Visible to the public Biblio

Filters: Keyword is Security Heuristics  [Clear All Filters]
2021-04-27
Fu, Y., Tong, S., Guo, X., Cheng, L., Zhang, Y., Feng, D..  2020.  Improving the Effectiveness of Grey-box Fuzzing By Extracting Program Information. 2020 IEEE 19th International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom). :434–441.
Fuzzing has been widely adopted as an effective techniques to detect vulnerabilities in softwares. However, existing fuzzers suffer from the problems of generating excessive test inputs that either cannot pass input validation or are ineffective in exploring unvisited regions in the program under test (PUT). To tackle these problems, we propose a greybox fuzzer called MuFuzzer based on AFL, which incorporates two heuristics that optimize seed selection and automatically extract input formatting information from the PUT to increase the chance of generating valid test inputs, respectively. In particular, the first heuristic collects the branch coverage and execution information during a fuzz session, and utilizes such information to guide fuzzing tools in selecting seeds that are fast to execute, small in size, and more importantly, more likely to explore new behaviors of the PUT for subsequent fuzzing activities. The second heuristic automatically identifies string comparison operations that the PUT uses for input validation, and establishes a dictionary with string constants from these operations to help fuzzers generate test inputs that have higher chances to pass input validation. We have evaluated the performance of MuFuzzer, in terms of code coverage and bug detection, using a set of realistic programs and the LAVA-M test bench. Experiment results demonstrate that MuFuzzer is able to achieve higher code coverage and better or comparative bug detection performance than state-of-the-art fuzzers.
Junosza-Szaniawski, K., Nogalski, D., Wójcik, A..  2020.  Exact and approximation algorithms for sensor placement against DDoS attacks. 2020 15th Conference on Computer Science and Information Systems (FedCSIS). :295–301.
In DDoS attack (Distributed Denial of Service), an attacker gains control of many network users by a virus. Then the controlled users send many requests to a victim, leading to lack of its resources. DDoS attacks are hard to defend because of distributed nature, large scale and various attack techniques. One of possible ways of defense is to place sensors in the network that can detect and stop an unwanted request. However, such sensors are expensive so there is a natural question about a minimum number of sensors and their optimal placement to get the required level of safety. We present two mixed integer models for optimal sensor placement against DDoS attacks. Both models lead to a trade-off between the number of deployed sensors and the volume of uncontrolled flow. Since above placement problems are NP-hard, two efficient heuristics are designed, implemented and compared experimentally with exact linear programming solvers.
Harada, T., Tanaka, K., Ogasawara, R., Mikawa, K..  2020.  A Rule Reordering Method via Pairing Dependent Rules. 2020 IEEE Conference on Communications and Network Security (CNS). :1–9.
Packet classification is used to determine the behavior of incoming packets to network devices. Because it is achieved using a linear search on a classification rule list, a larger number of rules leads to a longer communication latency. To decrease this latency, the problem is generalized as Optimal Rule Ordering (ORO), which aims to identify the order of rules that minimizes the classification latency caused by packet classification while preserving the classification policy. Because ORO is known to be NP-complete by Hamed and Al-Shaer [Dynamic rule-ordering optimization for high-speed firewall filtering, ASIACCS (2006) 332-342], various heuristics for ORO have been proposed. Sub-graph merging (SGM) by Tapdiya and Fulp [Towards optimal firewall rule ordering utilizing directed acyclical graphs, ICCCN (2009) 1-6] is the state of the art heuristic algorithm for ORO. In this paper, we propose a novel heuristic method for ORO. Although most heuristics try to recursively determine the maximum-weight rule and move it as far as possible to an upper position, our algorithm pairs rules that cause policy violations until there are no such rules to simply sort the rules by these weights. Our algorithm markedly decreases the classification latency and reordering time compared with SGM in experiments. The sets consisting of thousands of rules that require one or more hours for reordering by SGM can be reordered by the proposed method within one minute.
