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For an unmanned aerial system (UAS) to operate successfully, it has to communicate reliably with different entities within its network. These entities include other aircraft and UAS, ground control stations, navigation satellite systems, and air traffic control systems. However, these systems are vulnerable to different cyber-attacks including message injection, message modification, jamming, and GPS spoofing. These attacks can mislead not only UAS and aircraft, but also pilots and air traffic controllers, leading potentially to collisions and human casualties. Over the last decade, a number of technological solutions for multiple UAS problems have been developed. Yet, few works related to UAS cybersecurity have been done. This paper provides an overview of UAS vulnerabilities, cyber-attacks, and the state-of-the-art in detection and countermeasure techniques. Attacks are classified according to their types and defense strategies for the most important attacks are discussed and compared.

Breast cancer is currently one of the leading causes of death among women worldwide. Masses are considered significant signs of the existence of malignant lesions, as they occur in most breast cancer cases. However, their detection is challenging since masses have large variation in shape, margin, size and are often indistinguishable from surrounding tissue, making the radiologist's task tedious in the case where a significant number of mammograms require fast and accurate interpretation. For these reasons, computer-aided diagnosis (CAD) systems are being developed to make the diagnostic process easier for radiologists. In these systems, segmentation and classification of breast masses in mammograms are important steps. This work aims to evaluate the performance of machine learning techniques in classifying tumors into benign and malignant. The selected techniques were applied on 1663 mammograms from the Digital Database for Screening Mammography. Of the 1663 images, 769 images correspond to malignant cases, and 894 correspond to benign cases. The efficiency of each of the considered techniques was evaluated by using four metrics, namely, the false positive rate, sensitivity, specificity, and accuracy.

Smart grid is an emerging technology that delivers intelligently to the end-users through two-way communication. However, this technology can be subject to several cyber-attacks due to this network's inherent weaknesses. One practical solution to secure smart grid networks is using an intrusion detection system (IDS). IDS improves the smart grid’s security by detecting malicious activities in the network. However, existing systems have several shortcomings, such as a low detection rate and high false alarm. For this purpose, several studies have focused on addressing these issues, using techniques, including traditional machine learning models. In this paper, we investigate the performance of three different ensemble learning techniques: bagging-based, boosting-based, and stacking-based. Their results are compared to those of three traditional machine learning techniques, namely K nearest neighbor, decision tree, and Naive Bayes. To train, evaluate, and test the proposed methods. We used the benchmark of CICDDos 2019 that consists of several DDoS attacks. Two feature selection techniques are used to identify the most important features. The performance evaluation is based on the probability of detection, probability of false alarm, probability of miss detection, and accuracy. The simulation results show that the stacking-based ensemble learning techniques outperform the other algorithms in terms of the four-evaluation metrics.

Alzheimer's disease (AD) affects fifty million people worldwide and is the sixth cause of death in the United States. However, there is no cure or treatment for patients with AD; thus, it is important to detect this disease at an early stage to improve patients' lives qualities. Several studies have been proposed to detect and differentiate between different AD groups, although most of these works only focused on differentiating between healthy people and people with Alzheimer's. These studies also did not identify the most reliable biomarkers to provide more accurate results and did not use the best hyperparameters to provide optimal results. To address these issues, we developed a model that leads to a better performance in differentiating between healthy people (cognitively normal), people with mild cognitive impairment, and people with Alzheimer’s disease. For this purpose, we combined a stacking-based ensemble learning, consisting of four traditional classifiers, with a hyperparameter tuning technique, a genetic algorithm. The model was evaluated in terms of accuracy, precision, recall, and F1-score. The simulation results show that stacking-based ensemble learning, using genetic algorithm, provides 96.7% accuracy, 96.5% recall, 97.9% precision, and 97.1% F1-score in differentiating between CN, MCI, and AD groups.

Social Engineering outweighs any other security threats as it has proven to be one of the easiest, cheapest, and most potent and highly successful ways for criminals to achieve their ends. We have witness how globally governments, organizations and institutions down to every individual have embraced the state-of-the-art technological advances and interconnectedness brought about by networks and telecommunications. We all together experience the ease and comfort of these modern living; however, many malicious actors also identified these technological vehicles as a means to selfishly benefit themselves. Cybercriminals have found humans to be an easy prey to victimize and fall for their intent. Hence, we have presented the significance of mitigating this type of attack in order to give precaution on the danger of what social engineering can ensue. Following the quote of Sun Tzu "Know Your Enemy", the initial step to plan and perform for an effective defense strategy is similar to the military operations, which is to do a reconnaissance Synthesis analysis using the rapid synthesis approaches to systematically identify the primary studies and have followed an orderly series of steps in order to accomplish our exploration. Applying the same technique, we have investigated the current existing social engineering attacks and countermeasures and performed a distillation of some selected studies regarding attacks in social engineering.

