The Empirical Analysis on Proposed Ids Models based on Deep Learning Techniques for Privacy Preserving Cyber Security

In AI, the deep learning (DL) method of machine learning (ML) places an emphasis on large-scale, scalable models that can learn distributed representations from their input data. The scope and effectiveness of these techniques are demonstrated in this thesis through a number of case studies pertaining to cyber security. By the end of each study, the neural network models had been fine-tuned and expanded to provide better results. The key arguments presented and discussed in this thesis are as follows: 1) Creating an all-inclusive database for domain name detection using domain generation algorithms (DGAs) and a new architecture to improve DGA domain name detection overall performance. 2) Constructing a hybrid intrusion detection warning system that incorporates deep neural networks (DNNs) to examine host-level and network-level behaviours within an Ethernet LAN. thirdly, analysing data from social media platforms, email, and URLs to create a single DL-based framework for detecting spam and phishing. 4) ScaleMalNet, a novel hybrid framework proposal, is part four. This is a two-step process: first, we use static and dynamic analysis to determine if the executable file is malicious or not. Then, we categorise the malicious executable file into the appropriate malware family. Malware and ransomware analysis for Android is accomplished using a hybrid DL framework that is comparable to this one

The Privacy Leakage of IP Camera Systems

For in-home security, intelligent operations like top individual recognition and minimizing losses due to home break-ins, emergencies, and fraud are keys to success. This application integrates the closed-circuit television (CCTV) camera and the deep learning algorithms used to process these images. Automated intrusion detection alerts, real-time fire alerts, smart checkout, and potentially fraudulent point of sale (POS) transactions are its main features. Dynamic intrusion with machine learning is a software program in which the price of certain products changes over time through an algorithm that considers a variety of pricing variables. The face locator is a part of the algorithm that locates and detects motion by using the image search function. The system collects all available product locations from the live videos from multiple cameras. This is a helpful feature for finding misplaced products and detecting POS user fraud. This intrusion detection system (IDS) records POS transaction details on the screen as an overlay on video images to reduce home break-ins. To improve the ease and speed of transaction searches, the faces of individuals are used to search for disputed cases. Smart Checkout System (SCS) utilizes a self-service kiosk where users can generate bills by showing products to the linked camera. SCS uses Google vision technology to identify products. Motion detector and queue detection will detect long queues at the checkout counter in real-time and open new lanes to speed up the transaction, improve the experience, and reduce the number of abandoned purchases. Face recognition premium and alerts can also be provided

Improving the Anomaly Detection by Combining PSO Search Methods and J48 Algorithm

The feature selection techniques are used to find the most important and relevant features in a dataset. Therefore, in this study feature selection technique was used to improve the performance of Anomaly Detection. Many feature selection techniques have been developed and implemented on the NSL-KDD dataset. However, with the rapid growth of traffic on a network where more applications, devices, and protocols participate, the traffic data is complex and heterogeneous contribute to security issues. This makes the NSL-KDD dataset no longer reliable for it. The detection model must also be able to recognize the type of novel attack on complex network datasets. So, a robust analysis technique for a more complex and larger dataset is required, to overcome the increase of security issues in a big data network. This study proposes particle swarm optimization (PSO) Search methods as a feature selection method. As contribute to feature analysis knowledge, In the experiment a combination of particle swarm optimization (PSO) Search methods with other search methods are examined. To overcome the limitation NSL-KDD dataset, in the experiments the CICIDS2017 dataset used. To validate the selected features from the proposed technique J48 classification algorithm used in this study. The detection performance of the combination PSO Search method with J48 examined and compare with other feature selection and previous study. The proposed technique successfully finds the important features of the dataset, which improve detection performance with 99.89% accuracy. Compared with the previous study the proposed technique has better accuracy, TPR, and FPR

Malware: Detection and Defense

In today’s cyber security landscape, companies are facing increasing pressure to protect their data and systems from malicious attackers. As a result, there has been a significant rise in the number of security solutions that can identify malware. But how do you know if an image file is infected with malware? How can you prevent it from running? This blog post covers everything you need to know about malware in your images and how to prevent them from running. The malware will allow the attacker or un-legitimate user to enter the system without being recognized as a valid user. In this paper, we will look at how malware can hide within images and transfer between computers in the background of any system. In addition, we will describe how deep transfer learning can detect malware hidden beneath images in this paper. In addition, we will compare multiple kernel models for detecting malicious images. We also highly suggest which model should be used by the system for detecting malware

Метод захисту Web-сторiнок Iнтернет магазину

Робота публікується згідно наказу Ректора НАУ від 27.05.2021 р. №311/од "Про розміщення кваліфікаційних робіт здобувачів вищої освіти в репозиторії університету" . Керівник проекту: доцент, к.т.н., Гулак Н.К.В реаліях сучасності третину світового ВВП займають саме інформаційні ресурси. Інформація перетворюється на товар та фактор впливу. В останні роки в Україні та у всьому світі зростає кількість кібератак та кіберзлочинів. Кожна компанія, кожна ланка суспільства володіє конфіденційною інформацією, витоки якої призводять до колосальних збитків та непоправних наслідків. Для протидії кіберзлочинності великого значення набуває розробка методів та систем для захисту інформації. Кожна інформаційна система може бути захищеною, але з розвитком несанкціонованого доступу цей захист стає умовним і потребує удосконалення. Тому, тема дипломної роботи є досить актуальною на сьогодні. Метою дипломної роботи є розробка методу захисту Веб-сторiнок Iнтернет магазину, на основі методів екранування та приведення до цілочисельного типу. Досягнення мети потребує розв’язання таких задач: - аналіз стандартних засобів захисту Web-сторiнок Iнтернет магазину; - дослідження методів захисту Web-сторiнок Iнтернет магазину від атак; - розробка та тестування комбінації методів захисту Веб-сторiнок Iнтернет магазину. Об’єкт дослідження: процес захисту Веб-сторiнок Iнтернет магазину від мережевих атак. Практична цінність на основі методів екранування та приведення до цілочисельного типу було розробленно та протестовано метод захисту інформації від мережевих атак Веб-сторінок, який може використовуватися для захисту інформації в веб-сторінках

A Framework for Improving Intrusion Detection Systems by Combining Artificial Intelligence and Situational Awareness

The vast majority of companies do not have the requisite tools and analysis to make use of the data obtained from security incidents in order to protect themselves from attacks and lower their risk. Intrusion Detection Systems (IDS) are deployed by numerous businesses to lessen the impact of network attacks. This is mostly attributable to the fact that these systems are able to provide a situational picture of network traffic regardless of the method or technology that is used to generate alerts. In this paper, a framework is proposed for improving the performance of contemporary IDSs by incorporating Artificial Intelligence (AI) into multiple layers, presenting the appropriate abstraction and accumulation of information, and generating valuable logs and metrics for security analysts to use in order to make the most informed decisions possible. This is further enabled by including Situational Awareness (SA) at the fundamental levels of the framework. Keywords: Intrusion Detection System, Machine Learning, Deep Learning, Shallow Learning, Security Operation Center, Situational Awarenes