Secure Wireless Infrastructure Network Using Access Point Checking

Developments in computers, communication and networks has opened up the doors for wireless network evolution which enjoys attractive features such as dynamic communication and the ease of members to join the network. Improvements in wireless technology has increased the needed for more complicated security systems, where data security and protection represent main wireless networks features. In distributed systems, the use of networks and standard communication protocols facilitate data transmission between a terminal user and a computer - and between a computer and another computer. Network security measures the need to protect data during transmission. Clearly, wireless networks are less secure compared to wired networks. So, the most important question here is how to protect data transmission in wireless networks. In this work, we briefly glance at network classes and existing security mechanisms. We then propose our new access point checking algorithm to increase security over infrastructure wireless networks. The goal is to save the time consumed during message travel from one host to another in the network, while maintaining message security. We employ a checksum mechanism to enhance message integrity. In addition, access point (AP) will check the message and decide whether the message should be sent back to the original sender or not. Experimental results for different networking scenarios are provided to validate the system ability. Our technique outperforms traditional security mechanisms in terms of timing characteristics

Machine-assisted Cyber Threat Analysis using Conceptual Knowledge Discovery

Over the last years, computer networks have evolved into highly dynamic and interconnected environments, involving multiple heterogeneous devices and providing a myriad of services on top of them. This complex landscape has made it extremely difficult for security administrators to keep accurate and be effective in protecting their systems against cyber threats. In this paper, we describe our vision and scientific posture on how artificial intelligence techniques and a smart use of security knowledge may assist system administrators in better defending their networks. To that end, we put forward a research roadmap involving three complimentary axes, namely, (I) the use of FCA-based mechanisms for managing configuration vulnerabilities, (II) the exploitation of knowledge representation techniques for automated security reasoning, and (III) the design of a cyber threat intelligence mechanism as a CKDD process. Then, we describe a machine-assisted process for cyber threat analysis which provides a holistic perspective of how these three research axes are integrated together

Development of protection mechanisms against DRDoS-attacks and combined DRDoS-attacks

Distributed “denial of service” attacks based on the traffic reflection and amplification (DRDoS attacks) still are a powerful threat for computer networks. More than half of all attacks were executed by using multiple types of attacks. Development of new protection mechanisms against such attacks is one of the most important tasks in the field of computer security. In this paper, we present experiments on DNS attack, NTP attacks and combined DRDoS-attack simulation. We simulated several protection mechanisms as well as a mechanism developed by us. We compared these protection mechanisms for different kinds of attacks

Internetworking: integrating IP/ATM LAN/WAN security

Computer and network security is a complex problem that is not solely restricted to classified computer systems and networks. Accelerating trends in networking and the emphasis on open interoperable networks has left many unclassified systems vulnerable to a wide variety of attacks. Computer and network professionals must understand the scope of security, recognize the need for security even in unclassified systems, and then take appropriate action to protect their systems. Transmission of static passwords in plaintext over the Internet is one of the most widely publicized network vulnerabilities. Cue-time password mechanisms (such as S-Key) or other secure network access mechanisms (such as Kerberos) have been recommended to improve access security for computer systems connected to the Internet. This thesis examines many of the issues that must be addressed when assessing the need for computer and network security. This work provides the results of a site security survey for the unclassified IP/ATM LAN in the Systems Technology Lab (STL) at the Naval Postgraduate School (NPS). These results highlight new security vulnerabilities and strengths that occur when standard Internet Protocol (P) local-area networks (LANs) are internetworked with Asynchronous Transfer Mode (ATM) wide-area networks (WANs). Finally, we examine the feasibility of using the Kerberos authentication protocol for remote plaintext password protection and provide recommendations for additional workhttp://archive.org/details/internetworkingi00dennLieutenant, United States NavyApproved for public release; distribution is unlimited

Adversarial Attacks on Deep Neural Networks for Time Series Classification

Time Series Classification (TSC) problems are encountered in many real life data mining tasks ranging from medicine and security to human activity recognition and food safety. With the recent success of deep neural networks in various domains such as computer vision and natural language processing, researchers started adopting these techniques for solving time series data mining problems. However, to the best of our knowledge, no previous work has considered the vulnerability of deep learning models to adversarial time series examples, which could potentially make them unreliable in situations where the decision taken by the classifier is crucial such as in medicine and security. For computer vision problems, such attacks have been shown to be very easy to perform by altering the image and adding an imperceptible amount of noise to trick the network into wrongly classifying the input image. Following this line of work, we propose to leverage existing adversarial attack mechanisms to add a special noise to the input time series in order to decrease the network's confidence when classifying instances at test time. Our results reveal that current state-of-the-art deep learning time series classifiers are vulnerable to adversarial attacks which can have major consequences in multiple domains such as food safety and quality assurance.Comment: Accepted at IJCNN 201