4,153 research outputs found

    GRASE: Granulometry Analysis with Semi Eager Classifier to Detect Malware

    Get PDF
    Technological advancement in communication leading to 5G, motivates everyone to get connected to the internet including ‘Devices’, a technology named Web of Things (WoT). The community benefits from this large-scale network which allows monitoring and controlling of physical devices. But many times, it costs the security as MALicious softWARE (MalWare) developers try to invade the network, as for them, these devices are like a ‘backdoor’ providing them easy ‘entry’. To stop invaders from entering the network, identifying malware and its variants is of great significance for cyberspace. Traditional methods of malware detection like static and dynamic ones, detect the malware but lack against new techniques used by malware developers like obfuscation, polymorphism and encryption. A machine learning approach to detect malware, where the classifier is trained with handcrafted features, is not potent against these techniques and asks for efforts to put in for the feature engineering. The paper proposes a malware classification using a visualization methodology wherein the disassembled malware code is transformed into grey images. It presents the efficacy of Granulometry texture analysis technique for improving malware classification. Furthermore, a Semi Eager (SemiE) classifier, which is a combination of eager learning and lazy learning technique, is used to get robust classification of malware families. The outcome of the experiment is promising since the proposed technique requires less training time to learn the semantics of higher-level malicious behaviours. Identifying the malware (testing phase) is also done faster. A benchmark database like malimg and Microsoft Malware Classification challenge (BIG-2015) has been utilized to analyse the performance of the system. An overall average classification accuracy of 99.03 and 99.11% is achieved, respectively

    Southern Adventist University Undergraduate Catalog 2023-2024

    Get PDF
    Southern Adventist University\u27s undergraduate catalog for the academic year 2023-2024.https://knowledge.e.southern.edu/undergrad_catalog/1123/thumbnail.jp

    Wide-band spectrum sensing with convolution neural network using spectral correlation function

    Get PDF
    Recognition of signals is a spectrum sensing challenge requiring simultaneous detection, temporal and spectral localization, and classification. In this approach, we present the convolution neural network (CNN) architecture, a powerful portrayal of the cyclo-stationarity trademark, for remote range detection and sign acknowledgment. Spectral correlation function is used along with CNN. In two scenarios, method-1 and method-2, the suggested approach is used to categorize wireless signals without any previous knowledge. Signals are detected and classified simultaneously in method-1. In method-2, the sensing and classification procedures take place sequentially. In contrast to conventional spectrum sensing techniques, the proposed CNN technique need not bother with a factual judgment process or past information on the signs’ separating qualities. The method beats both conventional sensing methods and signal-classifying deep learning networks when used to analyze real-world, over-the-air data in cellular bands. Despite the implementation’s emphasis on cellular signals, any signal having cyclo-stationary properties may be detected and classified using the provided approach. The proposed model has achieved more than 90% of testing accuracy at 15 dB

    Evolutionary ecology of obligate fungal and microsporidian invertebrate pathogens

    Get PDF
    The interactions between hosts and their parasites and pathogens are omnipresent in the natural world. These symbioses are not only key players in ecosystem functioning, but also drive genetic diversity through co-evolutionary adaptations. Within the speciose invertebrates, a plethora of interactions with obligate fungal and microsporidian pathogens exist, however the known interactions is likely only a fraction of the true diversity. Obligate invertebrate fungal and microsporidian pathogen require a host to continue their life cycle, some of which have specialised in certain host species and require host death to transmit to new hosts. Due to their requirement to kill a host to spread to a new one, obligate fungal and microsporidian pathogens regulate invertebrate host populations. Pathogen specialisation to a single or very few hosts has led to some fungi evolving the ability to manipulate their host’s behaviour to maximise transmission. The entomopathogenic fungus, Entomophthora muscae, infects houseflies (Musca domestica) over a week-long proliferation cycle, resulting in flies climbing to elevated positions, gluing their mouthparts to the substrate surface, and raising their wings to allow for a clear exit from fungal conidia through the host abdomen. These sequential behaviours are all timed to occur within a few hours of sunset. The E. muscae mechanisms used in controlling the mind of the fly remain relatively unknown, and whether other fitness costs ensue from an infection are understudied.European Commissio

    Soft touchless sensors and touchless sensing for soft robots

    Get PDF
    Soft robots are characterized by their mechanical compliance, making them well-suited for various bio-inspired applications. However, the challenge of preserving their flexibility during deployment has necessitated using soft sensors which can enhance their mobility, energy efficiency, and spatial adaptability. Through emulating the structure, strategies, and working principles of human senses, soft robots can detect stimuli without direct contact with soft touchless sensors and tactile stimuli. This has resulted in noteworthy progress within the field of soft robotics. Nevertheless, soft, touchless sensors offer the advantage of non-invasive sensing and gripping without the drawbacks linked to physical contact. Consequently, the popularity of soft touchless sensors has grown in recent years, as they facilitate intuitive and safe interactions with humans, other robots, and the surrounding environment. This review explores the emerging confluence of touchless sensing and soft robotics, outlining a roadmap for deployable soft robots to achieve human-level dexterity