This paper discusses how visualization and machine learning models can be effectively used to track and forecast trap counts of Culex Tarsalis, female mosquitoes responsible for spreading the West Nile Virus (WNV). This paper applies four different machine learning models namely, Support vector machines (SVM), Regression tree (RT), Partial Least Square Regression (PLSR), and a hybrid combination of SVM-PLSR to multi-year WNV data sets. Precisely, historical data sets ranging from 2005–2015 was used to predict trap counts for 2016. This paper also discusses a tree-based data visualization technique for displaying historical trap counts. The visualization model is designed to focus on identifying trends in the behavior of Culex tarsalis by tracking parameters such as meteorological data, dead birds, WNV cases, human cases, and deaths. The preliminary results indicate that SVM model outperforms interms of accuracy than other machine learning models. Keywords: Culex Tarsalis, Partial Least Squares Regression (PLSR), Support Vector Machines(SVM), SVM-PLSR, West Nile Virus (WNV), Vector Control.

Cognitive radio is a promising technology proposed to solve the scarcity of the radio spectrum by opportunistically allocating the idle channels of the licensed users to unlicensed ones. The effectiveness of the cognitive radio is highly dependent on fair and efficient management of the access to the unused portion of the frequency channels, which is performed by the media access control layer. Therefore, any malicious activities disrupting operation of this layer result in significant performance degradation of the cognitive radio networks. As a result, it is necessary to understand functions of the media access control layer of the cognitive radio and analyze the impacts of possible attacks these functions might encounter. This paper provides an overview of different attacks that target the media access control layer of cognitive radio and analyzes them based on this layer's functions. In addition, the paper describes and compares recent defense strategies related to each attack.

The U.S. Federal Aviation Administration has mandated the use of the Automatic Dependent Surveillance-Broadcast (ADS-B) system by January 2020 as a key component of the NextGen Project, which is intended to upgrade the air traffic control infrastructure and operations. The ADS-B system seeks to replace legacy approaches such as primary and secondary radars by employing global satellite navigation systems to generate precise air pictures for air traffic management. The security of ADS-B is a major concern because the system broadcasts detailed information about aircraft, their positions, velocities and other data over unencrypted data links, making it easy to launch eavesdropping, jamming and message modification attacks on aircraft in flight. This paper discusses ADS-B vulnerabilities and attacks that leverage the ADS-B protocol stack. The paper also presents the security requirements, state-of-the-art attack detection techniques and countermeasures, along with an overall risk analysis of the ADS-B system.

The scarcity of the radio spectrum has motivated a search for more optimal and efficient spectrum management methods. One of these methods is spectrum sharing, which multiplies the number of devices that can use this resource without causing harmful interference to licensees. Spectrum sharing requires spectrum scanning to gain awareness of the spectrum occupancy patterns and decide how to allocate access to this resource. This process has been traditionally done by sensing the channel to determine its state, occupied or empty, and then using frequentist inference to estimate the channel occupancy. However, frequentist inference does not handle uncertainty and does not take into account the probabilities of false alarm and detection when estimating the channel occupancy rate. On the other hand, Bayesian inference can handle uncertainty by considering the impact of these parameters on spectrum sensing results. Additionally, it is possible to include previous knowledge into the construction of Bayesian models to learn and make decision under uncertainty. In this paper, we propose a spectrum scanning method, Bayesian inference, to estimate the channel occupancy rate. One advantage of this method is that it takes into consideration the probabilities of false alarm and detection of the spectrum sensor. This feature makes the estimation of the channel occupancy rate more accurate.