Gui, J., Li, D., Chen, Z., Rhee, J., Xiao, X., Zhang, M., Jee, K., Li, Z., Chen, H..  2020.  APTrace: A Responsive System for Agile Enterprise Level Causality Analysis. 2020 IEEE 36th International Conference on Data Engineering (ICDE). :1701–1712.
While backtracking analysis has been successful in assisting the investigation of complex security attacks, it faces a critical dependency explosion problem. To address this problem, security analysts currently need to tune backtracking analysis manually with different case-specific heuristics. However, existing systems fail to fulfill two important system requirements to achieve effective backtracking analysis. First, there need flexible abstractions to express various types of heuristics. Second, the system needs to be responsive in providing updates so that the progress of backtracking analysis can be frequently inspected, which typically involves multiple rounds of manual tuning. In this paper, we propose a novel system, APTrace, to meet both of the above requirements. As we demonstrate in the evaluation, security analysts can effectively express heuristics to reduce more than 99.5% of irrelevant events in the backtracking analysis of real-world attack cases. To improve the responsiveness of backtracking analysis, we present a novel execution-window partitioning algorithm that significantly reduces the waiting time between two consecutive updates (especially, 57 times reduction for the top 1% waiting time).
Vuppalapati, C., Ilapakurti, A., Kedari, S., Vuppalapati, R., Vuppalapati, J., Kedari, S..  2020.  The Role of Combinatorial Mathematical Optimization and Heuristics to improve Small Farmers to Veterinarian access and to create a Sustainable Food Future for the World. 2020 Fourth World Conference on Smart Trends in Systems, Security and Sustainability (WorldS4). :214–221.
The Global Demand for agriculture and dairy products is rising. Demand is expected to double by 2050. This will challenge agriculture markets in a way we have not seen before. For instance, unprecedented demand to increase in dairy farm productivity of already shrinking farms, untethered perpetual access to veterinarians by small dairy farms, economic engines of the developing countries, for animal husbandry and, finally, unprecedented need to increase productivity of veterinarians who're already understaffed, over-stressed, resource constrained to meet the current global dairy demands. The lack of innovative solutions to address the challenge would result in a major obstacle to achieve sustainable food future and a colossal roadblock ending economic disparities. The paper proposes a novel innovative data driven framework cropped by data generated using dairy Sensors and by mathematical formulations using Solvers to generate an exclusive veterinarian daily farms prioritized visit list so as to have a greater coverage of the most needed farms performed in-time and improve small farmers access to veterinarians, a precious and highly shortage & stressed resource.
Saganowski, S..  2020.  A Three-Stage Machine Learning Network Security Solution for Public Entities. 2020 IEEE 19th International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom). :1097–1104.
In the era of universal digitization, ensuring network and data security is extremely important. As a part of the Regional Center for Cybersecurity initiative, a three-stage machine learning network security solution is being developed and will be deployed in March 2021. The solution consists of prevention, monitoring, and curation stages. As prevention, we utilize Natural Language Processing to extract the security-related information from social media, news portals, and darknet. A deep learning architecture is used to monitor the network in real-time and detect any abnormal traffic. A combination of regular expressions, pattern recognition, and heuristics are applied to the abuse reports to automatically identify intrusions that passed other security solutions. The lessons learned from the ongoing development of the system, alongside the results, extensive analysis, and discussion is provided. Additionally, a cybersecurity-related corpus is described and published within this work.
Tahsini, A., Dunstatter, N., Guirguis, M., Ahmed, C. M..  2020.  DeepBLOC: A Framework for Securing CPS through Deep Reinforcement Learning on Stochastic Games. 2020 IEEE Conference on Communications and Network Security (CNS). :1–9.

