Ahmad Y. Javaid

Cyber security threat analysis and modeling of an unmanned aerial vehicle system

Advances in technology for miniature electronic military equipment and systems have led to the emergence of unmanned aerial vehicles (UAVs) as the new weapons of war and tools used in various other areas. UAVs can easily be controlled from a remote location. They are being used for critical operations, including offensive, reconnaissance, surveillance and other civilian missions. The need to secure these channels in a UAV system is one of the most important aspects of the security of this system because all information critical to the mission is sent through wireless communication channels. It is well understood that loss of control over these systems to adversaries due to lack of security is a potential threat to national security. In this paper various security threats to a UAV system is analyzed and a cyber-security threat model showing possible attack paths has been proposed. This model will help designers and users of the UAV systems to understand the threat profile of the system so as to allow them to address various system vulnerabilities, identify high priority threats, and select mitigation techniques for these threats.

UAVSim: A simulation testbed for unmanned aerial vehicle network cyber security analysis

Increased use of unmanned systems in various tasks enables users to complete important missions without risking human lives. Nonetheless, these systems pose a huge threat if the operational cyber security is not handled properly, especially for the unmanned aerial vehicle systems (UAVS), which can cause catastrophic damages. Therefore, it is important to check the impact of various attack attempts on the UAV system. The most economical and insightful way to do this is to simulate operational scenarios of UAVs in advance. In this paper, we introduce UAVSim, a simulation testbed for Unmanned Aerial Vehicle Networks cyber security analysis. The testbed allows users to easily experiment by adjusting different parameters for the networks, hosts and attacks. In addition, each UAV host works on well-defined mobility framework and radio propagation models, which resembles real-world scenarios. Based on the experiments performed in UAVSim, we evaluate the impact of Jamming attacks against UAV networks and report the results to demonstrate the necessity and usefulness of the testbed.

An efficient compression scheme based on adaptive thresholding in wavelet domain using particle swarm optimization

Image compression is one of the most important research areas in the field of image processing due to its large number of applications such as aerial surveillance, reconnaissance, medicine and multimedia communication. Even when high data rates are available, image compression is necessary in order to reduce the transmission cost. For applications involving information security, a fast delivery also reduces the chances of compromise over a communication channel. In this paper, we explore the possibility of using one of the computational intelligence techniques, namely, Particle Swarm Optimization (PSO), for optimal thresholding in the 2-D discrete wavelet transform (DWT) of an image. To this end, a set of optimal thresholds is obtained using the PSO algorithm. Finally, a variable length coding scheme, such as arithmetic coding is used to encode the results. Finding an optimal threshold value for the wavelet coefficients is very crucial in reducing the source entropy and bit-rate reduction. The proposed method is tested using several standard images against other popular techniques and proved to be more efficient compared to other methods. HighlightsAn evolutionary optimization technique is devised for thresholding the wavelet coefficients.PSO is used to find the optimum thresholds for various sub-bands in the wavelet domain.The method is adaptive in the sense that a single threshold is not used for all sub-bands.A VLC coding scheme is used to efficiently compress the wavelet coefficients.

Efficient spam detection across Online Social Networks

Online Social Networks (OSNs) have become more and more popular in the whole world. People share their personal activities, views and opinions among different OSNs. At the same time, social spam appears more frequently and in various formats throughout popular OSNs. Therefore, efficient detection of spam has become an important and popular problem. This paper focuses on spam detection across multiple online social networks by leveraging the knowledge of detecting similar spam within a social network and using it in different networks. We chose Facebook and Twitter for our study targets, considering that they share the most similar features in posts, topics, and user activities, etc. We collected two datasets from them and performed analysis based on our proposed methodology. The results show that detection combined with spam in Facebook show a more than 50% decrease of spam tweets in Twitter, and detection combined with spam of Twitter shows a nearly 71.2% decrease of spam posts in Facebook. This means similar spam of one social network can greatly facilitate spam detection in other social networks. We proposed a new perspective of spam detection in OSNs.

A Deep Learning Based DDoS Detection System in Software-Defined Networking (SDN)

Distributed Denial of Service (DDoS) is one of the most prevalent attacks that an organizational network infrastructure comes across nowadays. We propose a deep learning based multi-vector DDoS detection system in a software-defined network (SDN) environment. SDN provides flexibility to program network devices for different objectives and eliminates the need for third-party vendor-specific hardware. We implement our system as a network application on top of an SDN controller. We use deep learning for feature reduction of a large set of features derived from network traffic headers. We evaluate our system based on different performance metrics by applying it on traffic traces collected from different scenarios. We observe high accuracy with a low false-positive for attack detection in our proposed system.