International audience; Jamming attacks target a wireless network creating an unwanted denial of service. 5G is vulnerable to these attacks despite its resilience prompted by the use of millimeter wave bands. Over the last decade, several types of jamming detection techniques have been proposed, including fuzzy logic, game theory, channel surfing, and time series. Most of these techniques are inefficient in detecting smart jammers. Thus, there is a great need for efficient and fast jamming detection techniques with high accuracy. In this paper, we compare the efficiency of several machine learning models in detecting jamming signals. We investigated the types of signal features that identify jamming signals, and generated a large dataset using these parameters. Using this dataset, the machine learning algorithms were trained, evaluated, and tested. These algorithms are random forest, support vector machine, and neural network. The performance of these algorithms was evaluated and compared using the probability of detection, probability of false alarm, probability of miss detection, and accuracy. The simulation results show that jamming detection based random forest algorithm can detect jammers with a high accuracy, high detection probability and low probability of false alarm.

Spectrum sensing plays an important role in enabling cognitive radio technology for the up-and-coming generation of wireless communication systems. Over the last decade, several sensing methods have been proposed, including energy detection, cyclostationary feature, and matched filter. However, these techniques present several limitations. Energy detection performs poorly under low signal-to-noise ratio, cyclostationary features are complex, and matched filter requires some prior knowledge about the primary user signal. In addition, all of these techniques require setting a threshold which needs the prior knowledge of the noise distribution. Thus, the reliability of spectrum sensing is still an open issue in wireless communication research. In this paper, we propose a spectrum sensing method based on a machine learning theory for cognitive radio networks. The spectrum sensing problem is rigorously modeled and out of which a large-scale comprehensive dataset is built. This dataset is then used to train, validate, and test several machine learning techniques, including random forest, support vector machine with different kernels, decision tree, Naive Bayes, K-nearest neighbors, and logistic regression. The models were extensively tested and evaluated using metrics such as the probabilities of detection, false alarm, and miss-detection as well as the accuracy of the classification. The simulation results show that the random forest model outperforms all the other machine learning methods.

Aviation communities are nowadays deploying automatic dependent surveillance-broadcast (ADS-B) systems on aircraft to have more accurate and reliable air traffic control. This system broadcasts some of the aircraft telemetry data in the form of plaintext messages over unencrypted datalinks, which makes this system vulnerable to several cyber-attacks including jamming. To mitigate these attacks, the first step to take is to detect them. A few techniques have been proposed in the literature, but these techniques suffer from several limitations. This paper presents a comprehensive study highlighting the efficiency of machine learning based classifiers towards jamming attacks detection. Several supervised machine learning algorithms including support vector machine, k-nearest neighbor, artificial neural network, and decision tree are used and their performance is compared. Features such as bit error rate, bad packet ratio, and energy statistic are utilized to train these models and to distinguish the received jamming signals from legitimate ones. The results show that the two-hidden-layer neural network with 15 neurons outperforms all the other algorithms.

With the rapid proliferation of mobile users, the spectrum scarcity has become one of the issues that have to be addressed. Cognitive Radio technology addresses this problem by allowing an opportunistic use of the spectrum bands. In cognitive radio networks, unlicensed users can use licensed channels without causing harmful interference to licensed users. However, cognitive radio networks can be subject to different security threats which can cause severe performance degradation. One of the main attacks on these networks is the primary user emulation in which a malicious node emulates the characteristics of the primary user signals. In this paper, we propose a detection technique of this attack based on the RSS-based localization with the maximum likelihood estimation. The simulation results show that the proposed technique outperforms the RSS-based localization method in detecting the primary user emulation attacker.

Smart grid is an emerging and promising technology. It uses the power of information technologies to deliver intelligently the electrical power to customers, and it allows the integration of the green technology to meet the environmental requirements. Unfortunately, information technologies have its inherent vulnerabilities and weaknesses that expose the smart grid to a wide variety of security risks. The Intrusion detection system (IDS) plays an important role in securing smart grid networks and detecting malicious activity, yet it suffers from several limitations. Many research papers have been published to address these issues using several algorithms and techniques. Therefore, a detailed comparison between these algorithms is needed. This paper presents an overview of four data mining algorithms used by IDS in Smart Grid. An evaluation of performance of these algorithms is conducted based on several metrics including the probability of detection, probability of false alarm, probability of miss detection, efficiency, and processing time. Results show that Random Forest outperforms the other three algorithms in detecting attacks with higher probability of detection, lower probability of false alarm, lower probability of miss detection, and higher accuracy., Comment: 6 pages, 6 Figures