One important aspect in protecting Cyber Physical System (CPS) is ensuring that the proper control and measurement signals are propagated within the control loop. The CPS research community has been developing a large set of check blocks that can be integrated within the control loop to check signals against various types of attacks (e.g., false data injection attacks). Unfortunately, it is not possible to integrate all these “checks” within the control loop as the overhead introduced when checking signals may violate the delay constraints of the control loop. Moreover, these blocks do not completely operate in isolation of each other as dependencies exist among them in terms of their effectiveness against detecting a subset of attacks. Thus, it becomes a challenging and complex problem to assign the proper checks, especially with the presence of a rational adversary who can observe the check blocks assigned and optimizes her own attack strategies accordingly. This paper tackles the inherent state-action space explosion that arises in securing CPS through developing DeepBLOC (DB)-a framework in which Deep Reinforcement Learning algorithms are utilized to provide optimal/sub-optimal assignments of check blocks to signals. The framework models stochastic games between the adversary and the CPS defender and derives mixed strategies for assigning check blocks to ensure the integrity of the propagated signals while abiding to the real-time constraints dictated by the control loop. Through extensive simulation experiments and a real implementation on a water purification system, we show that DB achieves assignment strategies that outperform other strategies and heuristics.

Ritter, D..  2020.  Cost-efficient Integration Process Placement in Multiclouds. 2020 IEEE 24th International Enterprise Distributed Object Computing Conference (EDOC). :115–124.
Integration as a service (INTaaS) is the centrepiece of current corporate, cloud and device integration processes. Thereby, compositions of integration patterns denote the required integration logic as integration processes, currently running in single-clouds. While multicloud settings gain importance, their promised freedom of selecting the best option for a specific problem is currently not realized as well as security constraints are handled in a cost-intensive manner for the INTaaS vendors, leading to security vs. costs goal conflicts.In this work, we propose a design-time placement for processes in multiclouds that is cost-optimal for the INTaaS vendors, and respects configurable security constraints of their customers. To make the solution tractable for larger, productive INTaaS processes, it is relaxed using local search heuristics. The approach is evaluated on real-world integration processes with respect to cost- and runtime-efficiency, and discusses interesting trade-offs.
Tolsdorf, J., Iacono, L. Lo.  2020.  Vision: Shred If Insecure – Persuasive Message Design as a Lesson and Alternative to Previous Approaches to Usable Secure Email Interfaces. 2020 IEEE European Symposium on Security and Privacy Workshops (EuroS PW). :172–177.
Despite the advances in research on usable secure email, the majority of mail user agents found in practice still violates best practices in UI design and uses ineffective and inhomogeneous design strategies to communicate and let users control the security status of an email message.We propose a novel interaction and design concept that we refer to as persuasive message design. Our approach is derived from heuristics and a systematic meta-study of existing HCI literature on email management, usable secure email and phishing research. Concluding on this body of knowledge we propose the design of interfaces that suppress weak cues and instead manipulate the display of emails according to their technical security level. Persuasive message design addresses several shortcomings of current secure email user interfaces and provides a consistent user experience that can be deployed even by email providers.
2021-01-25
Oesch, S., Bridges, R., Smith, J., Beaver, J., Goodall, J., Huffer, K., Miles, C., Scofield, D..  2020.  An Assessment of the Usability of Machine Learning Based Tools for the Security Operations Center. 2020 International Conferences on Internet of Things (iThings) and IEEE Green Computing and Communications (GreenCom) and IEEE Cyber, Physical and Social Computing (CPSCom) and IEEE Smart Data (SmartData) and IEEE Congress on Cybermatics (Cybermatics). :634—641.

Gartner, a large research and advisory company, anticipates that by 2024 80% of security operation centers (SOCs) will use machine learning (ML) based solutions to enhance their operations.11https://www.ciodive.com/news/how-data-science-tools-can-lighten-the-load-for-cybersecurity-teams/572209/ In light of such widespread adoption, it is vital for the research community to identify and address usability concerns. This work presents the results of the first in situ usability assessment of ML-based tools. With the support of the US Navy, we leveraged the national cyber range-a large, air-gapped cyber testbed equipped with state-of-the-art network and user emulation capabilities-to study six US Naval SOC analysts' usage of two tools. Our analysis identified several serious usability issues, including multiple violations of established usability heuristics for user interface design. We also discovered that analysts lacked a clear mental model of how these tools generate scores, resulting in mistrust \$a\$ and/or misuse of the tools themselves. Surprisingly, we found no correlation between analysts' level of education or years of experience and their performance with either tool, suggesting that other factors such as prior background knowledge or personality play a significant role in ML-based tool usage. Our findings demonstrate that ML-based security tool vendors must put a renewed focus on working with analysts, both experienced and inexperienced, to ensure that their systems are usable and useful in real-world security operations settings.

