hacking Approaches for RisK Scanning in Internet-of-Things and Cyber-Physical Systems based on Machine Learning

Cyber-physical systems (CPS) and Internet-of-Things (IoT) devices are increasingly being deployed across multiple functionalities, ranging from healthcare devices and wearables to critical infrastructures, e.g., nuclear power plants, autonomous vehicles, smart cities, and smart homes. These devices are inherently not secure across their comprehensive software, hardware, and network stacks, thus presenting a large attack surface that can be exploited by hackers. In this article, we present an innovative technique for detecting unknown system vulnerabilities, managing these vulnerabilities, and improving incident response when such vulnerabilities are exploited. The novelty of this approach lies in extracting intelligence from known real-world CPS/IoT attacks, representing them in the form of regular expressions, and employing machine learning (ML) techniques on this ensemble of regular expressions to generate new attack vectors and security vulnerabilities. Our results show that 10 new attack vectors and 122 new vulnerability exploits can be successfully generated that have the potential to exploit a CPS or an IoT ecosystem. The ML methodology achieves an accuracy of 97.4% and enables us to predict these attacks efficiently with an 87.2% reduction in the search space. We demonstrate the application of our method to the hacking of the in-vehicle network of a connected car. To defend against the known attacks and possible novel exploits, we discuss a defense-in-depth mechanism for various classes of attacks and the classification of data targeted by such attacks. This defense mechanism optimizes the cost of security measures based on the sensitivity of the protected resource, thus incentivizing its adoption in real-world CPS/IoT by cybersecurity practitioners.Comment: This article has been accepted in IEEE Transactions on Emerging Topics in Computing. 17 pages, 12 figures, IEEE copyrigh

A Survey of Using Machine Learning in IoT Security and the Challenges Faced by Researchers

The Internet of Things (IoT) has become more popular in the last 15 years as it has significantly improved and gained control in multiple fields. We are nowadays surrounded by billions of IoT devices that directly integrate with our lives, some of them are at the center of our homes, and others control sensitive data such as military fields, healthcare, and datacenters, among others. This popularity makes factories and companies compete to produce and develop many types of those devices without caring about how secure they are. On the other hand, IoT is considered a good insecure environment for cyber thefts. Machine Learning (ML) and Deep Learning (DL) also gained more importance in the last 15 years; they achieved success in the networking security field too. IoT has some similar security requirements such as traditional networks, but with some differences according to its characteristics, some specific security features, and environmental limitations, some differences are made such as low energy resources, limited computational capability, and small memory. These limitations inspire some researchers to search for the perfect and lightweight security ways which strike a balance between performance and security. This survey provides a comprehensive discussion about using machine learning and deep learning in IoT devices within the last five years. It also lists the challenges faced by each model and algorithm. In addition, this survey shows some of the current solutions and other future directions and suggestions. It also focuses on the research that took the IoT environment limitations into consideration

Risk driven models & security framework for drone operation in GNSS-denied environments

Flying machines in the air without human inhabitation has moved from abstracts to reality and the concept of unmanned aerial vehicles continues to evolve. Drones are popularly known to use GPS and other forms of GNSS for navigation, but this has unfortunately opened them up to spoofing and other forms of cybersecurity threats. The use of computer vision to find location through pre-stored satellite images has become a suggested solution but this gives rise to security challenges in the form of spoofing, tampering, denial of service and other forms of attacks. These security challenges are reviewed with appropriate requirements recommended. This research uses the STRIDE threat analysis model to analyse threats in drone operation in GNSS-denied environment. Other threat models were considered including DREAD and PASTA, but STRIDE is chosen because of its suitability and the complementary ability it serves to other analytical methods used in this work. Research work is taken further to divide the drone system into units based in similarities in functions and architecture. They are then subjected to Failure Mode and Effects Analysis (FMEA), and Fault Tree Analysis (FTA). The STRIDE threat model is used as base events for the FTA and an FMEA is conducted based on adaptations from IEC 62443-1-1, Network and System Security- Terminology, concepts, and models and IEC 62443-3-2, security risk assessment for system design. The FTA and FMEA are widely known for functional safety purposes but there is a divergent use for the tools where we consider cybersecurity vulnerabilities specifically, instead of faults. The IEC 62443 series has become synonymous with Industrial Automation and Control Systems. However, inspiration is drawn from that series for this work because, drones, as much as any technological gadget in play recently, falls under a growing umbrella of quickly evolving devices, known as Internet of Things (IoT). These IoT devices can be principally considered as part of Industrial Automation and Control Systems. Results from the analysis are used to recommend security standards & requirements that can be applied in drone operation in GNSS-denied environments. The framework recommended in this research is consistent with IEC 62443-3-3, System security requirements and security levels and has the following categorization from IEC 62443-1-1, identification, and authentication control, use control, system integrity, data confidentiality, restricted data flow, timely response to events and resource availability. The recommended framework is applicable and relevant to military, private and commercial drone deployment because the framework can be adapted and further tweaked to suit the context which it is intended for. Application of this framework in drone operation in GNSS denied environment will greatly improve upon the cyber resilience of the drone network system

A Novel Ensemble Model Using Learning Classifiers to Enhance Malware Detection for Cyber Security Systems

In the Internet of Things arena, smart gadgets are employed to offer quick and dependable access to services. IoT technology has the ability to recognize extensive information, provide information reliably, and process that information intelligently. Data networks, controllers, and sensors are increasingly used in industrial systems nowadays. Attacks have increased as a result of the growth in connected systems and the technologies they employ. These attacks may interrupt international business and result in significant financial losses. Utilizing a variety of methods, including deep learning (DL) and machine learning (ML), cyber assaults have been discovered. In this research, we provide an ensemble staking approach to efficiently and quickly detect cyber-attacks in the IoT. The NSL, credit card, and UNSW information bases were the three separate datasets used for the experiments. The suggested novel combinations of ensemble classifiers are done better than the other individual classifiers from the base model. Additionally, based on the test outcomes, it could be concluded that all tree and bagging-based combinations performed admirably and that, especially when their corresponding hyperparameters are set properly, differences in performance across methods are not significant statistically. Additionally, compared to other comparable PE (Portable Executable) malware detectors that were published recently, the suggested tree-based ensemble approaches outperformed them