In this paper, a cooperative spectrum sensing scheme based on compressive sensing is proposed. In this scheme, secondary users (SUs) are organized in clusters. In each cluster, SUs forward their compressed signals to the cluster head. Then, the cluster heads report the local sensing data to the fusion center to make the final decision. Analog to information converter is adopted to acquire the received SUs signals. The proposed scheme is evaluated using several metrics, including probability of detection, compression ratio, probability of error, and processing time. The simulation results are compared with those of other cooperative sensing schemes. With our proposed scheme, cooperative spectrum sensing achieves a high probability of detection of 97%, a processing time of 2ms, a low compression ratio of 10%, and low error rate of 35 %. These results show that our proposed method outperforms the spectrum sensing without compressive sensing., Comment: pp. 124-136

Cyber-security threats and issues have been exponentially increasing over the last two decades, including in cognitive radio networks. These attacks and vulnerabilities negatively impact the normal functioning of these networks. Investigating these vulnerabilities and their countermeasures is a necessary step toward securing the cognitive radio based networks. Although there have numerous survey papers on cognitive radio security, a few papers related to the security issues over the physical layer which has a primordial role in establishing radio communications. Thus, an up to date overview of the ongoing research about the threats, detection, and countermeasures techniques over the physical layer is necessary. In this paper, different attacks targeting the physical layer are described and analyzed. Detection methods and countermeasures corresponding to each attack are also discussed and compared. Challenges and future directions are presented as well.

One of the disadvantages of the Automatic Dependent Surveillance-Broadcast (ADS-B) communications protocol is its vulnerability to a number of security attacks since it broadcasts information of the flying aircraft carrying the device over unencrypted datalink. As the U.S. Federal Aviation Administration has mandated the use of ADS-B by 2020, solid, reliable security solutions are needed to secure ADS-B data exchange. The first step in this regard is investigating ADS-B attacks and their impacts on air traffic. Therefore, in this paper, we describe a preliminary work performed to study some effects of an ADS-B attack category, message injection, using Hardware in the Loop testing platform. Using a set of flight plans developed to generate aircraft encounters, results show that this attack can distract pilots and ground controllers, cause air traffic disturbance, and increase the risk of aircraft collisions.

Smart grid uses the power of information technology to intelligently deliver energy to customers by using a two-way communication, and wisely meet the environmental requirements by facilitating the integration of green technologies. Although smart grid addresses several problems of the traditional grid, it faces a number of security challenges. Because communication has been incorporated into the electrical power with its inherent weaknesses, it has exposed the system to numerous risks. Several research papers have discussed these problems. However, most of them classified attacks based on confidentiality, integrity, and availability, and they excluded attacks which compromise other security criteria such as accountability. In addition, the existed security countermeasures focus on countering some specific attacks or protecting some specific components, but there is no global approach which combines these solutions to secure the entire system. The purpose of this paper is to provide a comprehensive overview of the relevant published works. First, we review the security requirements. Then, we investigate in depth a number of important cyber-attacks in smart grid to diagnose the potential vulnerabilities along with their impact. In addition, we proposed a cyber security strategy as a solution to address breaches, counter attacks, and deploy appropriate countermeasures. Finally, we provide some future research directions.

The advancements in digital communication technology have made communication between humans more accessible and instant. However, personal and sensitive information may be available online through social networks and online services that lack the security measures to protect this information. Communication systems are vulnerable and can easily be penetrated by malicious users through social engineering attacks. These attacks aim at tricking individuals or enterprises into accomplishing actions that benefit attackers or providing them with sensitive data such as social security number, health records, and passwords. Social engineering is one of the biggest challenges facing network security because it exploits the natural human tendency to trust. This paper provides an in-depth survey about the social engineering attacks, their classifications, detection strategies, and prevention procedures.

Since the introduction of iPhone in 2007, smartphones have become very popular (e.g., the number of worldwide smartphone sales has surpassed the number of PC sales in 2011). The feature of high mobility and small size of smartphones has created many applications that are not possible or inconvenient for PCs and servers, even laptops. Location-based services (LBS), one of mobile applications, have attracted a great attention recently. This research proposes a location-based service, which predicts a spatial trajectory based on the current and previous trajectories by using a novel matrix representation. Spatial trajectory prediction can be used in a variety of purposes such as travel recommendations and traffic control and planning, but at the same time, just like most location-based services, the user privacy concern is a major issue. Without rigorous privacy protection, users would be reluctant to use the service. The proposed method is simple but effective and user privacy is rigorously preserved at the same time because the trajectory prediction is performed at the user-side. Additionally, this research is not only useful but also pedagogical because it involves a variety of topics like (i) mobile computing, (ii) mobile security, and (iii) human behavior recognition.