2020-03-09
Ali Mirza, Qublai K., Hussain, Fatima, Awan, Irfan, Younas, Muhammad, Sharieh, Salah.  2019.  Taxonomy-Based Intelligent Malware Detection Framework. 2019 IEEE Global Communications Conference (GLOBECOM). :1–6.
Timely detection of a malicious piece of code accurately, in an enterprise network or in an individual device, before it propagates and mutate itself, is one of the most challenging tasks in the domain of cyber security. Millions of variants of each latest malware are released every day and each of these variants have a unique static signature. Conventional anti-malware tools use signatures and static heuristics of malware to segregate them from legitimate files, which is not an effective technique because of the number of malware variants released every passing day. To overcome the fundamental flaw of operational techniques, we propose a framework that generalizes the static and dynamic malwarefeaturesthatareusedtotrainmultiplemachinelearning algorithms. The generalization of clean and malicious features enables the framework to accurately differentiate between clean and malicious files.
Cao, Yuan, Zhao, Yongli, Li, Jun, Lin, Rui, Zhang, Jie, Chen, Jiajia.  2019.  Reinforcement Learning Based Multi-Tenant Secret-Key Assignment for Quantum Key Distribution Networks. 2019 Optical Fiber Communications Conference and Exhibition (OFC). :1–3.
We propose a reinforcement learning based online multi-tenant secret-key assignment algorithm for quantum key distribution networks, capable of reducing tenant-request blocking probability more than half compared to the benchmark heuristics.
Nilizadeh, Shirin, Noller, Yannic, Pasareanu, Corina S..  2019.  DifFuzz: Differential Fuzzing for Side-Channel Analysis. 2019 IEEE/ACM 41st International Conference on Software Engineering (ICSE). :176–187.
Side-channel attacks allow an adversary to uncover secret program data by observing the behavior of a program with respect to a resource, such as execution time, consumed memory or response size. Side-channel vulnerabilities are difficult to reason about as they involve analyzing the correlations between resource usage over multiple program paths. We present DifFuzz, a fuzzing-based approach for detecting side-channel vulnerabilities related to time and space. DifFuzz automatically detects these vulnerabilities by analyzing two versions of the program and using resource-guided heuristics to find inputs that maximize the difference in resource consumption between secret-dependent paths. The methodology of DifFuzz is general and can be applied to programs written in any language. For this paper, we present an implementation that targets analysis of Java programs, and uses and extends the Kelinci and AFL fuzzers. We evaluate DifFuzz on a large number of Java programs and demonstrate that it can reveal unknown side-channel vulnerabilities in popular applications. We also show that DifFuzz compares favorably against Blazer and Themis, two state-of-the-art analysis tools for finding side-channels in Java programs.
Hăjmăȿan, Gheorghe, Mondoc, Alexandra, Creț, Octavian.  2019.  Bytecode Heuristic Signatures for Detecting Malware Behavior. 2019 Conference on Next Generation Computing Applications (NextComp). :1–6.
For a long time, the most important approach for detecting malicious applications was the use of static, hash-based signatures. This approach provides a fast response time, has a low performance overhead and is very stable due to its simplicity. However, with the rapid growth in the number of malware, as well as their increased complexity in terms of polymorphism and evasion, the era of reactive security solutions started to fade in favor of new, proactive approaches such as behavior based detection. We propose a novel approach that uses an interpreter virtual machine to run proactive behavior heuristics from bytecode signatures, thus combining the advantages of behavior based detection with those of signatures. Based on our approximation, using this approach we succeeded to reduce by 85% the time required to update a behavior based detection solution to detect new threats, while continuing to benefit from the versatility of behavior heuristics.
Li, Chi, Zhou, Min, Gu, Zuxing, Gu, Ming, Zhang, Hongyu.  2019.  Ares: Inferring Error Specifications through Static Analysis. 2019 34th IEEE/ACM International Conference on Automated Software Engineering (ASE). :1174–1177.
