Crystal Ball: From Innovative Attacks to Attack Effectiveness Classifier

Android OS is one of the most popular operating systems worldwide, making it a desirable target for malware attacks. Some of the latest and most important defensive systems are based on machine learning (ML) and cybercriminals continuously search for ways to overcome the barriers posed by these systems. Thus, the focus of this work is on evasion attacks in the attempt to show the weaknesses of state of the art research and how more resilient systems can be built. Evasion attacks consist of manipulating either the actual malicious application (problem-based) or its extracted feature vector (feature-based), to avoid being detected by ML systems. This study presents a set of innovative problem-based evasion attacks against well-known Android malware detection systems, which decrease their detection rate by up to 97%. Moreover, an analysis of the effectiveness of these attacks against VirusTotal (VT) scanners was conducted, empirically showing their efficiency against well-known scanners (e.g., McAfee and Comodo) as well. The VT system proved to be a great candidate for the attacks, as in 98% of the apps, less scanners detected the manipulated apps than the original malicious apps. As not all the attacks are effective in the same manner against the VT scanners, the attack efficiency classifiers are advised. Each classifier predicts the applicability of one of the attacks. The set of classifiers creates an ensemble, which shows high success rates, allowing the attacker to decide which attack is best to use for each malicious app and defense system

Breaking the structure of MaMaDroid

Android malware is a continuously expanding threat to billions of mobile users around the globe. Detection systems are updated constantly to address these threats. However, a backlash takes the form of evasion attacks, in which an adversary changes malicious samples in the wild such that they will be misclassified as benign. This paper comprehensively inspects a well-known Android malware detection system, MaMaDroid, which analyzes the control flow graph of the application. Changes in the portion of benign samples in the training set are considered to reveal their effect on the resulting classifier. These changes in the ratio between benign and malicious samples have a clear effect on each of the models, resulting in a decrease of more than 40% in their detection rate, model confidence, and reliability. Moreover, adopted Machine Learning models were implemented as well, including 5-NN, Decision Tree, and Adaboost. Exploration of the six models showed a typical behavior in different cases, of tree-based models and distance-based models. Moreover, three novel attacks that manipulate the Control Flow Graph (CFG) are described for each of the targeted models. The attacks decrease the detection rate of most models to less than 10%, with regards to different ratios of benign to malicious apps. As a result, a new version of MaMaDroid is engineered, which fuses the CFG of the app and static analysis of features of the app. This improved model is proven to be robust against evasion attacks targeting CFG-based models and static analysis models, achieving a detection rate of ∼80%

Tailoring Microstructure and Mechanical Properties of Additively-Manufactured Ti6Al4V Using Post Processing

Additively-manufactured Ti-6Al-4V (Ti64) exhibits high strength but in some cases inferior elongation to those of conventionally manufactured materials. Post-processing of additively manufactured Ti64 components is investigated to modify the mechanical properties for specific applications while still utilizing the benefits of the additive manufacturing process. The mechanical properties and fatigue resistance of Ti64 samples made by electron beam melting were tested in the as-built state. Several heat treatments (up to 1000 °C) were performed to study their effect on the microstructure and mechanical properties. Phase content during heating was tested with high reliability by neutron diffraction at Los Alamos National Laboratory. Two different hot isostatic pressings (HIP) cycles were tested, one at low temperature (780 °C), the other is at the standard temperature (920 °C). The results show that lowering the HIP holding temperature retains the fine microstructure (~1% β phase) and the 0.2% proof stress of the as-built samples (1038 MPa), but gives rise to higher elongation (~14%) and better fatigue life. The material subjected to a higher HIP temperature had a coarser microstructure, more residual β phase (~2% difference), displayed slightly lower Vickers hardness (~15 HV10N), 0.2% proof stress (~60 MPa) and ultimate stresses (~40 MPa) than the material HIP’ed at 780 °C, but had superior elongation (~6%) and fatigue resistance. Heat treatment at 1000 °C entirely altered the microstructure (~7% β phase), yield elongation of 13.7% but decrease the 0.2% proof-stress to 927 MPa. The results of the HIP at 780 °C imply it would be beneficial to lower the standard ASTM HIP temperature for Ti6Al4V additively manufactured by electron beam melting

