Biblio

Cybercrime is growing dramatically in the technological world nowadays. World Wide Web criminals exploit the personal information of internet users and use them to their advantage. Unethical users leverage the dark web to buy and sell illegal products or services and sometimes they manage to gain access to classified government information. A number of illegal activities that can be found in the dark web include selling or buying hacking tools, stolen data, digital fraud, terrorists activities, drugs, weapons, and more. The aim of this project is to collect evidence of any malicious activity in the dark web by using computer security mechanisms as traps called honeypots.

More than 75% of the Internet traffic is now encrypted, while this percentage is constantly increasing. The majority of communications are secured using common encryption protocols such as SSL/TLS and IPsec to ensure security and protect the privacy of Internet users. Yet, encryption can be exploited to hide malicious activities. Traditionally, network traffic inspection is based on techniques like deep packet inspection (DPI). Common applications for DPI include but are not limited to firewalls, intrusion detection and prevention systems, L7 filtering and packet forwarding. The core functionality of such DPI implementations is based on pattern matching that enables searching for specific strings or regular expressions inside the packet contents. With the widespread adoption of network encryption though, DPI tools that rely on packet payload content are becoming less effective, demanding the development of more sophisticated techniques in order to adapt to current network encryption trends. In this work, we present HeaderHunter, a fast signature-based intrusion detection system even in encrypted network traffic. We generate signatures using only network packet metadata extracted from packet headers. Also, to cope with the ever increasing network speeds, we accelerate the inner computations of our proposed system using off-the-shelf GPUs.

The fast growing of ransomware attacks has become a serious threat for companies, governments and internet users, in recent years. The increasing of computing power, memory and etc. and the advance in cryptography has caused the complicating the ransomware attacks. Therefore, effective methods are required to deal with ransomwares. Although, there are many methods proposed for ransomware detection, but these methods are inefficient in detection ransomwares, and more researches are still required in this field. In this paper, we have proposed a novel method for identify ransomware from benign software using process mining methods. The proposed method uses process mining to discover the process model from the events logs, and then extracts features from this process model and using these features and classification algorithms to classify ransomwares. This paper shows that the use of classification algorithms along with the process mining can be suitable to identify ransomware. The accuracy and performance of our proposed method is evaluated using a study of 21 ransomware families and some benign samples. The results show j48 and random forest algorithms have the best accuracy in our method and can achieve to 95% accuracy in detecting ransomwares.

Keeping Internet users safe from attacks and other threats is one of the biggest security challenges nowadays. Distributed Denial of Service (DDoS) [1] is one of the most common attacks. DDoS makes the system stop working by resource overload. Software Define Networking (SDN) [2] has recently emerged as a new networking technology offering an unprecedented programmability that allows network operators to dynamically configure and manage their infrastructures. The flexible processing and centralized management of SDN controller allow flexibly deploying complex security algorithms and mitigation methods. In this paper, we propose a new TCP-SYN flood attack mitigation in SDN networks using machine learning. By using a testbed, we implement the proposed algorithms, evaluate their accuracy and address the trade-off between the accuracy and capacity of the security device. The results show that the algorithms can mitigate TCP-SYN Flood attack over 96.

As the number of Internet users increases, their usage also diversifies, and it is important to prevent Identity on the Internet (Digital Identity) from being violated. Unauthorized authentication is one of the methods to infringe Digital Identity. Multi-factor authentication has been proposed as a method for preventing unauthorized authentication. However, the cryptographic authenticator required for multi-factor authentication is expensive both financially and UX-wise for the user. In this paper, we design, implement and evaluate multi-factor authentication using My Number Card provided by public personal identification service and WebUSB, which is being standardized.

Question Answering system(QA) plays an increasingly important role in the Internet age. The proportion of using the QA is getting higher and higher for the Internet users to obtain knowledge and solve problems, especially in the modern agricultural filed. However, the answer quality in QA varies widely due to the agricultural expert's level. Answer quality assessment is important. Due to the lexical gap between questions and answers, the existing approaches are not quite satisfactory. A practical approach RCAS is proposed to rank the candidate answers, which utilizes the support sets to reduce the impact of lexical gap between questions and answers. Firstly, Similar questions are retrieved and support sets are produced with their high-quality answers. Based on the assumption that high quality answers would also have intrinsic similarity, the quality of candidate answers are then evaluated through their distance from the support sets. Secondly, Different from the existing approaches, previous knowledge from similar question-answer pairs are used to bridge the straight lexical and semantic gaps between questions and answers. Experiments are implemented on approximately 0.15 million question-answer pairs about agriculture, dietetics and food from Yahoo! Answers. The results show that our approach can rank the candidate answers more precisely.

Internet users are increasing day by day. The web services and mobile web applications or desktop web application's demands are also increasing. The chances of a system being hacked are also increasing. All web applications maintain data at the backend database from which results are retrieved. As web applications can be accessed from anywhere all around the world which must be available to all the users of the web application. SQL injection attack is nowadays one of the topmost threats for security of web applications. By using SQL injection attackers can steal confidential information. In this paper, the SQL injection attack detection method by removing the parameter values of the SQL query is discussed and results are presented.

Nowadays, cyber attacks affect many institutions and individuals, and they result in a serious financial loss for them. Phishing Attack is one of the most common types of cyber attacks which is aimed at exploiting people's weaknesses to obtain confidential information about them. This type of cyber attack threats almost all internet users and institutions. To reduce the financial loss caused by this type of attacks, there is a need for awareness of the users as well as applications with the ability to detect them. In the last quarter of 2016, Turkey appears to be second behind China with an impact rate of approximately 43% in the Phishing Attack Analysis report between 45 countries. In this study, firstly, the characteristics of this type of attack are explained, and then a machine learning based system is proposed to detect them. In the proposed system, some features were extracted by using Natural Language Processing (NLP) techniques. The system was implemented by examining URLs used in Phishing Attacks before opening them with using some extracted features. Many tests have been applied to the created system, and it is seen that the best algorithm among the tested ones is the Random Forest algorithm with a success rate of 89.9%.

Language vector space models (VSMs) have recently proven to be effective across a variety of tasks. In VSMs, each word in a corpus is represented as a real-valued vector. These vectors can be used as features in many applications in machine learning and natural language processing. In this paper, we study the effect of vector space representations in cyber security. In particular, we consider a passive traffic analysis attack (Website Fingerprinting) that threatens users' navigation privacy on the web. By using anonymous communication, Internet users (such as online activists) may wish to hide the destination of web pages they access for different reasons such as avoiding tyrant governments. Traditional website fingerprinting studies collect packets from the users' network and extract features that are used by machine learning techniques to reveal the destination of certain web pages. In this work, we propose the packet to vector (P2V) approach where we model website fingerprinting attack using word vector representations. We show how the suggested model outperforms previous website fingerprinting works.

Language vector space models (VSMs) have recently proven to be effective across a variety of tasks. In VSMs, each word in a corpus is represented as a real-valued vector. These vectors can be used as features in many applications in machine learning and natural language processing. In this paper, we study the effect of vector space representations in cyber security. In particular, we consider a passive traffic analysis attack (Website Fingerprinting) that threatens users' navigation privacy on the web. By using anonymous communication, Internet users (such as online activists) may wish to hide the destination of web pages they access for different reasons such as avoiding tyrant governments. Traditional website fingerprinting studies collect packets from the users' network and extract features that are used by machine learning techniques to reveal the destination of certain web pages. In this work, we propose the packet to vector (P2V) approach where we model website fingerprinting attack using word vector representations. We show how the suggested model outperforms previous website fingerprinting works.