Cognitive radio is a new technology that aims to solve the scarcity and underutilization of the radio spectrum. It also aims to achieve its goals with a high quality of service. Hence, channel quality estimation metrics are used to help the cognitive radio to readjust its parameters and enhance its quality of service. One of these metrics is the probability of outage. This metric depends on either the level of signal to interference plus noise ratio (SINR), or the channel capacity and data rate. However, uncertainty affects these two variables, which in turns affects the probability of outage. Therefore, a method that deals with uncertainty is necessary. In this paper, we propose a model based on a Bayesian network. This method qualitatively and quantitatively relates the variables affecting SINR and outage probability by a conditional probabilistic model. The results of the proposed Bayesian model show the effectiveness in handling uncertainty.

This paper discusses a model that predicts trap counts of Culex tarsalis, a female mosquito that is responsible for West Nile Virus (WNV) using machine-learning algorithms. Culex mosquitoes are the main transmission vectors for WNV infections. In this research, a Partial Least Square Regression (PLSR) has been deployed to predict mosquito trap counts of Culex tarsalis using historical meteorological and trap count data from 2005–2015. The associations between 10 years of mosquito capture data and the time lagged environmental quantities trap counts, rainfall, temperature, precipitation, and relative humidity were used to generate a predictive model for the population dynamics of this vector species. Statistical measure of Mean Absolute Error (MAE) is compared with other existing actual collected trap counts to analyze accuracy the predictive models. The paper also details the development of a user-friendly web-interface containing interactive web pages that allow users to visualize the North Dakota mosquito population, weather pattern, and WNV incidence data. The interface utilizes multi-layered Google Maps developed through Google Fusion Tables. An understanding of historical data and weather variables is essential for providing sufficient lead time to predict WNV occurrence, and for implementing disease control and prevention strategies such as spray period and hiring of seasonal mosquito workers. Further, an approach similar to the proposed approach of this paper, which involves the integration of data mining and data visualization techniques, brings novelty to vector control initiatives.

The proliferation of mobile devices has led to an increasing demand for radio spectrum resources. Currently, the spectrum allocation is static, which has resulted in underutilization of this resource. This situation has motivated the search for solutions to address the spectrum scarcity problem. The channel occupancy rate is a piece of information that can assist the decision making process regarding reallocation of spectral resources. This information can be useful for DSA (Dynamic Spectrum Access) systems, spectrum decision, and spectrum regulators. In most of the spectrum usage surveys, frequentist inference techniques are used to scan the spectrum and estimate the occupancy rate of particular frequency bands. This approach has several limitations that are addressed in this work through Bayesian inference in order to build a spectrum decision framework able to deal with uncertainty. This paper presents a work in progress, whose goal is to build a probabilistic model to assist the spectrum decision process in cognitive radios.

Relational databases have dominated the database markets for decades because they perform extremely well for traditional applications like electronic commerce and inventory systems. However, the relational databases do not suit some of the contemporary applications such as big data and cloud computing well because of various reasons like their low scalability and unable to handle a high volume of data. NoSQL (not only SQL) databases are part of the solution for developing those newer applications. The approach they use is different from the one used by relational databases. This chapter discusses NoSQL databases by using an empirical instead of theoretical approach. Other than introducing the types and features of generic NoSQL databases, practical NoSQL database programming and usage are shown by using MongoDB, a NoSQL database. A summary of this research is given at the end of this chapter.

One of the location-based services, spatial trajectory prediction, can be used in a variety of purposes such as travel recommendations and traffic control and planning, but at the same time, just like most location-based services, the concern of user privacy is a major issue. Without rigorous privacy protection, users would be reluctant to use the services. This research proposes a privacy-preserving method of predicting a spatial trajectory based on the current and previous trajectories by using a matrix representation. Preliminary experimental results show this method is effective and secure for spatial trajectory prediction.

This paper proposes a fuzzy logic technique to detect the loss of the UAV’s communication link. The method consists in testing the channel when a communication failure is detected and sending diagnostic packages to the far end node of the link. To detect the loss of communication and determine the cause, four metrics are used: Bad Packet Ratio (BPR), Clear Channel Assessment (CCA), RSS Received Signal Strength (RSS), and Packet Delivery Ratio (PDR). A Fuzzy Inference System (FSI) uses these parameters to compute a Jamming Index (JI) and to determine if the link is functioning normally, or if it is lost due to jamming or other causes. Evaluation results show that the proposed technique is efficient in detecting the loss of communication and determining its cause.

