Statistical analysis driven optimized deep learning system for intrusion detection

Attackers have developed ever more sophisticated and intelligent ways to hack information and communication technology systems. The extent of damage an individual hacker can carry out upon infiltrating a system is well understood. A potentially catastrophic scenario can be envisaged where a nation-state intercepting encrypted financial data gets hacked. Thus, intelligent cybersecurity systems have become inevitably important for improved protection against malicious threats. However, as malware attacks continue to dramatically increase in volume and complexity, it has become ever more challenging for traditional analytic tools to detect and mitigate threat. Furthermore, a huge amount of data produced by large networks has made the recognition task even more complicated and challenging. In this work, we propose an innovative statistical analysis driven optimized deep learning system for intrusion detection. The proposed intrusion detection system (IDS) extracts optimized and more correlated features using big data visualization and statistical analysis methods (human-in-the-loop), followed by a deep autoencoder for potential threat detection. Specifically, a pre-processing module eliminates the outliers and converts categorical variables into one-hot-encoded vectors. The feature extraction module discard features with null values and selects the most significant features as input to the deep autoencoder model (trained in a greedy-wise manner). The NSL-KDD dataset from the Canadian Institute for Cybersecurity is used as a benchmark to evaluate the feasibility and effectiveness of the proposed architecture. Simulation results demonstrate the potential of our proposed system and its outperformance as compared to existing state-of-the-art methods and recently published novel approaches. Ongoing work includes further optimization and real-time evaluation of our proposed IDS.Comment: To appear in the 9th International Conference on Brain Inspired Cognitive Systems (BICS 2018

Anomaly-Based Intrusion Detection System To Detect Advanced Persistent Threats: Environmental Sustainability

In an evolving digital world, Advanced Persistent Threats (APTs) pose severe cybersecurity challenges. These extended, stealthy cyber-attacks, often elude conventional Intrusion Detection Systems (IDS). To bridge this gap, our research introduces a novel, environmentally conscious, deep learning-based IDS designed for APT detection. The system encompasses various stages from objective definition, data collection and preprocessing, to model development, integration, validation, and deployment. The system, utilizing deep learning algorithms, scrutinizes network traffic to detect patterns characteristic of APTs. This approach improves IDS accuracy and allows real-time threat detection, enabling prompt response to potential threats. Importantly, our system contributes to environmental protection by minimizing power consumption and electronic waste associated with cyberattacks, promoting sustainable cybersecurity practices. Our research outcomes are expected to enhance APT detection, providing robust defense against sophisticated cyber threats. Our environmentally-conscious perspective adds a unique dimension to the cybersecurity domain, underlining its role in sustainable practices

A FRAMEWORK FOR ARABIC SENTIMENT ANALYSIS USING MACHINE LEARNING CLASSIFIERS

International audienceIn recent years, the use of Internet and online comments, expressed in natural language text, have increased significantly. However, it is difficult for humans to read all these comments and classify them appropriately. Consequently, an automatic approach is required to classify the unstructured data. In this paper, we propose a framework for Arabic language comprising of three steps: pre-processing, feature extraction and machine learning classification. The main aim of the proposed framework is to exploit the combination of different Arabic linguistic features. We evaluate the framework using two benchmark Arabic tweets datasets (ASTD, ATA), which enable sentiment polarity detection in general Arabic and Jordanian dialects. Comparative simulation results show that machine learning classifiers such as Support Vector Machine (SVM), Naive Bayes, MultiLayer Perceptron (MLP) and Logistic Regression-based produce the best performance by using a combination of n-gram features from Arabic tweets datasets. Finally, we evaluate the performance of our proposed framework using an Ensemble classifier approach, with promising results

Паралелізація алгоритму класифікації Random Forest для пришвидшення виявлення кібератак