    On the Control of Microgrids Against Cyber-Attacks: A Review of Methods and Applications

    Get PDF
    Nowadays, the use of renewable generations, energy storage systems (ESSs) and microgrids (MGs) has been developed due to better controllability of distributed energy resources (DERs) as well as their cost-effective and emission-aware operation. The development of MGs as well as the use of hierarchical control has led to data transmission in the communication platform. As a result, the expansion of communication infrastructure has made MGs as cyber-physical systems (CPSs) vulnerable to cyber-attacks (CAs). Accordingly, prevention, detection and isolation of CAs during proper control of MGs is essential. In this paper, a comprehensive review on the control strategies of microgrids against CAs and its defense mechanisms has been done. The general structure of the paper is as follows: firstly, MGs operational conditions, i.e., the secure or insecure mode of the physical and cyber layers are investigated and the appropriate control to return to a safer mode are presented. Then, the common MGs communication system is described which is generally used for multi-agent systems (MASs). Also, classification of CAs in MGs has been reviewed. Afterwards, a comprehensive survey of available researches in the field of prevention, detection and isolation of CA and MG control against CA are summarized. Finally, future trends in this context are clarified

    Experimental analysis of intrusion detection systems using machine learning algorithms and artificial neural networks

    Get PDF
    Since the invention of the internet for military and academic research purposes, it has evolved to meet the demands of the increasing number of users on the network, who have their scope beyond military and academics. As the scope of the network expanded maintaining its security became a matter of increasing importance. With various users and interconnections of more diversified networks, the internet needs to be maintained as securely as possible for the transmission of sensitive information to be one hundred per cent safe; several anomalies may intrude on private networks. Several research works have been released around network security and this research seeks to add to the already existing body of knowledge by expounding on these attacks, proffering efficient measures to detect network intrusions, and introducing an ensemble classifier: a combination of 3 different machine learning algorithms. An ensemble classifier is used for detecting remote to local (R2L) attacks, which showed the lowest level of accuracy when the network dataset is tested using single machine learning models but the ensemble classifier gives an overall efficiency of 99.8%

    The Importance of Conceptualising the Human-Centric Approach in Maintaining and Promoting Cybersecurity-Hygiene in Healthcare 4.0

    Get PDF
    The cyberspace depicts an increasing number of difficulties related to security, especially in healthcare. This is evident from how vulnerable critical infrastructures are to cyberattacks and are unprotected against cybercrime. Users, ideally, should maintain a good level of cyber hygiene, via regular software updates and the development of unique passwords, as an effective way to become resilient to cyberattacks. Cyber security breaches are a top priority, and most users are aware that their behaviours may put them at risk; however, they are not educated to follow best practices, such as protecting their passwords. Mass cyber education may serve as a means to offset poor cyber security behaviours; however, mandatory education becomes a questionable point if the content is not focused on human factors, using human-centric approaches and taking into account end users’ behaviours, which is currently the case. The nature of the present paper is largely exploratory, and the purpose is two-fold: To present and explore the cyber hygiene definition, context and habits of end users in order to strengthen our understanding of users. Our paper reports the best practices that should be used by healthcare organisations and healthcare professionals to maintain good cyber hygiene and how these can be applied via a healthcare use case scenario to increase awareness related to data privacy and cybersecurity. This is an issue of great importance and urgency considering the rapid increase of cyberattacks in healthcare organisations, mainly due to human errors. Further to that, based on human-centric approaches, our long-term vision and future work involves facilitating the development of efficient practices and education associated with cybersecurity hygiene via a flexible, adaptable and practical framework

    DECEPTION BASED TECHNIQUES AGAINST RANSOMWARES: A SYSTEMATIC REVIEW

    Get PDF
    Ransomware is the most prevalent emerging business risk nowadays. It seriously affects business continuity and operations. According to Deloitte Cyber Security Landscape 2022, up to 4000 ransomware attacks occur daily, while the average number of days an organization takes to identify a breach is 191. Sophisticated cyber-attacks such as ransomware typically must go through multiple consecutive phases (initial foothold, network propagation, and action on objectives) before accomplishing its final objective. This study analyzed decoy-based solutions as an approach (detection, prevention, or mitigation) to overcome ransomware. A systematic literature review was conducted, in which the result has shown that deception-based techniques have given effective and significant performance against ransomware with minimal resources. It is also identified that contrary to general belief, deception techniques mainly involved in passive approaches (i.e., prevention, detection) possess other active capabilities such as ransomware traceback and obstruction (thwarting), file decryption, and decryption key recovery. Based on the literature review, several evaluation methods are also analyzed to measure the effectiveness of these deception-based techniques during the implementation process
    • …
    corecore