Distributed network traffic feature extraction for a real-time IDS

Internet traffic as well as network attacks have been growing rapidly that necessitates efficient network traffic monitoring. Many efforts have been put to address this issue; however, rapid monitoring applications are needed. We propose a distributed architecture based intrusion detection system (IDS) that is capable of detecting the anomalies in the network in real-time. To achieve this, we exploit the Apache Spark framework and Netmap- a line-rate packet capturing tool. In this work, we implement one of the challenging modules of an IDS, i.e., feature extraction, and present the computational results of the same for TCP-based traffic. Related results are presented along with the insight gained for future work.

UAVNet Simulation in UAVSim: A Performance Evaluation and Enhancement

Several works have been done to design a simulation testbed for unmanned aerial vehicles (UAVs) in order to simulate the UAV Network (UAVNet) in a cost-effective manner. Our previously developed UAVSim is one of those attempts and has the capability of simulating large UAV networks as well while giving detailed results in terms of mobility modeling, traffic measurements, attack analysis, etc. The usefulness of such a simulation testbed cannot be guaranteed unless it is hardware independent. Therefore, we present a performance evaluation of such a recently developed software simulation testbed, UAVSim, using traditional and generic hardware available in any regular computer laboratory, in order to show its usefulness in an academic research setup. We show performances for two different environments for two separate machines. Results show that the simulation time is quite predictable and reasonable for a particular network size.

Facial Emotion Recognition: A Survey and Real-World User Experiences in Mixed Reality

Extensive possibilities of applications have made emotion recognition ineluctable and challenging in the field of computer science. The use of non-verbal cues such as gestures, body movement, and facial expressions convey the feeling and the feedback to the user. This discipline of Human–Computer Interaction places reliance on the algorithmic robustness and the sensitivity of the sensor to ameliorate the recognition. Sensors play a significant role in accurate detection by providing a very high-quality input, hence increasing the efficiency and the reliability of the system. Automatic recognition of human emotions would help in teaching social intelligence in the machines. This paper presents a brief study of the various approaches and the techniques of emotion recognition. The survey covers a succinct review of the databases that are considered as data sets for algorithms detecting the emotions by facial expressions. Later, mixed reality device Microsoft HoloLens (MHL) is introduced for observing emotion recognition in Augmented Reality (AR). A brief introduction of its sensors, their application in emotion recognition and some preliminary results of emotion recognition using MHL are presented. The paper then concludes by comparing results of emotion recognition by the MHL and a regular webcam.

Performance evaluation of VoIP broadcasting over LTE for varying speeds and distances of mobile nodes

The exponential increase in smartphone use has caused a paradigm shift in the Internet access pattern from traditional applications to VoIP, video streaming, and social networking. These changes urge the demand for high-speed mobile network access technologies day-by-day. In this paper, we evaluate the performance of Long Term Evaluation (LTE) network for VoIP application, focusing primarily on downlink (broadcasting mode). We studied the implementation details of LTE from user perspective and used a network simulator, SimuLTE, to perform various simulations. We measure the VoIP QoS by varying speed, distance (from the eNodeB) and number of mobile nodes. Metrics used include packet delay, delay variation (jitter), packet loss, and Mean Opinion Score (MOS). We observed that LTE provides good VoIP quality in broadcasting mode and less affected by varying number of users, distance, and speed.

Effects of Voice-Based Synthetic Assistant on Performance of Emergency Care Provider in Training

As part of a perennial project, our team is actively engaged in developing new synthetic assistant (SA) technologies to assist in training combat medics and medical first responders. It is critical that medical first responders are well trained to deal with emergencies more effectively. This would require real-time monitoring and feedback for each trainee. Therefore, we introduced a voice-based SA to augment the training process of medical first responders and enhance their performance in the field. The potential benefits of SAs include a reduction in training costs and enhanced monitoring mechanisms. Despite the increased usage of voice-based personal assistants (PAs) in day-to-day life, the associated effects are commonly neglected for a study of human factors. Therefore, this paper focuses on performance analysis of the developed voice-based SA in emergency care provider training for a selected emergency treatment scenario. The research discussed in this paper follows design science in developing proposed technology; at length, we discussed architecture and development and presented working results of voice-based SA. The empirical testing was conducted on two groups as user studies using statistical analysis tools, one trained with conventional methods and the other with the help of SA. The statistical results demonstrated the amplification in training efficacy and performance of medical responders powered by SA. Furthermore, the paper also discusses the accuracy and time of task execution (t) and concludes with the guidelines for resolving the identified problems.