Misuse of APIs happens frequently due to misunderstanding of API semantics and lack of documentation. An important category of API-related defects is the error handling defects, which may result in security and reliability flaws. These defects can be detected with the help of static program analysis, provided that error specifications are known. The error specification of an API function indicates how the function can fail. Writing error specifications manually is time-consuming and tedious. Therefore, automatic inferring the error specification from API usage code is preferred. In this paper, we present Ares, a tool for automatic inferring error specifications for C code through static analysis. We employ multiple heuristics to identify error handling blocks and infer error specifications by analyzing the corresponding condition logic. Ares is evaluated on 19 real world projects, and the results reveal that Ares outperforms the state-of-the-art tool APEx by 37% in precision. Ares can also identify more error specifications than APEx. Moreover, the specifications inferred from Ares help find dozens of API-related bugs in well-known projects such as OpenSSL, among them 10 bugs are confirmed by developers. Video: https://youtu.be/nf1QnFAmu8Q. Repository: https://github.com/lc3412/Ares.
Perner, Cora, Kinkelin, Holger, Carle, Georg.  2019.  Adaptive Network Management for Safety-Critical Systems. 2019 IFIP/IEEE Symposium on Integrated Network and Service Management (IM). :25–30.
Present networks within safety-critical systems rely on complex and inflexible network configurations. New technologies such as software-defined networking are more dynamic and offer more flexibility, but due care needs to be exercised to ensure that safety and security are not compromised by incorrect configurations. To this end, this paper proposes the use of pre-generated and optimized configuration templates. These provide alternate routes for traffic considering availability, resilience and timing constraints where network components fail due to attacks or faults.To obtain these templates, two heuristics based on Dijkstra's algorithm and an optimization algorithm providing the maximum resilience were investigated. While the configurations obtained through optimization yield appropriate templates, the heuristics investigated are not suitable to obtain configuration templates, since they cannot fulfill all requirements.
Nathezhtha, T., Sangeetha, D., Vaidehi, V..  2019.  WC-PAD: Web Crawling based Phishing Attack Detection. 2019 International Carnahan Conference on Security Technology (ICCST). :1–6.
Phishing is a criminal offense which involves theft of user's sensitive data. The phishing websites target individuals, organizations, the cloud storage hosting sites and government websites. Currently, hardware based approaches for anti-phishing is widely used but due to the cost and operational factors software based approaches are preferred. The existing phishing detection approaches fails to provide solution to problem like zero-day phishing website attacks. To overcome these issues and precisely detect phishing occurrence a three phase attack detection named as Web Crawler based Phishing Attack Detector(WC-PAD) has been proposed. It takes the web traffics, web content and Uniform Resource Locator(URL) as input features, based on these features classification of phishing and non phishing websites are done. The experimental analysis of the proposed WC-PAD is done with datasets collected from real phishing cases. From the experimental results, it is found that the proposed WC-PAD gives 98.9% accuracy in both phishing and zero-day phishing attack detection.
Hermawan, Indra, Ma’sum, M. Anwar, Riskyana Dewi Intan, P, Jatmiko, Wisnu, Wiweko, Budi, Boediman, Alfred, Pradekso, Beno K..  2019.  Temporal feature and heuristics-based Noise Detection over Classical Machine Learning for ECG Signal Quality Assessment. 2019 International Workshop on Big Data and Information Security (IWBIS). :1–8.
This study proposes a method for ECG signals quality assessment (SQA) by using temporal feature, and heuristic rule. The ECG signal will be classified as acceptable or unacceptable. Seven types of noise were able to be detected by the prosed method. The noises are: FL, TVN, BW, AB, MA, PLI and AWGN. The proposed method is aimed to have better performance for SQA than classical machine learning method. The experiment is conducted by using 1000 instances ECG signal. The experiment result shows that db8 has the best performance with 0.86, 0.85 and 85.6% on lead-1 signal and 0.69, 0.79, and 74% on lead-5 signal for specificity, sensitivity and accuracy respectively. Compared to the classical machine learning, the proposed heuristic method has same accuracy but has 48% and 31% better specificity for lead-1 and lead-5. It means that the proposed method has far better ability to detect noise.
Calzavara, Stefano, Conti, Mauro, Focardi, Riccardo, Rabitti, Alvise, Tolomei, Gabriele.  2019.  Mitch: A Machine Learning Approach to the Black-Box Detection of CSRF Vulnerabilities. 2019 IEEE European Symposium on Security and Privacy (EuroS P). :528–543.

