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 Research and Development (Academic-Industry Joint Projects)
 Affiliation: University of New Brunswick (UNB) and Atlantic Innovation Fund (AIF), Canada
 Title: design and development of an automated intelligent tool-set to assess risks and mitigate security threats for large-scale systems
 Type: R&D (My Postdoctoral fellowship)
  Outcomes: Publications , Risk Management Application (Assessment, Analysis, and Mitigation)
 Affiliation: University of New Brunswick (UNB)
 Title: IDS/IPS
 Type: R&D
  Outcomes: Publications
 Abstract:With exponential growth in the size of computer networks and developed applications, the significant increasing of the potential damage that can be caused by launching attacks is becoming obvious. Meanwhile, Intrusion Detection Systems (IDSs) and Intrusion Prevention Systems (IPSs) are one of the most important defense tools against the sophisticated and ever-growing network attacks. Due to the lack of adequate dataset, anomaly-based approaches in intrusion detection systems are suffering from accurate deployment, analysis and evaluation. There exist a number of such datasets such as DARPA98, KDD99, ISC2012, and ADFA13 that have been used by the researchers to evaluate the performance of their proposed intrusion detection and intrusion prevention approaches. Based on our study over eleven available datasets since 1998, many such datasets are out of date and unreliable to use. Some of these datasets suffer from lack of traffic diversity and volumes, some of them do not cover the variety of attacks, while others anonymized packet information and payload which cannot reflect the current trends, or they lack feature set and metadata. This project produces a reliable dataset that contains benign and most of common attack network flows, which meets real world criteria and is publicly avaliable. Consequently, the project evaluates the performance of a comprehensive set of network traffic features and machine learning algorithms to indicate the best set of features for detecting the certain attack categories.
 Researchers:Arash Habibi Lashkari, Iman Sharafaldin, Amirhossein Gharib, Ali Ghorbani
 Affiliation: University of New Brunswick (UNB), Canada
 Title: Android malware Detection and Classification
 Type: R&D
  Outcomes: Publications , Adware Dataset
 Abstract:Recently, usage of smartphones has increased and Android has invoked as an efficient and practical mobile operating system (OS) for mobile devices. However, the recent widespread tendency to Android devices makes them one of the most essential targets for malicious intent, and a motivation for exploiting Android OS vulnerabilities. In fact, thinking that all of the applications that users download from well known Android, third party markets are benign is wrong. In Android security mechanism, users must grant permissions before installation of applications. Otherwise, access to resources will be denied. The fact that unprofessional users make the final decision to grant access is a serious drawback for this security mechanism. Numerous researches have been conducted on Android malware detection to prevent unauthorized access to users’ essential and private information. In this effort, we analyze a comprehensive Android malware dataset, which includes a collection of runtime captured behavior of malware samples in different categories and families. Based on this analysis, we extract discriminative features and try to define a common pattern malware behavior for detecting and classifying malware using AI techniques.
 Researchers:Arash Habibi Lashkari, Andi Fitriah A.Kadir, Hugo Gonzalez, Laya Taheri
 Affiliation: University of New Brunswick (UNB), Univerisyt of Malaya, Binary University College
 Title: Botnet Detection and Visualization
 Type: R&D
  Outcomes: Publications
 Abstract:Nowadays, there are many serious cyber security threats such as viruses, worms and trojans but without a doubt botnets are one of the largest threats. Although there are numerous ways to discover botnets and mitigate their effects, most methods have problems effecting detection, due to their evasive characteristics. Also, the majority of previous research uses only one data source (e.g. network traffic), which makes the botnet detection process very difficult over a network. This project proposes a taxonomy, detection and visualization system based on traffic and memory forensic analysis.
 Researchers:Arash Habibi Lashkari, Iman Sharafaldin, Amirhossein Gharib, Seyedeh Ghazal ghalebandi
 Affiliation: University of New Brunswick (UNB), Canada
 Title: Dark Web Detection and Classificaiton
 Type: R&D
  Outcomes: Publications , Traffic Analyser
 Abstract:Darkweb detection and classification has been the topic of many research efforts, but the quick evolution of Internet services and the pervasive use of encryption makes it an open challenge. Encryption is essential in protecting the privacy of Internet users, a key technology used in the different privacy enhancing tools that have appeared in the recent years. There are different techniques such as Tor, that decouple the sender from the receiver by encrypting the traffic between them, and routing it through a distributed network of servers and VPN that allows user to create a secure connection to another network over the Internet. In this project, we present a time-based analysis on Tor and VPN traffic flows, captured between the client and the entry node. We define two scenarios, one to detect Tor and VPN traffic flows and the other to detect the application type: Browsing, Chat, Streaming, Mail, Voip, P2P or File Transfer. In addition, with this project we publish the Tor and VPN labelled datasets we generated and used to test our classifiers. In future, we will focus on the multi layers encrypted communications such as Tor+VPN.
