Yousef Jaradat

Measures and Countermeasures for Null Frequency Jamming of On-Demand Routing Protocols in Wireless Ad Hoc Networks

Distributed network protocols operate similar to periodic state machines, utilizing internal states and timers for network coordination, which creates opportunities for carefully engineered radio jamming to target the protocol operating periods and disrupt network communications. Such periodic attacks targeting specific protocol period/frequency of operation is referred to as Null Frequency Jamming (NFJ). Our hypothesis is that NFJ is a pervasive phenomenon in dynamic systems, including wireless ad-hoc networks. This paper aims to test the hypothesis by investigating NFJ targeted at the on-demand routing protocols for ad-hoc networks. Our mathematical analysis and simulation results show substantial degradation in end-to-end network throughput at certain null periods/frequencies, where the jamming periodicity self-synchronizes with the route-recovery cycle. We also study an effective countermeasure, randomized route-recovery periods, for eliminating the presence of predictable null frequencies and mitigating the impact of NFJ. Our analytical model and simulation results validate the effectiveness of randomized route recovery with appropriately chosen randomization ranges.

Capacity of Wireless Networks With Directed Energy Links in the Presence of Obstacles

In this paper, we study the capacity of wireless networks with directed energy (DE) links in the presence of obstacles. DE links are highly focused wireless links that can be treated as “pencil beam,” an example of which is the E-band link (71–76 and 81–86 GHz) newly made available by the FCC. The 10-GHz spectrum of E-band is 50-times that of the entire cellular spectrum and provides the much needed spectrum in today’s world of exponential growth in mobile applications. Since the performance of highly focused DE links are highly susceptible to the presence of obstacles and real-world applications typically involve obstacles, it is important to study the capacity of wireless networks with DE links in the presence of obstacles, which is the subject of this paper. In the following, we first provide a study of probability distribution of DE links in the presence of obstacles and investigate how the number, shape, and size of obstacles impact the DE link probability distribution. Then, based on the probability distribution of DE links, we derive the capacity scaling laws for wireless networks in the presence of obstacles. Furthermore, our results can be extended to obstacles with arbitrary shapes quite accurately.

Capacity of Large-Scale Wireless Networks Under Jamming: Modeling and Analyses

Distributed jamming has important applications not only in the military context but also in the civilian context, where spectrum sharing is increasingly used and inadvertent jamming becomes a reality. In this paper, we derive the capacity bounds of wireless networks in the presence of jamming. We show that when the density of jammers is higher than that of target nodes by a certain threshold, the capacity of wireless networks approaches zero as the numbers of target nodes and jammers go to infinity. This is true even when the total power of target nodes is much higher than that of the jammers. We provide the optimal communication schemes to achieve the capacity bounds. We also describe the power efficiency of wireless networks, showing that there is an optimal target node density for power-efficient network operation. Our results can provide guidance for designing optimal wireless networking protocols that have to deal with large-scale distributed jamming.

Towards Achieving Linear Capacity Scaling in Wireless Networks through Directed Energy Links

Large-scale multi-hop wireless networks have many important applications. However, Gupta and Kumar showed that the capacity of multi-hop wireless networks decreases as the number of nodes in the network increases. Subsequent research efforts to achieve linear capacity scaling have significant limitations such as long latency, high technical complexity, restricted traffic pattern, or infrastructure requirement. We propose to achieve close-to-linear (CTL) capacity scaling through the use of directed energy (DE) links such as laser communications links or highly directional pencil beam links in the EHF band in a hybrid network that also contains traditional omni-directional (OD) antenna links. Our approach has none of limitations mentioned earlier. We show that when the probability distribution of DE links follows the inverse-square law, a distributed scheme with local routing information suffice to achieve CTL capacity scaling.

The Impact of 16-bit and 32-bit ASNs Coexistence on the Accuracy of Internet AS Graph

Modeling Internet structure as an autonomous system (AS) graph has attracted researchers over years. AS graph model demonstrates the power-law distribution of the Internet. It also demonstrates the relationship between the cluster coefficient and the small-world structure of the Internet. To obtain an accurate AS graph model, the data used to generate the graph should be massive and correct. In this work, we studied the correctness of the data that is utilized to generate Internet AS graph. We conducted an experiment to measure the popularity of 32-bit AS numbers (ASNs) in the Internet. We examined the impact of the special purpose 16-bit reserved ASN AS23456 on the accuracy of the AS graph. To this end, we proposed a cleaning algorithm to correct the conflict that AS23456 produces. Our results show that the current existing method of mapping 16-bit and 32-bit ASNs reduces the fidelity of the constructed AS graph to various graph parameters.

Software Project Management: Resources Prediction and Estimation Utilizing Unsupervised Machine Learning Algorithm

Software project effort estimation is a major process in software development cycle. This process helps in decision making in resource allocation and distribution. In this work, a new effort estimation clustering method based on estimation maximization soft-clustering unsupervised machine learning algorithm is proposed. This model classifies any software project into one of four categories. An enterprise will accept to develop a software project if this project is clustered into a class that requires resources equal or less than the enterprises resources. The new model helps in decision making process in one hand and helps consumers in assigning projects to a developing enterprise in the other hand. COCOMO dataset has been used to implement, deploy and test the model. The propose model has been compared with K-means algorithm to show the differences between soft and hard clustering. The paper results show that soft-clustering has the ability to estimate efforts like any supervised machine learning algorithms.

Contention delay distribution in event driven wireless sensor networks

In this paper we derive the distribution of the total contention delay in wireless sensor networks (WSNs) tasked with detection of an intruder in a specific sensing field. We use a stochastic geometry framework to model the WSN, in which the sensor nodes send their local decisions and data to the fusion center over a shared communication channel employing a contention based MAC protocol. We first derive the mean and variance of the contention delay. Then we approximate the distribution with a negative binomial via moment matching. This statistical characterization is then used to find the optimum medium access probability to minimize both the mean and the variance of the total delay. Simulation results almost exactly follow our theoretical results.

On tackling social engineering web phishing attacks utilizing software defined networks (SDN) approach

Web phishing attacks are one of the challenging security threats. Phishing depends on humans’ behavior but not protocols and devices vulnerabilities. In this work, software defined networking (SDN) will be tailored to tackle phishing attacks. In SDN, network devices forward received packets to a central point ‘controller’ that makes decision on behalf of them. This approach allows more control and management over network devices and protocol. In this work, we propose a neural network based phishing prevention algorithm (PPA) that is implemented utilizing Ryu, an open source, SDN controller. The PPA algorithm has been tested in a home network that is constructed with HP2920-24G switch. Moreover, a phished version of Facebook, Yahoo and Hotmail login pages have been written and hosted on three different free hosting domains. PPA has detected all of the phished versions and allowed the access to real version of these services.

Null Frequency Jamming of Dynamic Routing in Wireless Ad Hoc Networks

Distributed network protocols operate similar to periodic state machines, utilizing internal states and timers, for network coordination. This creates opportunities for carefully engineered radio jamming to target the protocol operating periods and disrupt network communications. Such periodic attacks targeting specific protocol period/frequency of operation is referred to as Null Frequency Jamming (NFJ). In this paper, we investigate NFJ targeted at the on-demand route recovery procedure, which is a crucial functionality for ad-hoc network operation. We use DSR as the example routing protocol. Our mathematical analysis and simulation results show substantial degradation in network throughput at certain null frequencies, where the jamming periodicity self-synchronizes with the route recovery cycle. Using simulations, we also demonstrate an effective countermeasure, randomized route-recovery periods, for eliminating the presence of predictable null frequencies and mitigating the impact of NFJ.