IoT Malware Network Traffic Classification using Visual Representation and Deep Learning

With the increase of IoT devices and technologies coming into service, Malware has risen as a challenging threat with increased infection rates and levels of sophistication. Without strong security mechanisms, a huge amount of sensitive data is exposed to vulnerabilities, and therefore, easily abused by cybercriminals to perform several illegal activities. Thus, advanced network security mechanisms that are able of performing a real-time traffic analysis and mitigation of malicious traffic are required. To address this challenge, we are proposing a novel IoT malware traffic analysis approach using deep learning and visual representation for faster detection and classification of new malware (zero-day malware). The detection of malicious network traffic in the proposed approach works at the package level, significantly reducing the time of detection with promising results due to the deep learning technologies used. To evaluate our proposed method performance, a dataset is constructed which consists of 1000 pcap files of normal and malware traffic that are collected from different network traffic sources. The experimental results of Residual Neural Network (ResNet50) are very promising, providing a 94.50% accuracy rate for detection of malware traffic.Comment: 10 pages, 5 figures, 2 table

Insider Misuse Identification using Transparent Biometrics

Insider misuse is a key threat to organizations. Recent research has focused upon the information itself – either through its protection or approaches to detect the leakage. This paper seeks a different approach through the application of transparent biometrics to provide a robust approach to the identification of the individuals who are misusing systems and information. Transparent biometrics are a suite of modalities, typically behavioral-based that can capture biometric signals covertly or non-intrusively – so the user is unaware of their capture. Transparent biometrics are utilized in two phases a) to imprint digital objects with biometric-signatures of the user who last interacted with the object and b) uniquely applied to network traffic in order to identify users traffic (independent of the Internet Protocol address) so that users rather than machine (IP) traffic can be more usefully analyzed by analysts. Results from two experimental studies are presented and illustrate how reliably transparent biometrics are in providing this link-ability of information to identity.

Physical activity recognition by utilising smartphone sensor signals

Human physical motion activity identification has many potential applications in various fields, such as medical diagnosis, military sensing, sports analysis, and human-computer security interaction. With the recent advances in smartphones and wearable technologies, it has become common for such devices to have embedded motion sensors that are able to sense even small body movements. This study collected human activity data from 60 participants across two different days for a total of six activities recorded by gyroscope and accelerometer sensors in a modern smartphone. The paper investigates to what extent different activities can be identified by utilising machine learning algorithms using approaches such as majority algorithmic voting. More analyses are also provided that reveal which time and frequency domain-based features were best able to identify individuals’ motion activity types. Overall, the proposed approach achieved a classification accuracy of 98% in identifying four different activities: walking, walking upstairs, walking downstairs, and sitting (on a chair) while the subject is calm and doing a typical desk-based activity

A generalized laser simulator algorithm for mobile robot path planning with obstacle avoidance

This paper aims to develop a new mobile robot path planning algorithm, called generalized laser simulator (GLS), for navigating autonomously mobile robots in the presence of static and dynamic obstacles. This algorithm enables a mobile robot to identify a feasible path while finding the target and avoiding obstacles while moving in complex regions. An optimal path between the start and target point is found by forming a wave of points in all directions towards the target position considering target minimum and border maximum distance principles. The algorithm will select the minimum path from the candidate points to target while avoiding obstacles. The obstacle borders are regarded as the environment’s borders for static obstacle avoidance. However, once dynamic obstacles appear in front of the GLS waves, the system detects them as new dynamic obstacle borders. Several experiments were carried out to validate the effectiveness and practicality of the GLS algorithm, including path-planning experiments in the presence of obstacles in a complex dynamic environment. The findings indicate that the robot could successfully find the correct path while avoiding obstacles. The proposed method is compared to other popular methods in terms of speed and path length in both real and simulated environments. According to the results, the GLS algorithm outperformed the original laser simulator (LS) method in path and success rate. With application of the all-direction border scan, it outperforms the A-star (A*) and PRM algorithms and provides safer and shorter paths. Furthermore, the path planning approach was validated for local planning in simulation and real-world tests, in which the proposed method produced the best path compared to the original LS algorithm

A novel behaviour profiling approach to continuous authentication for mobile applications