Cross-Site Request Forgery (CSRF) is one of the oldest and simplest attacks on the Web, yet it is still effective on many websites and it can lead to severe consequences, such as economic losses and account takeovers. Unfortunately, tools and techniques proposed so far to identify CSRF vulnerabilities either need manual reviewing by human experts or assume the availability of the source code of the web application. In this paper we present Mitch, the first machine learning solution for the black-box detection of CSRF vulnerabilities. At the core of Mitch there is an automated detector of sensitive HTTP requests, i.e., requests which require protection against CSRF for security reasons. We trained the detector using supervised learning techniques on a dataset of 5,828 HTTP requests collected on popular websites, which we make available to other security researchers. Our solution outperforms existing detection heuristics proposed in the literature, allowing us to identify 35 new CSRF vulnerabilities on 20 major websites and 3 previously undetected CSRF vulnerabilities on production software already analyzed using a state-of-the-art tool.

Gregory, Jason M., Al-Hussaini, Sarah, Gupta, Satyandra K..  2019.  Heuristics-Based Multi-Agent Task Allocation for Resilient Operations. 2019 IEEE International Symposium on Safety, Security, and Rescue Robotics (SSRR). :1–8.
Multi-Agent Task Allocation is a pre-requisite for many autonomous, real-world systems because of the need for intelligent task assignment amongst a team for maximum efficiency. Similarly, agent failure, task, failure, and a lack of state information are inherent challenges when operating in complex environments. Many existing solutions make simplifying assumptions regarding the modeling of these factors, e.g., Markovian state information. However, it is not clear that this is always the appropriate approach or that results from these approaches are necessarily representative of performance in the natural world. In this work, we demonstrate that there exists a class of problems for which non-Markovian state modeling is beneficial. Furthermore, we present and characterize a novel heuristic for task allocation that incorporates realistic state and uncertainty modeling in order to improve performance. Our quantitative analysis, when tested in a simulated search and rescue (SAR) mission, shows a decrease in performance of more than 57% when a representative method with Markovian assumptions is tested in a non-Markovian setting. Our novel heuristic has shown an improvement in performance of 3-15%, in the same non-Markovian setting, by modeling probabilistic failure and making fewer assumptions.
2019-07-01
Nwebonyi, Francis N., Martins, Rolando, Correia, Manuel E..  2018.  Reputation-Based Security System For Edge Computing. Proceedings of the 13th International Conference on Availability, Reliability and Security. :39:1-39:8.

Given the centralized architecture of cloud computing, there is a genuine concern about its ability to adequately cope with the demands of connecting devices which are sharply increasing in number and capacity. This has led to the emergence of edge computing technologies, including but not limited to mobile edge-clouds. As a branch of Peer-to-Peer (P2P) networks, mobile edge-clouds inherits disturbing security concerns which have not been adequately addressed in previous methods. P2P security systems have featured many trust-based methods owing to their suitability and cost advantage, but these approaches still lack in a number of ways. They mostly focus on protecting client nodes from malicious service providers, but downplay the security of service provider nodes, thereby creating potential loopholes for bandwidth attack. Similarly, trust bootstrapping is often via default scores, or based on heuristics that does not reflect the identity of a newcomer. This work has patched these inherent loopholes and improved fairness among participating peers. The use cases of mobile edge-clouds have been particularly considered and a scalable reputation based security mechanism was derived to suit them. BitTorrent protocol was modified to form a suitable test bed, using Peersim simulator. The proposed method was compared to some related methods in the literature through detailed simulations. Results show that the new method can foster trust and significantly improve network security, in comparison to previous similar systems.

Liu, Changming, Zou, Deqing, Luo, Peng, Zhu, Bin B., Jin, Hai.  2018.  A Heuristic Framework to Detect Concurrency Vulnerabilities. Proceedings of the 34th Annual Computer Security Applications Conference. :529-541.