Map navigation is one of the most popular applications used by mobile users. At the same time, it is also one of the time- and resource-consuming applications. Various methods such as most-recently used and first-in, first-out algorithms are used to reduce the map transmission time and delay. One of the popular methods is online mobile map prefetching and caching. However, the mobility and location features of mobile users are usually left out by these methods. Caching and prefetching maps based on a mobile user’s location would greatly reduce the transmission time and hence the battery power consumption. For example, if a user is visiting a town, prefetching the maps of nearby interesting stores and caching the maps of the visited, neighboring landmarks would help the user’s visitation experience and save the transmission time. Online mobile map prefetching or caching is useful, but is not widely employed because it involves several different subjects and developers usually are not familiar with all of them. This chapter intends to relieve the problem by introducing essential technologies for online mobile map prefetching and caching so more developers can start working on this kind of project or research. It consists of four themes: (1) green handheld computing, (2) location-based services and programming, (3) map tile system, and (4) location-aware map prefetching and caching methods. A summary is given at the end of this chapter.

Since the introduction of iPhone in 2007, smartphones have become very popular. The high mobility of smartphones has created many applications that are not possible or inconvenient for PCs and servers, even notebooks. One of the mobile applications, location-based services (LBS), has attracted great attention recently. This paper proposes location-based research, which uses location information to find route anomalies, a common problem of daily life. For example, an alert should be generated when a school bus misses part of a route. Different kinds of route anomalies are discussed and various methods for detecting the anomalies are proposed in this paper. The major methods use a technique of incremental location search, which finds matched routes as the search route is entered location by location. An alert is generated when no matched routes exist. Preliminary experiment results show the proposed methods are effective and easy-to-use.

This paper describes a combined Pulse Width Modulation (PWM)-Pulse Frequency Modulation (PFM) technique implemented in a Field Programmable Gate Array (FPGA) to control DC-DC converters. This technique changes the mode of operation from PWM to PFM with the change in the load condition. The efficiency of the proposed technique is determined by implementing a DC-DC converter and calculating the conduction losses, switching losses, and ripple voltages at different load values. Furthermore, the efficiency of the converter based on the proposed technique is compared to the efficiency of processor-based implementation. The results show that the efficiency of the converter with the FPGA-based technique is higher than the efficiency of the converter with processor based technique.

Since the launch of iPhones in 2007, smartphones become very popular these days. Because of their small sizes and high mobility, smartphones are easily lost or stolen. When people lost their smartphones, they are worried the private data stored in the phones may be revealed to strangers. This research proposes a novel approach for mobile data protection. Mobile usage data is first collected and usage patterns are then discovered and saved. An optimization Hopfield neural network is proposed to match the usage data with the stored usage patterns. When an unusual usage pattern such as an unlawful user trying to access the mobile data is detected, the device will automatically lock itself down until a further action is taken. Experimental results show this method is effective and convenient for mobile data protection.

Handheld devices are easily lost because of their small sizes and high mobility. Personal data like addresses and telephone numbers stored in the devices are revealed when the devices are lost or used by unauthorized persons. This research proposes a novel approach for handheld data protection by using handheld usage context matching. The proposed system consists of five components: (i) usage data gathering, (ii) usage data preparation, (iii) usage pattern discovery, (iv) usage pattern analysis and visualization, and (v) usage pattern applications. Handheld usage context data is collected before applying this method. The usage context is then used to check device usage. When an unusual usage context such as an unlawful user trying to access the handheld data is detected, the device will automatically lock itself down until an action, like entering a password, is taken. Experimental results show this method is effective and convenient for mobile data protection.

Microarray is considered an important instrument and powerful new technology for large-scale gene sequence and gene expression analysis. One of the major challenges of this technique is the image processing phase. The accuracy of this phase has an important impact on the accuracy and effectiveness of the subsequent gene expression and identification analysis. The processing can be organized mainly into four steps: gridding, spot isolation, segmentation, and quantification. Although several commercial software packages are now available, microarray image analysis still requires some intervention by the user, and thus a certain level of image processing expertise. This paper describes and compares four techniques that perform automatic gridding and spot isolation. The proposed techniques are based on template matching technique, standard deviation, sum, and derivative of these profiles. Experimental results show that the accuracy of the derivative of the sum profile is highly accurate compared to other techniques for good and poor quality microarray images., 5 pages, IEEE Electro/Information Technology, 2008