The growth in smartphone usage has led to increased user concerns regarding privacy and security. Smartphones contain sensitive information, such as personal data, images, and emails, and can be used to perform various types of activity, such as transferring money via mobile Internet banking, making calls and sending emails. As a consequence, concerns regarding smartphone security have been expressed and there is a need to devise new solutions to enhance the security of mobile applications, especially after initial access to a mobile device. This paper presents a novel behavioural profiling approach to user identity verification as part of mobile application security. A study involving data collected from 76 users over a 1-month period was conducted, generating over 3 million actions based on users’ interactions with their smartphone. The study examines a novel user interaction approach based on supervised machine learning algorithms, thereby enabling a more reliable identity verification method. The experimental results show that users could be distinguished via their behavioural profiling upon each action within the application, with an average equal error rate of 26.98% and the gradient boosting classifier results prove quite compelling. Based on these findings, this approach is able to provide robust, continuous and transparent authentication

Leveraging biometrics for insider misuse identification

Insider misuse has become a real threat to many enterprises in the last decade. A major source of such threats originates from those individuals who have inside knowledge about the organization’s resources. Therefore, preventing or responding to such incidents has become a challenging task. Digital forensics has grown into a de-facto standard in the examination of electronic evidence, which provides a basis for investigating incidents. A key barrier however is often being able to associate an individual to the stolen data—especially when stolen credentials and the Trojan defense are two commonly cited arguments. This paper proposes an approach that can more inextricably link the use of information (e.g. images, documents and emails) to the individual users who use and access them through the use of transparent biometric imprinting. The use of transparent biometrics enables the covert capture of a user’s biometric information—avoiding the potential for forgery. A series of experiments are presented to evaluate the capability of retrieving the biometric information through a variety of file modification attacks. The preliminary feasibility study has shown that it is possible to correlate an individual’s biometric information with a digital object (images) and still be able to recover the biometric signal even with significant file modification

Identification d'un marqueur précoce potentiel et caractérisation du rôle de l'initiation dans le processus cancéreux suite à l'étude des mécanismes moléculaires impliqués dans l'hépatocarcinogénèse non -génotoxique induite par l'acide clorifibrique chez le rat

L'évaluation de l'effet hépatocarcinogène non génotoxique (HNG) d'une molécule en développement nécessite des études longues (2 ans) réalisés chez les rongeurs. Le mécanisme d'action de l'acide clofibrique (CLO) , un HNG chez les rongeurs, passe par l'activation d'un récepteur nucléaire le PPARa. La génomique a été utilisée pour obtenir une cartographie exhaustive des effets épigéniques découlant de cette activation transcriptionnelle. L'objectif de notre travail étant de développer des modèles expérimentaux plus courts, nous avons montré que l'addition d'une étape d'initiation permet d'accélerer l'apparition des lésions néoplasiques induites par le CLO sans modifier le déroulement moléculaire de cancérogénèse. Pour étudier spécifiquement les cellules prénéoplasiques , nous avons évalué la faisabilité de combiner la microdissection laserPARIS5-BU-Necker : Fermée (751152101) / SudocPARIS-BIUP (751062107) / SudocSudocFranceF

Artificial-Intelligence-Based Decision Making for Oral Potentially Malignant Disorder Diagnosis in Internet of Medical Things Environment

Oral cancer is considered one of the most common cancer types in several counties. Earlier-stage identification is essential for better prognosis, treatment, and survival. To enhance precision medicine, Internet of Medical Things (IoMT) and deep learning (DL) models can be developed for automated oral cancer classification to improve detection rate and decrease cancer-specific mortality. This article focuses on the design of an optimal Inception-Deep Convolution Neural Network for Oral Potentially Malignant Disorder Detection (OIDCNN-OPMDD) technique in the IoMT environment. The presented OIDCNN-OPMDD technique mainly concentrates on identifying and classifying oral cancer by using an IoMT device-based data collection process. In this study, the feature extraction and classification process are performed using the IDCNN model, which integrates the Inception module with DCNN. To enhance the classification performance of the IDCNN model, the moth flame optimization (MFO) technique can be employed. The experimental results of the OIDCNN-OPMDD technique are investigated, and the results are inspected under specific measures. The experimental outcome pointed out the enhanced performance of the OIDCNN-OPMDD model over other DL models