With a growing demand of concurrent software to exploit multi-core hardware capability, concurrency vulnerabilities have become an inevitable threat to the security of today's IT industry. Existing concurrent program detection schemes focus mainly on detecting concurrency errors such as data races, atomicity violation, etc., with little attention paid to detect concurrency vulnerabilities that may be exploited to infringe security. In this paper, we propose a heuristic framework that combines both static analysis and fuzz testing to detect targeted concurrency vulnerabilities such as concurrency buffer overflow, double free, and use-after-free. The static analysis locates sensitive concurrent operations in a concurrent program, categorizes each finding into a potential type of concurrency vulnerability, and determines the execution order of the sensitive operations in each finding that would trigger the suspected concurrency vulnerability. The results are then plugged into the fuzzer with the execution order fixed by the static analysis in order to trigger the suspected concurrency vulnerabilities. In order to introduce more variance which increases possibility that the concurrency errors can be triggered, we also propose manipulation of thread scheduling priority to enable a fuzzer such as AFL to effectively explore thread interleavings in testing a concurrent program. To the best of our knowledge, this is the first fuzzer that is capable of effectively exploring concurrency errors. In evaluating the proposed heuristic framework with a benchmark suit of six real-world concurrent C programs, the framework detected two concurrency vulnerabilities for the proposed concurrency vulnerability detection, both being confirmed to be true positives, and produced three new crashes for the proposed interleaving exploring fuzzer that existing fuzzers could not produce. These results demonstrate the power and effectiveness of the proposed heuristic framework in detecting concurrency errors and vulnerabilities.

Napoli, Daniela.  2018.  Developing Accessible and Usable Security (ACCUS) Heuristics. Extended Abstracts of the 2018 CHI Conference on Human Factors in Computing Systems. :SRC16:1-SRC16:6.

Currently, usable security and web accessibility design principles exist separately. Although literature at the intersect of accessibility and security is developing, it is limited in its understanding of how users with vision loss operate the web securely. In this paper, we propose heuristics that fuse the nuances of both fields. With these heuristics, we evaluate 10 websites and uncover several issues that can impede users' ability to abide by common security advice.

Amjad, N., Afzal, H., Amjad, M. F., Khan, F. A..  2018.  A Multi-Classifier Framework for Open Source Malware Forensics. 2018 IEEE 27th International Conference on Enabling Technologies: Infrastructure for Collaborative Enterprises (WETICE). :106-111.

Traditional anti-virus technologies have failed to keep pace with proliferation of malware due to slow process of their signatures and heuristics updates. Similarly, there are limitations of time and resources in order to perform manual analysis on each malware. There is a need to learn from this vast quantity of data, containing cyber attack pattern, in an automated manner to proactively adapt to ever-evolving threats. Machine learning offers unique advantages to learn from past cyber attacks to handle future cyber threats. The purpose of this research is to propose a framework for multi-classification of malware into well-known categories by applying different machine learning models over corpus of malware analysis reports. These reports are generated through an open source malware sandbox in an automated manner. We applied extensive pre-modeling techniques for data cleaning, features exploration and features engineering to prepare training and test datasets. Best possible hyper-parameters are selected to build machine learning models. These prepared datasets are then used to train the machine learning classifiers and to compare their prediction accuracy. Finally, these results are validated through a comprehensive 10-fold cross-validation methodology. The best results are achieved through Gaussian Naive Bayes classifier with random accuracy of 96% and 10-Fold Cross Validation accuracy of 91.2%. The said framework can be deployed in an operational environment to learn from malware attacks for proactively adapting matching counter measures.

Ferreyra, N. E. Díaz, Meisy, R., Heiselz, M..  2018.  At Your Own Risk: Shaping Privacy Heuristics for Online Self-Disclosure. 2018 16th Annual Conference on Privacy, Security and Trust (PST). :1-10.

Revealing private and sensitive information on Social Network Sites (SNSs) like Facebook is a common practice which sometimes results in unwanted incidents for the users. One approach for helping users to avoid regrettable scenarios is through awareness mechanisms which inform a priori about the potential privacy risks of a self-disclosure act. Privacy heuristics are instruments which describe recurrent regrettable scenarios and can support the generation of privacy awareness. One important component of a heuristic is the group of people who should not access specific private information under a certain privacy risk. However, specifying an exhaustive list of unwanted recipients for a given regrettable scenario can be a tedious task which necessarily demands the user's intervention. In this paper, we introduce an approach based on decision trees to instantiate the audience component of privacy heuristics with minor intervention from the users. We introduce Disclosure- Acceptance Trees, a data structure representative of the audience component of a heuristic and describe a method for their generation out of user-centred privacy preferences.