 Researchers:Arash Habibi Lashkari, Gerard Draper Gill, Mohammad Mamun
  Affiliation: Personal
 Title: "A Secure Graphical Password for mobiles and tablets”
 Type: R&D (International Telecommunication Union (ITU))
 Outcomes: Project nominated for a WSIS Project Prize 2014 (Mobile Application)
 Abstract: Undoubtedly, there is currently the phenomenon of threats at the threshold of the internet, internal networks and secure environments. Although security researchers have made great strides in fighting these threats by protecting systems, individual users and digital assets, unfortunately the threats continue to cause problems. The principle area of attack is AUTHENTICATION, which is of course the process of determining the accessibility of a user to a particular resource or system. Today, passive or active users are the key consideration of security mechanisms. The passive user is only interested in understanding the system. The active user, on the other hand, will consider and reflect on ease of use, efficiency, memorability, effectiveness and satisfaction of the system. One of the major problems of the textual password is the difficulty of remembering passwords. A survey has shown that most of the users tend to select short passwords or passwords that are easy to remember which unfortunately, can be easily guessed or broken by attackers.
 Researchers:Arash Habibi Lashkari, Mohammadreza Moradhaseli
 Affiliation: University Technology Malasyia (UTM), University of Malaya, and Research University Grant(RUG-Tier1), Malaysia
 Title: "Graphical User Authentication based on Steganography”
 Type: R&D (Part of my PhD thesis)
 Outcomes: Publications, Medals and Awards , Patent (under process), Password Hiding Software for Smart Phones
 Abstract: Without a doubt, “user authentication” is the most critical element in the field of information security. Despite the general usage of “text based passwords”, this sort of authentication has shown difficulties, such as user problems memorizing strong password. In these situations, users write passwords down manually or save them on a hard disk, which inadvertently gives rise to security issues and increases vulnerability to attacks. For “graphical user authentication” or “graphical passwords”, security is the bedrock of its foundation. The main idea behind this authentication is that psychologically, humans remember images more easily and for a longer period than text. Although there has been some research into making graphical passwords withstand attacks, most if not all algorithms are still susceptible to brute force, dictionary and manipulation attacks. From three categories of graphical passwords, “Recognition-based algorithms”, two concepts can guarantee the schema’s security; firstly, increasing the password space to make it resistant to brute force and dictionary attacks, and secondly, hiding user passwords in images to make it resistant to manipulation attacks. The main objective of this research is proposing a new optimal steganography technique for embedding user ids and passwords in images. Another objective is to propose a new graphical password algorithm based on rotation and resizing to achieve the highest password space. Also, a new evaluation method will be proposed based on the insufficiencies of current method in terms of attacks, passwords space and password entropy. The result of evaluation shows that the proposed algorithm is 25 percent more robust and secure than current algorithms in brute force and manipulation attacks.
 Researchers:Arash Habibi Lashkari, Azizah Abdul Manaf, Maslin Masroom, Rosli Saleh
 Affiliation: LUCT University, Malaysia
 Title: "Augmented Reality (AR)”
 Type: R&D
 Outcomes: Publications, Medals and Awards , Applications
 Abstract: Due to the increase of interest in Augmented Reality (AR), the potential uses of AR are increasing also. It can benefit the user in various fields such as education, business, medicine, and other. Augmented Reality supports the real environment with synthetic environment to give more details and meaning to the objects in the real word. AR refers to a situation in which the goal is to supplement a user’s perception of the real-world through the addition of virtual objects. In this project, we proposed and developed more than 12 small and medium ideas and solutions for cell-phone and desktop platforms.
 Researchers:Arash Habibi Lashkari, Behrang Parhizkar, Ashraf Abbas M. Al-Modwahi, Hossein Reza Babaei, ZM Gebril
 Affiliation: University Malaya (UM), Malaysia
 Title: "Graphical Password by Rotation and Resizing (GUABR2)”
 Type: R&D (Part of my master thesis)
 Outcomes: Publications , Medals and Awards , Patent (under process) , One-time usage passwords generator application
  Abstract: It is now beyond any doubt that USER AUTHENTICATION is the most critical element in the field of Information Security. To date, Text Based Password Authentication (TBPA) has shown some difficulties that users have tended to write passwords down manually or save them on hard disc. This tendency is caused by the passwords being strong and thus difficult to memorize in most cases. This has inadvertently given rise to security issues pertaining to attack. Graphical User Authentication (GUA) has two symbiotic pillars as its foundation: USABILITY & SECURITY.
 Affiliation: University Malaya (UM) and Elecomp Co. (Belkin's wireless Products), Malaysia
 Title: "Wireless Security Protocols (WEP, WPA1, WPA2)”
 Type: Collaboration Research (Research methodology module of my master degree)
 Outcomes: Publications
 Abstract: There are some demonstrable reasons for customers who like use from wireless technology and this is clear because there are various benefits for using wireless technology. The contrast between wireless usage and security techniques growing, show that the security is not adequate enough for this data growing. It’s obvious that the hackers are able to monitor the transmitted data and hack whatever they want. So we see that these days Companies are investing more money on securing their wireless networks. There are three major type of security in wireless...
 Affiliation: NBB Group, Iran
 Title: "Document Follow-up System (DFS) for project management”
 Type: R&D
 Outcome(s): Project Control Software