Дипломна робота має обсяг 60 сторінок, містить 18 рисунків, 5 таблиць, 2 додатки та 13 джерел посилань. З кожним днем кількість даних у мережі інтернет зростає в геометричній прогресії. Зростає кількість людей та час, що ці люди проводять на сторінках, де можна знайти все що завгодно. Інтернет також став основою для багатьох бізнесів, які навіть не є можливими без під’єднання мережі Інтернет. Проте виникає проблема, що завжди знайдуться особи, які прагнуть заволодіти інформацією, що їм не належить, або обмежити доступність деякого сервісу, та отримати від цього певну користь у вигляді грошей або інші блага. Одна з найпоширеніших атак на сьогоднішній день є атака DDoS, що може змушувати цілу систему вийти з ладу на деякий, іноді тривалий час. Найбільш вразливими до DDoS атак є IoT(Internet of Things) сегмент, де при виявлені атаки важлива кожна секунда. Об’єктом дослідження є боротьба з DDoS атаками. Метою дослідження є створення методики покращення виявлення DDoS атак.виникає проблема, що завжди знайдуться особи, які прагнуть заволодіти інформацією, що їм не належить, або обмежити доступність деякого сервісу, та отримати від цього певну користь у вигляді грошей або інші блага. Одна з найпоширеніших атак на сьогоднішній день є атака DDoS, що може змушувати цілу систему вийти з ладу на деякий, іноді тривалий час. Найбільш вразливими до DDoS атак є IoT(Internet of Things) сегмент, де при виявлені атаки важлива кожна секунда. Об’єктом дослідження є боротьба з DDoS атаками. Метою дослідження є створення методики покращення виявлення DDoS атак. Ї.there is a problem that there will always be people who want to take information that does not belong to them, or limit the availability of a service, and get some benefit from it in the form of money or other benefits. One of the most common attacks today is a DDoS attack, which can cause the entire system to fail for a while, sometimes for a long time. The most vulnerable to DDoS attacks is the IoT (Internet of Things) segment, where every second counts when attacks are detected. The object of research is the fight against DDoS attacks. The aim of the study is to create a methodology to improve the detection of DDoS attacks.The work consists of 60 pages, has 18 illustrations, 5 tables, 2 appendices and 13 references. Every day the amount of data on the Internet grows exponentially. The number of people and the time that these people spend on pages where you can find anything. The Internet has also become the basis for many businesses that are not even possible without an Internet connection. However, there is a problem that there will always be people who want to take information that does not belong to them, or limit the availability of a service, and get some benefit from it in the form of money or other benefits. One of the most common attacks today is a DDoS attack, which can cause the entire system to fail for a while, sometimes for a long time. The most vulnerable to DDoS attacks is the IoT (Internet of Things) segment, where every second counts when attacks are detected. The object of research is the fight against DDoS attacks. The aim of the study is to create a methodology to improve the detection of DDoS attacks

Extending persian sentiment lexicon with idiomatic expressions for sentiment analysis

Nowadays, it is important for buyers to know other customer opinions to make informed decisions on buying a product or service. In addition, companies and organizations can exploit customer opinions to improve their products and services. However, the Quintilian bytes of the opinions generated every day cannot be manually read and summarized. Sentiment analysis and opinion mining techniques offer a solution to automatically classify and summarize user opinions. However, current sentiment analysis research is mostly focused on English, with much fewer resources available for other languages like Persian. In our previous work, we developed PerSent, a publicly available sentiment lexicon to facilitate lexicon-based sentiment analysis of texts in the Persian language. However, PerSent-based sentiment analysis approach fails to classify the real-world sentences consisting of idiomatic expressions. Therefore, in this paper, we describe an extension of the PerSent lexicon with more than 1000 idiomatic expressions, along with their polarity, and propose an algorithm to accurately classify Persian text. Comparative experimental results reveal the usefulness of the extended lexicon for sentiment analysis as compared to PerSent lexicon-based sentiment analysis as well as Persian-to-English translation-based approaches. The extended version of the lexicon will be made publicly available