Tailoring Microstructure and Mechanical Properties of Additively-Manufactured Ti6Al4V Using Post Processing

Additively-manufactured Ti-6Al-4V (Ti64) exhibits high strength but in some cases inferior elongation to those of conventionally manufactured materials. Post-processing of additively manufactured Ti64 components is investigated to modify the mechanical properties for specific applications while still utilizing the benefits of the additive manufacturing process. The mechanical properties and fatigue resistance of Ti64 samples made by electron beam melting were tested in the as-built state. Several heat treatments (up to 1000 °C) were performed to study their effect on the microstructure and mechanical properties. Phase content during heating was tested with high reliability by neutron diffraction at Los Alamos National Laboratory. Two different hot isostatic pressings (HIP) cycles were tested, one at low temperature (780 °C), the other is at the standard temperature (920 °C). The results show that lowering the HIP holding temperature retains the fine microstructure (~1% β phase) and the 0.2% proof stress of the as-built samples (1038 MPa), but gives rise to higher elongation (~14%) and better fatigue life. The material subjected to a higher HIP temperature had a coarser microstructure, more residual β phase (~2% difference), displayed slightly lower Vickers hardness (~15 HV10N), 0.2% proof stress (~60 MPa) and ultimate stresses (~40 MPa) than the material HIP’ed at 780 °C, but had superior elongation (~6%) and fatigue resistance. Heat treatment at 1000 °C entirely altered the microstructure (~7% β phase), yield elongation of 13.7% but decrease the 0.2% proof-stress to 927 MPa. The results of the HIP at 780 °C imply it would be beneficial to lower the standard ASTM HIP temperature for Ti6Al4V additively manufactured by electron beam melting

Sip1 regulates the generation of the inner nuclear layer retinal cell lineages in mammals

textabstractThe transcription factor Sip1 (Zeb2) plays multiple roles during CNS development from early acquisition of neural fate to cortical neurogenesis and gliogenesis. In humans, SIP1 (ZEB2) haploinsufficiency leads to Mowat–Wilson syndrome, a complex congenital anomaly including intellectual disability, epilepsy and Hirschsprung disease. Here we uncover the role of Sip1 in retinogenesis. Somatic deletion of Sip1 from mouse retinal progenitors primarily affects the generation of inner nuclear layer cell types, resulting in complete loss of horizontal cells and reduced numbers of amacrine and bipolar cells, while the number of Muller glia is increased. Molecular analysis places Sip1 downstream of the eye field transcription factor Pax6 and upstream of Ptf1a in the gene network required for generating the horizontal and amacrine lineages. Intriguingly, characterization of differentiation dynamics reveals that Sip1 has a role in promoting the timely differentiation of retinal interneurons, assuring generation of the proper number of the diverse neuronal and glial cell subtypes that constitute the functional retina in mammals

Sip1 regulates the generation of the inner nuclear layer retinal cell lineages in mammals

textabstractThe transcription factor Sip1 (Zeb2) plays multiple roles during CNS development from early acquisition of neural fate to cortical neurogenesis and gliogenesis. In humans, SIP1 (ZEB2) haploinsufficiency leads to Mowat–Wilson syndrome, a complex congenital anomaly including intellectual disability, epilepsy and Hirschsprung disease. Here we uncover the role of Sip1 in retinogenesis. Somatic deletion of Sip1 from mouse retinal progenitors primarily affects the generation of inner nuclear layer cell types, resulting in complete loss of horizontal cells and reduced numbers of amacrine and bipolar cells, while the number of Muller glia is increased. Molecular analysis places Sip1 downstream of the eye field transcription factor Pax6 and upstream of Ptf1a in the gene network required for generating the horizontal and amacrine lineages. Intriguingly, characterization of differentiation dynamics reveals that Sip1 has a role in promoting the timely differentiation of retinal interneurons, assuring generation of the proper number of the diverse neuronal and glial cell subtypes that constitute the functiona